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Intrumer:A Multi Module Distributed Explainable IDS/IPS for Securing Cloud Environment

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Computers, Materials & Continua 2025年第1期82卷 579-607页

作者： Nazreen banu A S.K.b.Sangeetha Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai600026Tamil NaduIndia

The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have been developed to tackle these ***,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.

关键词： Cloud computing intrusion detection system transformers and explainable artificial intelligence(XAI)

Neuroinflammation revisited through the microglial lens

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Neural Regeneration Research 2025年第7期20卷 1989-1990页

作者： Renato Socodato João b.Relvas Institute of Research and Innovation in Health(i3S)and Institute for Molecular and Cell biology(IbMC) University of PortoPortoPortugal Department of biomedicine Faculty of Medicine of the University of Porto(FMUP)PortoPortugal

Neuroimmunology is emerging as a pivotal field,shedding light on the intricate dialogues between the central nervous system(CNS)and the immune *** exploration is particularly significant in understanding microglia,the CNS’s innate immune cells,beyond the conventional conflation of“neuroinflammation”and“microglial activation.”This conflation has clouded the true complexity of these processes,potentially stalling scientific progress and the development of new *** challenge the long-standing perspectives that have oversimplified these interactions,advocating for a deeper exploration of the dynamic relationship between neuroinflammation and microglial *** dissecting specific molecular pathways,we aim to illuminate their elaborate roles in neuroinflammatory responses,especially in the context of Alzheimer’s disease(AD).Here,neuroinflammation is not merely a passive observer or a direct antagonist but a complex agent in the disease’s *** article calls for a significant paradigm shift towards an integrative,multi-omics approach to *** such a comprehensive framework is crucial for advancing our understanding of neuroinflammatory conditions and paving the way for targeted therapeutic strategies for brain diseases.

关键词： inflammation simplified activation

Federated Learning’s Role in Next-Gen TV Ad Optimization

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Computers, Materials & Continua 2025年第1期82卷 675-712页

作者： Gabriela Dobrita Simona-Vasilica Oprea Adela bâra Department of Economic Informatics and Cybernetics Bucharest University of Economic StudiesBucharest010374Romania

In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become *** methods often operate in silos,limiting the potential insights gained from broader data analysis due to concerns over privacy and data *** article introduces a novel approach that leverages Federated Learning(FL)to enhance TV ad schedule optimization,combining the strengths of local optimization techniques with the power of global Machine Learning(ML)models to uncover actionable insights without compromising data *** combines linear programming for initial ads scheduling optimization with ML—specifically,a K-Nearest Neighbors(KNN)model—for predicting ad spot *** into account the diversity and the difficulty of the ad-scheduling problem,we propose a prescriptivepredictive approach in which first the position of the ads is optimized(using Google’s OR-Tools CP-SAT)and then the scheduled position of all ads will be the result of the optimization ***,this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale(using KNN,Light Gradient boosting Machine and Random Forest).Furthermore,we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data *** FL approach resulted in 8750 ads being precisely matched to their optimal category placements,showcasing an alignment with the intended diversity ***,there was a minimal deviation observed,with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset.

关键词： Ad scheduling prescriptive-predictive approach federated learning KNN

Deep learning identification of novel autophagic protein-protein interactions and experimental validation of beclin 2-Ubiquilin 1 axis in triple-negative breast cancer

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Oncology research 2025年第1期33卷 67-81页

作者： Xiang Li Wenke Jin Lifeng Wu Huan Wang Xin Xie Wei Huang b O Liu State Key Laboratory of Southwestern Chinese Medicine Resources School of Pharmacy and College of Medical Technology Chengdu University of Traditional Chinese Medicine Chengdu 611137 China. Department of biotherapy Cancer Center and State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu 610041 China.

background:Triple-negative breast cancer (TNbC), characterized by its lack of traditional hormone receptors and HER2, presents a significant challenge in oncology due to its poor response to conventional therapies. Autophagy is an important process for maintaining cellular homeostasis, and there are currently autophagy biomarkers that play an effective role in the clinical treatment of tumors. In contrast to targeting protein activity, intervention with protein-protein interaction (PPI) can avoid unrelated crosstalk and regulate the autophagy process with minimal interference pathways.Methods:Here, we employed Naive bayes, Decision Tree, and k-Nearest Neighbors to elucidate the complex PPI network associated with autophagy in TNbC, aiming to uncover novel therapeutic targets. Meanwhile, the candidate proteins interacting with beclin 2 were initially screened in MDA-Mb-231 cells using beclin 2 as bait protein by immunoprecipitation-mass spectrometry assay, and the interaction relationship was verified by molecular docking and CO-IP experiments after intersection. Colony formation, cellular immunofluorescence, cell scratch and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) tests were used to predict the clinical therapeutic effects of manipulating candidate PPI.Results:by developing three PPI classification models and analyzing over 13,000 datasets, we identified 3733 previously unknown autophagy-related PPIs. Our network analysis revealed the central role of beclin 2 in autophagy regulation, uncovering its interactions with 39 newly identified proteins. Notably, the CO-IP studies identified the substantial interaction between beclin 2 and Ubiquilin 1, which was anticipated by our model and discovered in immunoprecipitation-mass spectrometry assay results. Subsequently, investigations showed that overexpressing beclin 2 increased Ubiquilin 1, promoted autophagy-dependent cell death, and inhibited proliferation and metastasis in MDA-Mb-231 cells.

关键词： Artificial intelligence (AI) Autophagy beclin 2 Protein-protein interactions (PPI) Triple-negative breast cancer (TNbC) Ubiquilin 1

Piling up multidecker pyrgos[n]cages as antibacterial materials

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Science China Materials 2025年第01期 314-316页

作者： Miguel A.Liuzzi-Vaamonde Jackson b.Lancaster Samantha A.Mason Yuanning Feng Department of Chemistry and biochemistry The University of Oklahoma

The development of antibiotics is regarded as one of the most significant scientific advances in modern medicine. before the commercialization of antibiotics, infectious diseases were catastrophic throughout history [1]. Penicillin was used to treat serious infections in the 1940s, but unfortunately, with the introduction of antibiotics came the emergence of antibiotic resistance [2,3]. Evolved and adapted methicillin-resistant Staphylococcus aureus (MRSA) strains are presently impacting community and healthcare systems across the globe [4].

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Speedy Grass Stomata： Emerging Molecular anc Evolutionary Features

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Molecular Plant 2017年第7期10卷 912-914页

作者： Shengguan Cai Maria Papanatsiou Michael R. blatt Zhong-Hua Chen College of Agriculture and biotechnology Zhejiang University Hangzhou 310058 China School of Science and Health Hawkesbury Institute for the Environment Western Sydney University Penrith NSW 2751 Australia Laboratory of Plant Physiology and biophysics University of Glasgow Glasgow G12 8QQ UK

Stomata in most land plants are formed by a pair of guard cells, controlling the water loss and the carbon dioxide uptake. The development, patterning, and density of stomata are fundamental traits for stomatal functi...

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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi bansal Razaz Waheeb Attar Mosiur Rahman brij b.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

Precore/basal core promoter mutants and hepatitis b viral DNA levels as predictors for liver deaths and hepatocellular carcinoma

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World Journal of Gastroenterology 2006年第41期12卷 6620-6626页

作者： Myron J Tong Lawrence M blatt Jia-Horng Kao Jason Tzuying Cheng William G Corey The Liver Center Huntington Medical Research InstitutesPasadenaCaliforniaUnited States The Pfleger Liver InstituteDavid Geffen Schoolof Medicine at the University of California in Los AngelesCaliforniaUnited States Intermune Inc.BrisbaneCaliforniaUnited States The Hepatitis Research Center National TaiwanUniversity HospitalTaipeiTaiwanChina The Liver Center Huntington Medical Research InstitutesPasadenaCaliforniaUnited States

AIM： To conduct a retrospective study in 400 chronic hepatitis b patients in order to identify hepatitis b viral factors associated with complications of liver disease or development of hepatocellular carcinoma. METHODS： The mean follow-up time was 83.6 ± 39.6 mo. Alpha-fetoprotein test and abdominal ultrasound were used for cancer surveillance. Hepatitis b basal core promoter mutants, precore mutants, genotypes, hepatitis b viral DNA （HbV DNA） level and hepatitis b e antigen （HbeAg） were measured. Univariate analysis and logistic regression were used to assess odds ratios for viral factors related to liver deaths and hepatocellular carcinoma development. RESULTS： During follow-up, 38 patients had liver deaths not related to hepatocellular carcinoma. On multivariate analysis, older age [odds ratio： 95.74 （12.13-891.31）, P 〈 0.0001], male sex [odds ratio： 7.61 （2.20-47.95）; P = 0.006], and higher Iogzo HbV DNA [odds ratio： 4.69 （1.16-20.43）; P 〈 0.0001] were independently predictive for these liver related deaths. Also, 31 patients developed hepatocellular carcinoma. Multivariate analysis showed that older age [odds ratio： 26.51 （2.36-381.47）; P = 0.007], presence of precore mutants [odds ratio： 4.23 （1.53-19.58）, P = 0.02] and presence of basal core promoter mutants [odds ratio： 2.93 （1.24-7.57）; P = 0.02] were independent predictors for progression to hepatocellular carcinoma. CONCLUSION： Our results show that high levels of baseline serum HbV DNA are associated with non- hepatocellular carcinoma-related deaths of liver failure, while genetic mutations in the basal core promoter and precore regions are predictive for development of hepatocellular carcinoma.

关键词： basal core promoter mutants Precore mutants Hepatitis b viral genotypes Hepatitis b viral DNA Hepatitis b e antigen Liver failure Hepatocellular carcinoma

NEAR CIRCULARITY AND ZEROS OF THE ERROR FUNCTION FOR CHEbYSHEV APPROXIMATION 0N A DISK

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Analysis in Theory and Applications 1986年第1期 65-80页

作者： Hans-Peter blatt Lehr stchl fur Mathematik Katholische Universitat Eichstatt F.R.G.

1. Introduction A classical characterization of the best Chebyshev polynomialapproximation to a real continuous function on an interval [a, b]is the equioscillation property of the optimal error ***, when approximatin... 详细信息

关键词： NEAR CIRCULARITY AND ZEROS OF THE ERROR FUNCTION FOR CHEbYSHEV APPROXIMATION 0N A DISK exp