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  • Open Access

    ARTICLE

    Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

    Shijie Tang1,2, Yong Ding1,3,4,*, Huiyong Wang5

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1129-1150, 2025, DOI:10.32604/cmc.2024.059143 - 03 January 2025

    Abstract As more and more devices in Cyber-Physical Systems (CPS) are connected to the Internet, physical components such as programmable logic controller (PLC), sensors, and actuators are facing greater risks of network attacks, and fast and accurate attack detection techniques are crucial. The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series. To address this issue, we propose an anomaly detection method based on distributed deep learning. Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the More >

  • Open Access

    ARTICLE

    Anomaly Detection of Controllable Electric Vehicles through Node Equation against Aggregation Attack

    Jing Guo*, Ziying Wang, Yajuan Guo, Haitao Jiang

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 427-442, 2025, DOI:10.32604/cmc.2024.057045 - 03 January 2025

    Abstract The rapid proliferation of electric vehicle (EV) charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system. This study presents an innovative anomaly detection framework for EV charging stations, addressing the unique challenges posed by third-party aggregation platforms. Our approach integrates node equations-based on the parameter identification with a novel deep learning model, xDeepCIN, to detect abnormal data reporting indicative of aggregation attacks. We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation. The xDeepCIN model, incorporating a Compressed Interaction Network, has the ability… More >

  • Open Access

    ARTICLE

    HNRNPC as a pan-cancer biomarker and therapeutic target involved in tumor progression and immune regulation

    YUEZHOU ZHANG1,#, ZHAO ZHANG2,#, JINXIN DONG1, CHANGAN LIU1,*

    Oncology Research, Vol.33, No.1, pp. 83-102, 2025, DOI:10.32604/or.2024.055866 - 20 December 2024

    Abstract Background: Aberrant expression of RNA-binding proteins (RBPs) has been linked to a variety of diseases, including hematological disorders, cardiovascular diseases, and multiple types of cancer. Heterogeneous nuclear ribonucleoprotein C (HNRNPC), a member belonging to the heterogeneous nuclear ribonucleoprotein (hnRNP) family, plays a pivotal role in nucleic acid metabolism. Previous studies have underscored the significance of HNRNPC in tumorigenesis; however, its specific role in malignant tumor progression remains inadequately characterized. Methods: We leveraged publicly available databases, including The Cancer Genome Atlas (TCGA), to explore the potential involvement of HNRNPC across various cancers. Additionally, we performed experimental… More > Graphic Abstract

    HNRNPC as a pan-cancer biomarker and therapeutic target involved in tumor progression and immune regulation

  • Open Access

    ARTICLE

    The impact of alpha-fetoprotein (AFP), child-turcotte-pugh (CTP) score and disease staging on the survival of hepatocellular carcinoma (HCC) patients: a retrospective cohort from single oncology center

    NASSER MULLA1,*, YOUSEF KATIB2, ASIM M. ALMUGHAMSI3, DUAA S. ALKHAYAT1, MOHAMED MOSAAD1,4, SAMIR T. ALFOTIH5, RAWAN ALAOFI6

    Oncology Research, Vol.33, No.1, pp. 149-160, 2025, DOI:10.32604/or.2024.050903 - 20 December 2024

    Abstract Background: Hepatocellular carcinoma (HCC) is the most common cause of cancer-related death in Saudi Arabia. Our study aimed to investigate the patterns of HCC and the effect of TNM staging, Alfa-fetoprotein (AFP), and Child-Turcotte Pugh (CTP) on patients’ overall survival (OS). Methods: A retrospective analysis was conducted on 43 HCC patients at a single oncology center in Saudi Arabia from 2015 to 2020. All patients had to fulfill one of the following criteria: (a) a liver lesion reported as definitive HCC on dynamic imaging and/or (b) a biopsy-confirmed diagnosis. Results: The mean patient age of all… More >

  • Open Access

    ARTICLE

    STIL enhances the development of lung adenocarcinoma by regulating the glycolysis pathway

    LEI WANG1, XIANJIN XIE2,*

    Oncology Research, Vol.33, No.1, pp. 123-132, 2025, DOI:10.32604/or.2024.048562 - 20 December 2024

    Abstract Background: To investigate SCL/TAL 1 interrupting locus (STIL)’s role and prognostic significance in lung adenocarcinoma (LUAD) progression, we examined STIL and E2 promoter binding factor 1 (E2F1) expression and their impacts on LUAD prognosis using Gene Expression Profiling Interactive Analysis (GEPIA). Methods: Functional assays including CCK-8, wound-healing, 5-ethynyl-2-deoxyuridine (EdU), Transwell assays, and flow cytometry, elucidated STIL and E2F1’s effects on cell viability, proliferation, apoptosis, and migration. Gene set enrichment analysis (GSEA) identified potential pathways, while metabolic assays assessed glucose metabolism. Results: Our findings reveal that STIL and E2F1 are overexpressed in LUAD, correlating with adverse outcomes. It enhances More >

  • Open Access

    ARTICLE

    Long noncoding RNA LINC01106 promotes lung adenocarcinoma progression via upregulation of autophagy

    GENGYUN SUN1,*, YIPING ZHENG1,2, JIANFENG CAI2, JIE GAO2, LIE DONG2, XIANGBIN ZHANG2, YINGHUI HUANG2,*

    Oncology Research, Vol.33, No.1, pp. 171-184, 2025, DOI:10.32604/or.2024.047626 - 20 December 2024

    Abstract Background: Long noncoding RNA, LINC01106 exhibits high expression in lung adenocarcinoma (LUAD) tumor tissues, but its functional role and regulatory mechanism in LUAD cells remain unclear. Methods: LINC01106 expression was analyzed in LUAD tissues and its functional impact on LUAD cells was assessed. LUAD cells were silenced with sh-LINC01106 and injected into nude mice to investigate tumor growth. The downstream transcription factors and molecular mechanism were determined using the Human transcription factor database (TFDB) database and Gene Expression Profiling Interactive Analysis (GEPIA) database. Additionally, the impact of linc01106 on autophagy was analyzed by determining the… More > Graphic Abstract

    Long noncoding RNA LINC01106 promotes lung adenocarcinoma progression via upregulation of autophagy

  • Open Access

    REVIEW

    Emerging pharmaceutical therapies for targeting cholangiocarcinoma microenvironment and chemokine pathways

    ARMAND N. YAZDANI1, MICHAELA PLETSCH1, ABRAHAM CHORBAJIAN1, DAVID ZITSER1, VIKRANT RAI1,2,*

    BIOCELL, Vol.48, No.12, pp. 1683-1702, 2024, DOI:10.32604/biocell.2024.056252 - 30 December 2024

    Abstract Mixed cholangiocarcinoma is a rare and aggressive neoplastic proliferation of biliary tract epithelial cells, accounting for up to 20% of primary liver cancers. It is the second most common primary liver malignancy with a 5-year survivability of less than 10% at diagnosis and is associated with various inflammatory diseases. Current management involves systemic chemotherapy, targeted radiation, and surgical resection, but long-term survival remains low, especially for surgically unresectable cases. Novel discoveries and understandings of the tumor microenvironment reveal new opportunities for targeted therapies for cholangiocarcinoma. Specifically, new pharmaceuticals including cell-based vaccines, tumor-associated neutrophils, and hepatic… More >

  • Open Access

    ARTICLE

    Unveiling the predictive power of bacterial response-related genes signature in hepatocellular carcinoma: with bioinformatics analyses and experimental approaches

    ATIEH POURBAGHERI-SIGAROODI1, MAJID MOMENY2, NIMA REZAEI3,4,5, FATEMEH FALLAH1,*, DAVOOD BASHASH6,*

    BIOCELL, Vol.48, No.12, pp. 1781-1804, 2024, DOI:10.32604/biocell.2024.055848 - 30 December 2024

    Abstract Background: Despite progress in therapeutic strategies, treatment failure in hepatocellular carcinoma (HCC) remains a major challenge, resulting in low survival rates. The presence of bacteria and the host’s immune response to bacteria can influence the pathogenesis and progression of HCC. We developed a risk model based on bacterial response-related genes (BRGs) using gene sets from molecular signature databases to identify new markers for predicting HCC outcomes and categorizing patients into different risk groups. Methods: The data from the Cancer Genome Atlas (TCGA) portal was retrieved, and differentially expressed BRGs were identified. Uni- and multivariate Cox… More >

  • Open Access

    ARTICLE

    Enhanced Diagnostic Precision: Deep Learning for Tumors Lesion Classification in Dermatology

    Rafid Sagban1,2,*, Haydar Abdulameer Marhoon3,4, Saadaldeen Rashid Ahmed5,6,*

    Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 1035-1051, 2024, DOI:10.32604/iasc.2024.058416 - 30 December 2024

    Abstract Skin cancer is a highly frequent kind of cancer. Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities. Melanoma, basal, and squamous cell carcinomas are well-recognized cutaneous malignancies. Malignant We can differentiate Melanoma from non-pigmented carcinomas like basal and squamous cell carcinoma. The research on developing automated skin cancer detection systems has primarily focused on pigmented malignant type melanoma. The limited availability of datasets with a wide range of lesion categories has hindered in-depth exploration of non-pigmented malignant skin lesions. The present study investigates the feasibility of automated methods for… More >

  • Open Access

    ARTICLE

    A Scalable and Generalized Deep Ensemble Model for Road Anomaly Detection in Surveillance Videos

    Sarfaraz Natha1,2,*, Fareed A. Jokhio1, Mehwish Laghari1, Mohammad Siraj3,*, Saif A. Alsaif3, Usman Ashraf4, Asghar Ali5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3707-3729, 2024, DOI:10.32604/cmc.2024.057684 - 19 December 2024

    Abstract Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are used to surveillance and monitor the normal and anomalous incidents. Real-world anomaly detection is a significant challenge due to its complex and diverse nature. It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems, and the need for automated techniques has been raised to enhance detection accuracy. This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack… More >

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