Nodes In Subnetwork Research Articles

Cortical glutamatergic projection neuron types contribute to distinct functional subnetworks.

The cellular basis of cerebral cortex functional architecture remains not well understood. A major challenge is to monitor and decipher neural network dynamics across broad cortical areas yet with projection-neuron-type resolution in real time during behavior. Combining genetic targeting and wide-field imaging, we monitored activity dynamics of subcortical-projecting (PTFezf2) and intratelencephalic-projecting (ITPlxnD1) types across dorsal cortex of mice during different brain states and behaviors. ITPlxnD1 and PTFezf2 neurons showed distinct activation patterns during wakeful resting, during spontaneous movements and upon sensory stimulation. Distinct ITPlxnD1 and PTFezf2 subnetworks were dynamically tuned to different sensorimotor components of a naturalistic feeding behavior, and optogenetic inhibition of ITsPlxnD1 and PTsFezf2 in subnetwork nodes disrupted distinct components of this behavior. Lastly, ITPlxnD1 and PTFezf2 projection patterns are consistent with their subnetwork activation patterns. Our results show that, in addition to the concept of columnar organization, dynamic areal and projection-neuron-type specific subnetworks are a key feature of cortical functional architecture linking microcircuit components with global brain networks.

Identifying critical metro stations in multiplex network based on D–S evidence theory

Public transport networks (PTNs) undertake large amount of passenger demand in the urban transport system. Particularly, stations in metro networks with high significance in structure and function are more likely to contribute to passenger transport. Once these stations are under functional failure, it is easy to lead to the collapse of network connectivity. Thus, identifying critical nodes in PTNs is of practical significance for the public transport planning and operation. This study proposes an identification method for the critical nodes in multiplex network by considering the interaction between metro and bus networks. First, metro and bus networks are constructed by L-space and P-space methods, respectively. Then, ridership is extracted to describe the connection between nodes as the weights of links, and the metro-bus multiplex network is then constructed. Furthermore, the identification method in Multiplex Network based on Dempster–Shafer evidence theory (MNDS) is proposed to fuse the significance of nodes in sub-networks, and the critical nodes in the multiplex network are identified. Finally, the PTNs in Shenzhen City, China, is used as a case to demonstrate the feasibility of MNDS. By attacking critical nodes, a comparison is conducted and compared with two traditional identification methods (Weighted Closeness Centrality and Technique for Order Preference by Similarity to Ideal Solution) using two indicators, global efficiency (GE) and the size of the largest connected component (LCC). The results indicate the critical nodes identified by MNDS are of greater significance than those identified by the other two methods. This study provides a feasible method for critical nodes identification in urban public transport system, which can be applied to public transport operation and planning.

A Regression Method With Subnetwork Neurons for Vigilance Estimation Using EOG and EEG

In recent years, it has been observed that there is an increasing rate of road accidents due to the low vigilance of drivers. Thus, the estimation of drivers' vigilance state plays a significant role in public transportation safety. We have adopted a feature fusion strategy that combines the electroencephalogram (EEG) signals collected from various sites of the human brain, including forehead, temporal, and posterior and forehead electrooculogram (forehead-EOG) signals, to address this factor. The level of vigilance is predicted through a new learning model known as double-layered neural network with subnetwork nodes (DNNSNs), which comprises several subnetwork nodes, and each node in turn is composed of many hidden nodes that have various capabilities of feature selection (dimension reduced), feature learning, etc. The proposed single modality that uses only forehead-EOG signal exhibits a mean root-mean-square error (RMSE) of 0.12 and a mean Pearson product-moment correlation coefficient (COR) of 0.78. On one hand, an EEG signal achieved a mean RMSE of 0.13 and a mean COR of 0.72. Whereas, on the other, the proposed multimodality achieved values of 0.09 and 0.85 for the mean RMSE and the mean COR, respectively. Experimental results show that the proposed DNNSN with multimodality fusion outperforms the model with single modality for vigilance estimation due to the complementary information between forehead-EOG and EEG. After a favorable learning rate was applied to the input layer, the mean RMSE/COR improved to 0.11/0.79, 0.12/0.74, and 0.08/0.86, respectively. Hence, this quantitative analysis proves that the proposed method provides better feasibility and efficiency learning capability and surmounts other state-of-the-art techniques.

Network-guided bioengineering of oncolytic immunovirotherapies for glioblastoma

Abstract Glioblastoma is the most common and lethal primary brain tumor in adults, with a median survival less than 15 months despite current standard of care. Oncolytic immunovirotherapies (OVs) have shown promise in early clinical trials. OVs are engineered to specifically target cancer cells, trigger autophagic programmed cell death, and release tumor antigens in the context of T-cell activating cytokines. Cellular communication network factor 1 (CCN1) is an extracellular matrix protein expressed in 70% of glioblastomas that has been shown to drastically reduce OV efficacy, particularly herpes simplex virus type 1 (HSV-1). Our objectives were to decode protein-protein interaction (PPI) networks activated by CCN1, identify critical nodes in resistance networks, and use this analysis to design OVs that overcome CCN1-mediated resistance. We used NetDecoder to elucidate phenotype-specific PPI subnetworks in LN229 human glioblastoma cells with tetracycline-inducible CCN1 expression. Publicly available microarray data on CCN1-induced and control samples established differentially expressed genes, which served as sources in our PPI network to derive prioritized context-specific subnetworks. We found that 11 source genes collaborate via 39 deep nodes to confer OV resistance to CCN1-expressing glioblastomas. Of these, a router (IKBKE) and a sink (YBX1) have been previously implicated in glioblastoma pathogenesis. We conclude that autophagy regulator HSP90AA1, a critical node, may be targeted to improve OV efficacy in CCN1-expressing glioblastomas. Furthermore, WebGestalt overrepresentation analysis of subnetwork nodes suggests that oncolytic adenovirus may be more effective than HSV-1 as a synthetic biology chassis.

Faster Single Model Vigilance Detection Based on Deep Learning

Various reports have shown that the rate of road traffic accidents has increased due to reduced driver vigilance. Therefore, an accurate estimation of the driver's alertness status plays an important part. To estimate vigilance, we adopt a novel strategy that is a deep autoencoder with subnetwork nodes (DAE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SN</sub> ). The proposed network model is designed not only for sparse representation but also for dimension reduction. Some hidden layers are not calculated by randomly acquired, but by replacement technologies. Unlike the traditional electrooculogram (EOG) signals, the forehead EOG (EOG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> ) signals are collected through forehead electrodes that do not have to surround the eyes, which has a convenient and effective practical application. The root-mean-square error (RMSE) and correlation coefficient (COR) while separately using three EOG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> features improved to 0.11/0.79, 0.10/0.83, and 0.11/0.80, respectively. Implemented in an experimental environment, percentage of eye closure over time is calculated in real time through SMI eye-tracking-glasses, up to 120 frames/s. In addition, the time to extract features from the raw signal and display the prediction is only 34 ms, that is the level of the driver's fatigue can be detected quickly. The experimental study shows that the proposed model for vigilance analysis has better robustness and learning capability.

A Width-growth Model with Subnetwork Nodes and Refinement Structure for Representation Learning and Image Classification

This article presents a new supervised multilayer subnetwork-based feature refinement and classification model for representation learning. The novelties of this algorithm are as follows: 1) different from most multilayer networks that go deeper with increased number of network layers, this work architects a model with wider subnetwork nodes; 2) the conventional classification methods adopt a separate search mechanism to derive a generalized feature space and to get the final cognition, but this work proposes a one-shot process to find the meaningful latent space and recognize the objects; and 3) the traditional feature representation and image classification approaches apply a unimodal feature coding, which suffers from lack of global knowledge. This work overcomes the pitfall through multimodal fusion that fuses various feature sources into one superstate encoding to achieve higher performance. A cross-domain experimental study on camera identification and image classification shows that the proposed method achieves superior performance compared to the existing models.

Using Network Pharmacology to Explore Potential Treatment Mechanism for Coronary Heart Disease Using Chuanxiong and Jiangxiang Essential Oils in Jingzhi Guanxin Prescriptions.

Background To predict the active components and potential targets of traditional Chinese medicine and to determine the mechanism behind the curative effect of traditional Chinese medicine, a multitargeted method was used. Jingzhi Guanxin prescriptions expressed a high efficacy for coronary heart disease (CHD) patients of which essential oils from Chuanxiong and Jiangxiang were confirmed to be the most important effective substance. However, the interaction between the active components and the targets for the treatment of CHD has not been clearly explained in previous studies. Materials and Methods Genes associated with the disease and the treatment strategy were searched from the electronic database and analyzed by Cytoscape (version 3.2.1). Protein-protein interaction network diagram of CHD with Jiangxiang and Chuanxiong essential oils was constructed by Cytoscape. Pathway functional enrichment analysis was executed by clusterProfiler package in R platform. Results 121 ingredients of Chuanxiong and Jiangxiang essential oils were analyzed, and 393 target genes of the compositions and 912 CHD-related genes were retrieved. 15 coexpression genes were selected, including UGT1A1, DPP4, RXRA, ADH1A, RXRG, UGT1A3, PPARA, TRPC3, CYP1A1, ABCC2, AHR, and ADRA2A. The crucial pathways of occurrence and treatment molecular mechanism of CHD were analyzed, including retinoic acid metabolic process, flavonoid metabolic process, response to xenobiotic stimulus, cellular response to xenobiotic stimulus, cellular response to steroid hormone stimulus, retinoid binding, retinoic acid binding, and monocarboxylic acid binding. Finally, we elucidate the underlying role and mechanism behind these genes in the pathogenesis and treatment of CHD. Conclusions Generally speaking, the nodes in subnetwork affect the pathological process of CHD, thus indicating the mechanism of Jingzhi Guanxin prescriptions containing Chuanxiong and Jiangxiang essential oils in the treatment of CHD.

Features Combined From Hundreds of Midlayers: Hierarchical Networks With Subnetwork Nodes.

In this paper, we believe that the mixed selectivity of neuron in the top layer encodes distributed information produced from other neurons to offer a significant computational advantage over recognition accuracy. Thus, this paper proposes a hierarchical network framework that the learning behaviors of features combined from hundreds of midlayers. First, a subnetwork neuron, which itself could be constructed by other nodes, is functional as a subspace features extractor. The top layer of a hierarchical network needs subspace features produced by the subnetwork neurons to get rid of factors that are not relevant, but at the same time, to recast the subspace features into a mapping space so that the hierarchical network can be processed to generate more reliable cognition. Second, this paper shows that with noniterative learning strategy, the proposed method has a wider and shallower structure, providing a significant role in generalization performance improvements. Hence, compared with other state-of-the-art methods, multiple channel features with the proposed method could provide a comparable or even better performance, which dramatically boosts the learning speed. Our experimental results show that our platform can provide a much better generalization performance than 55 other state-of-the-art methods.

Multilayer Incremental Hybrid Cost-Sensitive Extreme Learning Machine With Multiple Hidden Output Matrix and Subnetwork Hidden Nodes

Recently, multilayer extreme learning machine with subnetwork nodes and cost sensitive learning was applied to representation learning and classification. However, the existing ML-ELM methods suffer from several drawbacks: 1) manual tuning for the number of hidden nodes in each layer will influent the training speed of the model, the best optimal determine approach for the number of hidden nodes is still for probing; 2) random projection of the input weight and bias in each layer still suboptimal and the network structure hard to reach best generalization performance. Inspired by the Subnetwork nodes and cost sensitive methods, using multiple hidden output matrix, a novel ELM method which called Multilayer Incremental Hybrid Cost Sensitive Extreme Learning Machine with Multiple Hidden Output Matrix and Subnetwork Hidden Nodes is proposed in this paper. Whose contributions are 1) a hybrid hidden nodes optimization methods is given which based on ant clone method and multiple grey wolf optimization method; 2) a multiple hidden layer output matrices are being using through weighted calculation of different output matrices which can optimize the network architecture effectively; 3) a novel ELM structure is proposed by merging the merits of Cost Sensitive Extreme Learning Machine and Subnetwork Hidden Nodes, while the regularized parameter $C$ is not sensitive to the generalization performance. Extensive experiments results show that the proposed method achieves better performance than existing state-of-art ELM methods.

Integration of Proteomics and Metabolomics Revealed Metabolite-Protein Networks in ACTH-Secreting Pituitary Adenoma.

An effective treatment for the management of adrenocorticotropic hormone-secreting pituitary adenomas (ACTH-PA) is currently lacking, although surgery is a treatment option. We have integrated information obtained at the metabolomic and proteomic levels to identify critical networks and signaling pathways that may play important roles in the metabolic regulation of ACTH-PA and therefore hopefully represent potential therapeutic targets. Six ACTH-PAs and seven normal pituitary glands were investigated via gas chromatography-mass spectrometry (GC-MS) analysis for metabolomics. Five ACTH-PAs and five normal pituitary glands were subjected to proteomics analysis via nano liquid chromatography tandem-mass spectrometry (nanoLC-MS/MS). The joint pathway analysis and network analysis was performed using MetaboAnalyst 3.0. software. There were significant differences of metabolites and protein expression levels between the ACTH-PAs and normal pituitary glands. A proteomic analysis identified 417 differentially expressed proteins that were significantly enriched in the Myc signaling pathway. The protein–metabolite joint pathway analysis showed that differentially expressed proteins and metabolites were significantly enriched in glycolysis/gluconeogenesis, pyruvate metabolism, citrate cycle (TCA cycle), and the fatty acid metabolism pathway in ACTH-PA. The protein–metabolite molecular interaction network identified from the metabolomics and proteomics investigation resulted in four subnetworks. Ten nodes in subnetwork 1 were the most significantly enriched in cell amino acid metabolism and pyrimidine nucleotide metabolism. Additionally, the metabolite–gene–disease interaction network established nine subnetworks. Ninety-two nodes in subnetwork 1 were the most significantly enriched in carboxylic acid metabolism and organic acid metabolism. The present study clarified the pathway networks that function in ACTH-PA. Our results demonstrated the presence of downregulated glycolysis and fatty acid synthesis in this tumor type. We also revealed that the Myc signaling pathway significantly participated in the metabolic changes and tumorigenesis of ACTH-PA. This data may provide biomarkers for ACTH-PA diagnosis and monitoring, and could also lead to the development of novel strategies for treating pituitary adenomas.

A random forest classifier predicts recurrence risk in patients with ovarian cancer.

Ovarian cancer (OC) is associated with a poor prognosis due to difficulties in early detection. The aims of the present study were to construct a recurrence risk prediction model and to reveal important OC genes or pathways. RNA sequencing data was obtained for 307 OC samples, and the corresponding clinical data were downloaded from The Cancer Genome Atlas database. Additionally, two validation datasets, GSE44104 (20 recurrent and 40 non-recurrent OC samples) and GSE49997 (204 OC samples), were obtained from the Gene Expression Omnibus database. Differentially expressed genes were screened using the differential expression via distance synthesis algorithm, followed by gene ontology enrichment analysis and weighted gene coexpression network analysis (WGCNA). Furthermore, subnetwork analysis was conducted for the protein-protein interaction (PPI) network using the BioNet package. Finally, a random forest classifier was constructed based on the subnetwork nodes, and its reliability was validated using the GSE44104 and GSE49997 validation datasets. A total of 44 upregulated and 117 downregulated genes were identified in the recurrent samples. Enrichment analysis indicated that cytochrome P450 family 17 subfamily A member 1 (CYP17A1) was associated with ‘positive regulation of steroid hormone biosynthetic processes’. WGCNA identified turquoise and grey modules that were significantly correlated with status and prognosis. A significant PPI subnetwork containing 16 nodes was also identified, including: Transcription factor GATA-4; fibroblast growth factor 9; aromatase; 3β-hydroxysteroid dehydrogenase/δ5-4-isomerase type 2; corticosteroid 11β-dehydrogenase isozyme 1; CYP17A1; pituitary homeobox 2; left-right determination factor 1; homeobox protein ARX; estrogen receptor β; steroidogenic factor 1; forkhead box protein L2; myocardin; steroidogenic acute regulatory protein mitochondrial; vesicular inhibitory amino acid transporter; and twist-related protein 1. A random forest classifier was constructed using the subnetwork nodes as feature genes, which exhibited a 92% true positive rate when classifying recurrent and non-recurrent OC samples. The classifying efficiency of the random forest classifier was validated using the two other independent datasets. Overall, 44 upregulated and 117 downregulated genes associated with OC recurrence were identified. Furthermore, the 16 subnetwork node genes that were identified may be important molecules in OC recurrence.

EEG-Based Emotion Recognition Using Hierarchical Network With Subnetwork Nodes

Emotions play a crucial role in decision-making, brain activity, human cognition, and social intercourse. This paper proposes a hierarchical network structure with subnetwork nodes to discriminate three human emotions: 1) positive; 2) neutral; and 3) negative. Each subnetwork node embedded in the network that are formed by hundreds of hidden nodes, could be functional as an independent hidden layer for feature representation. The top layer of the hierarchical network, like the mammal cortex in the brain, combine such features generated from subnetwork nodes, but simultaneously, recast these features into a mapping space so that the network can be performed to produce more reliable cognition. The proposed method is compared with other state-of-the-art methods. The experimental results from two different EEG datasets show that a promising result is obtained when using the proposed method with both single and multiple modality.

Finding the shortest path with PesCa: a tool for network reconstruction.

Network analysis is of growing interest in several fields ranging from economicsto biology. Several methods have been developed to investigate different propertiesof physical networks abstracted as graphs, including quantification of specifictopological properties, contextual data enrichment, simulation of pathway dynamicsand visual representation. In this context, the PesCa app for the Cytoscapenetwork analysis environment is specifically designed to help researchers infer andmanipulate networks based on the shortest path principle. PesCa offers different algorithmsallowing network reconstruction and analysis starting from a list of genes,proteins and in general a set of interconnected nodes. The app is useful in theearly stage of network analysis, i.e. to create networks or generate clusters basedon shortest path computation, but can also help further investigations and, in general,it is suitable for every situation requiring the connection of a set of nodes thatapparently do not share links, such as isolated nodes in sub-networks. Overall,the plugin enhances the ability of discovering interesting and not obvious relationsbetween high dimensional sets of interacting objects.

Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning.

The extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.

Finding the shortest path with PesCa: a tool for network reconstruction

Network analysis is of growing interest in several fields ranging from economics to biology. Several methods have been developed to investigate different properties of physical networks abstracted as graphs, including quantification of specific topological properties, contextual data enrichment, simulation of pathway dynamics and visual representation. In this context, the PesCa app for the Cytoscape network analysis environment is specifically designed to help researchers infer and manipulate networks based on the shortest path principle. PesCa offers different algorithms allowing network reconstruction and analysis starting from a list of genes, proteins and in general a set of interconnected nodes. The app is useful in the early stage of network analysis, i.e. to create networks or generate clusters based on shortest path computation, but can also help further investigations and, in general, it is suitable for every situation requiring the connection of a set of nodes that apparently do not share links, such as isolated nodes in sub-networks. Overall, the plugin enhances the ability of discovering interesting and not obvious relations between high dimensional sets of interacting objects.

Finding the shortest path with PesCa: a tool for network reconstruction

Network analysis is of growing interest in several fields ranging from economicsto biology. Several methods have been developed to investigate different propertiesof physical networks abstracted as graphs, including quantification of specifictopological properties, contextual data enrichment, simulation of pathway dynamicsand visual representation. In this context, the PesCa app for the Cytoscapenetwork analysis environment is specifically designed to help researchers infer andmanipulate networks based on the shortest path principle. PesCa offers different algorithmsallowing network reconstruction and analysis starting from a list of genes,proteins and in general a set of interconnected nodes. The app is useful in theearly stage of network analysis, i.e. to create networks or generate clusters basedon shortest path computation, but can also help further investigations and, in general,it is suitable for every situation requiring the connection of a set of nodes thatapparently do not share links, such as isolated nodes in sub-networks. Overall,the plugin enhances the ability of discovering interesting and not obvious relationsbetween high dimensional sets of interacting objects.

Greedy immunization strategy in weighted scale-free networks

Purpose – The purpose of this paper is to present a new immunization strategy for effectively solving the control of the spread of the virus. Design/methodology/approach – Inspired by the idea of network partition, taking two optimization targets which are the scale of sub-network and the sum of the strengths of the sub-network's nodes into account at the same time, a new immunization strategy based on greedy algorithm in the scale-free network is presented. After specifying the number of nodes through the immunization, the network is divided into the scale of sub-network and the sum of the strength of the sub-network's nodes as small as possible. Findings – The experimental results show that the proposed algorithm has the better performance than targeted immunization which is supposed to be highly efficient at present. Originality/value – This paper proposes a new immunization strategy based on greedy algorithm in the scale-free network for effectively solving the control of the spread of the virus.

Social network analysis and network connectedness analysis for industrial symbiotic systems: model development and case study

An important and practical pattern of industrial symbiosis is rapidly developing: eco-industrial parks. In this study, we used social network analysis to study the network connectedness (i.e., the proportion of the theoretical number of connections that had been achieved) and related attributes of these hybrid ecological and industrial symbiotic systems. This approach provided insights into details of the network’s interior and analyzed the overall degree of connectedness and the relationships among the nodes within the network. We then characterized the structural attributes of the network and subnetwork nodes at two levels (core and periphery), thereby providing insights into the operational problems within each eco-industrial park. We chose ten typical ecoindustrial parks in China and around the world and compared the degree of network connectedness of these systems that resulted from exchanges of products, byproducts, and wastes. By analyzing the density and nodal degree, we determined the relative power and status of the nodes in these networks, as well as other structural attributes such as the core-periphery structure and the degree of sub-network connectedness. The results reveal the operational problems created by the structure of the industrial networks and provide a basis for improving the degree of completeness, thereby increasing their potential for sustainable development and enriching the methods available for the study of industrial symbiosis.

Properties of Elements in Fault Delivering in Power System

This paper presents a novel method for Properties of Elements in Fault Delivering in power system. It is based on the properties of elements in fault delivering. the upstream fault indices of all nodes in sub-network also can be calculated. By synthesizing the upstream and downstream reliable indices, the reliable indices of all nodes in the sub-network can be obtained. the proposal method not only has the same effect but also save computing time. The results of simulation applying the proposed method to test system adequately showed that this method is effective

Neural computations of algebraic and geometrical structures

How Artificial Neural Networks (ANN) can be used to solve problems in algebra and geometry by modelling specific subnetwork nodes and connections is considered. This approach has the benefit of producing ANNs with well-defined hidden units and reduces the search to parameters which satisfy known model constraints—yet still gains from the benefits inherent in neural computing architectures.