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  • Open Access


    β-1,3-Galactosyl-O-Glycosyl-Glycoprotein β-1,6-N-Acetylglucosaminyltransferase 3 Increases MCAM Stability, Which Enhances S100A8/A9-Mediated Cancer Motility

    I Wayan Sumardika*†, Chen Youyi*, Eisaku Kondo, Yusuke Inoue§, I Made Winarsa Ruma*†, Hitoshi Murata*, Rie Kinoshita*, Ken-Ichi Yamamoto*, Shuta Tomida, Kazuhiko Shien#, Hiroki Sato#, Akira Yamauchi**, Junichiro Futami††, Endy Widya Putranto‡‡, Toshihiko Hibino§§, Shinichi Toyooka¶#¶¶, Masahiro Nishibori##, Masakiyo Sakaguchi*

    Oncology Research, Vol.26, No.3, pp. 431-444, 2018, DOI:10.3727/096504017X15031557924123

    Abstract We previously identified novel S100A8/A9 receptors, extracellular matrix metalloproteinase inducer (EMMPRIN), melanoma cell adhesion molecule (MCAM), activated leukocyte cell adhesion molecule (ALCAM), and neuroplastin (NPTN) β, that are critically involved in S100A8/A9-mediated cancer metastasis and inflammation when expressed at high levels. However, little is known about the presence of any cancerspecific mechanism(s) that modifies these receptors, further inducing upregulation at protein levels without any transcriptional regulation. Expression levels of glycosyltransferase-encoding genes were examined by a PCRbased profiling array followed by confirmation with quantitative real-time PCR. Cell migration and invasion were assessed using a Boyden chamber.… More >

  • Open Access


    Identification and Transcriptional Regulation of CAMTA Genes in Liriodendron chinense

    Kaiyue Hong, Yasmina Radani, Teja Manda, Jinhui Chen, Liming Yang*

    Phyton-International Journal of Experimental Botany, Vol.93, No.3, pp. 413-425, 2024, DOI:10.32604/phyton.2024.047739

    Abstract This study explores CAMTA genes in the rare and endangered Chinese plant species, Liriodendron chinense. Despite the completion of whole-genome sequencing, the roles of CAMTA genes in calcium regulation and stress responses in this species remain largely unexplored. Within the L. chinense genome, we identified two CAMTA genes, Lchi09764 and Lchi222536, characterized by four functional domains: CG-1, TIG, ANK repeats, and IQ motifs. Our analyses, including phylogenetic investigations, cis-regulatory element analyses, and chromosomal location studies, aim to elucidate the defining features of CAMTA genes in L. chinense. Applying Weighted Gene Co-Expression Network Analysis (WGCNA), we explored the impact of CAMTA genes on More >

  • Open Access


    BCCLR: A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

    Yunhe Wang1, Yuxin Xia2, Shuai Liu2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4489-4507, 2024, DOI:10.32604/cmc.2024.048813

    Abstract In recent years, skeleton-based action recognition has made great achievements in Computer Vision. A graph convolutional network (GCN) is effective for action recognition, modelling the human skeleton as a spatio-temporal graph. Most GCNs define the graph topology by physical relations of the human joints. However, this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs, resulting in a low recognition rate for specific actions with implicit correlation between joint pairs. In addition, existing methods ignore the trend correlation between adjacent frames within an action… More >

  • Open Access


    IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

    Yajing Ma1,2,3, Gulila Altenbek1,2,3,*, Yingxia Yu1

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2023.045486

    Abstract Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events, we propose an Independent Recurrent Temporal Graph Convolution Networks (IndRT-GCNets) framework to efficiently and accurately capture event attribute information. The framework models the knowledge graph sequences to learn the evolutionary representations of entities and relations within each period. Firstly, by utilizing the temporal graph convolution module in the evolutionary representation unit, the framework captures the structural dependency relationships within the knowledge graph in each period. Meanwhile, to achieve better event… More >

  • Open Access


    Identification of tumor-suppressor genes in lung squamous cell carcinoma through integrated bioinformatics analyses


    Oncology Research, Vol.32, No.1, pp. 187-197, 2024, DOI:10.32604/or.2023.030656

    Abstract Lung cancer is a prevalent malignancy, and fatalities of the disease exceed 400,000 cases worldwide. Lung squamous cell carcinoma (LUSC) has been recognized as the most common pathological form of lung cancer. The comprehensive understanding of molecular features related to LUSC progression has great significance in LUSC prognosis assessment and clinical management. In this study, we aim to identify a panel of signature genes closely associated with LUSC, which can provide novel insights into the progression of LUSC. Gene expression profiles were retrieved from public resources including gene expression omnibus (GEO) and the cancer genome… More >

  • Open Access


    DFE-GCN: Dual Feature Enhanced Graph Convolutional Network for Controversy Detection

    Chengfei Hua1,2,3, Wenzhong Yang2,3,*, Liejun Wang2,3, Fuyuan Wei2,3, KeZiErBieKe HaiLaTi2,3, Yuanyuan Liao2,3

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 893-909, 2023, DOI:10.32604/cmc.2023.040862

    Abstract With the development of social media and the prevalence of mobile devices, an increasing number of people tend to use social media platforms to express their opinions and attitudes, leading to many online controversies. These online controversies can severely threaten social stability, making automatic detection of controversies particularly necessary. Most controversy detection methods currently focus on mining features from text semantics and propagation structures. However, these methods have two drawbacks: 1) limited ability to capture structural features and failure to learn deeper structural features, and 2) neglecting the influence of topic information and ineffective utilization… More >

  • Open Access


    A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

    Tao Liu1, Kejia Zhang1,*, Jingsong Yin1, Yan Zhang1, Zihao Mu1, Chunsheng Li1, Yanan Hu2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2563-2582, 2023, DOI:10.32604/csse.2023.041228

    Abstract Spatio-temporal heterogeneous data is the database for decision-making in many fields, and checking its accuracy can provide data support for making decisions. Due to the randomness, complexity, global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions, traditional detection methods can not guarantee both detection speed and accuracy. Therefore, this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks. Firstly, the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted… More >

  • Open Access


    Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

    Qingyue Zhao1, Qiaoyu Gu2, Zhijun Gao3,*, Shipian Shao1, Xinyuan Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1773-1788, 2023, DOI:10.32604/cmes.2023.027500

    Abstract Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA) model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series, and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence. Finally, More >

  • Open Access


    Exploring the attenuation mechanisms of Dalbergia odorifera leaves extract on cerebral ischemia-reperfusion based on weighted gene co-expression network analysis


    BIOCELL, Vol.47, No.7, pp. 1611-1622, 2023, DOI:10.32604/biocell.2023.028684

    Abstract Background: The attenuation function of Dalbergia odorifera leaves on cerebral ischemia-reperfusion (I/R) is little known. The candidate targets for the Chinese herb were extracted from brain tissues through the high-affinity chromatography. The molecular mechanism of D. odorifera leaves on cerebral I/R was investigated. Methods: Serial affinity chromatography based on D. odorifera leaves extract (DLE) affinity matrices were applied to find specific binding proteins in the brain tissues implemented on C57BL/6 mice by intraluminal middle cerebral artery occlusion for 1 h and reperfusion for 24 h. Specific binding proteins were subjected to mass-spectrometry to search for the differentially expressed… More >

  • Open Access


    Anomaly Detection for Cloud Systems with Dynamic Spatiotemporal Learning

    Mingguang Yu1,2, Xia Zhang1,2,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1787-1806, 2023, DOI:10.32604/iasc.2023.038798

    Abstract As cloud system architectures evolve continuously, the interactions among distributed components in various roles become increasingly complex. This complexity makes it difficult to detect anomalies in cloud systems. The system status can no longer be determined through individual key performance indicators (KPIs) but through joint judgments based on synergistic relationships among distributed components. Furthermore, anomalies in modern cloud systems are usually not sudden crashes but rather gradual, chronic, localized failures or quality degradations in a weakly available state. Therefore, accurately modeling cloud systems and mining the hidden system state is crucial. To address this challenge,… More >

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