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运用在册数据观察美国预防服务工作组2009年推荐规范发布前后乳腺癌X线筛查的变化趋势

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国际医学放射学杂志 2014年第2期 169-170页

作者： b.l.sprague K.C.bolton J.l.Mace S.D.Herschorn T.A.James P.M.Vacek 刘靓 Department of Surgery University of Vermont1 S Prospect St Burlington VT 05401

目的评价佛蒙特州是否遵循美国预防服务工作组(USPSTF)2009年乳腺癌X线筛查指南对X线筛查的选用做出相应的改变。材料与方法本研究为回顾性研究,符合

关键词：筛查乳腺癌乳腺肿瘤预防服务工作组美国美利坚合众国北美洲

比较数字化乳腺摄影联合断层融合筛查女性致密乳腺的效果

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国际医学放射学杂志 2015年第3期 270-271页

作者： C.I.lee M.Cevik O.Alagoz b.l.sprague A.N.Tosteson D.l.Miglioretti 陶志刚 Dept of Radiology Univ of Washington825 Eastlake Ave E G3-200 Seattle WA 98109-1023

目的对女性致密型乳腺,两年一次数字化乳腺摄影结合断层融合筛查与两年一次单独数字化乳腺摄影筛查的效果比较。方法前瞻性地从联邦纳税人中筛选出具有一

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现代乳腺数字化X线摄影的国家诊断标准:乳腺癌监测协会最新进展

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国际医学放射学杂志 2017年第3期 343-344页

作者： b.l.sprague R.F.Arao D.l.Miglioretti l.M.Henderson D.S.buist T.Onega 谭红娜 Departments of Surgery Radiologyand BiochemistryUniversity of Vermont Cancer CenterUniversity of Vermont

摘要目的应用乳腺癌监测协会(bCSC)最新数据建立当代乳腺数字化X线诊断标准。材料与方法本研究获得机构伦理委员会许可,通过主动或被动征得同意或获得登记参与者、链接数据并进行分析的知情同意豁免权。数据来源于6个bCSC登记中心的418... 详细信息

摘要目的应用乳腺癌监测协会(bCSC)最新数据建立当代乳腺数字化X线诊断标准。材料与方法本研究获得机构伦理委员会许可,通过主动或被动征得同意或获得登记参与者、链接数据并进行分析的知情同意豁免权。数据来源于6个bCSC登记中心的418名放射科医师和92台乳腺X线摄影设备。前瞻性收集并评价接受诊断性数字化X线摄影检查的妇女的乳腺X线表现,并与国家癌症登记中心的诊断结果相链接。该项研究纳入自2007年—2013年间共265 360名妇女接受的401 548次乳腺数字化X线检查。结果整体

关键词：乳腺癌乳腺肿瘤浸润性癌阳性预测值摄影美术穿刺活检

现代乳腺数字化X线摄影的国家筛查标准:乳腺癌监测协会最新进展

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国际医学放射学杂志 2017年第3期 343-343页

作者： C.D.lehman R.F.Arao b.l.sprague J.M.lee D.S.buist K.Kerlikowske 谭红娜 Department of Radiology Massachusetts General HospitalHarvard Medical School

摘要目的建立现代乳腺数字化X线筛查标准,并在美国社区实践中评价其性能。材料与方法本研究获得机构伦理委员会批准并符合HIPAA。本研究定量评价数字化乳腺X线筛查的性能,数据来源于6个乳腺癌监测协会(bCSC)登记中心的359名放射科医生... 详细信息

摘要目的建立现代乳腺数字化X线筛查标准,并在美国社区实践中评价其性能。材料与方法本研究获得机构伦理委员会批准并符合HIPAA。本研究定量评价数字化乳腺X线筛查的性能,数据来源于6个乳腺癌监测协会(bCSC)登记中心的359名放射科医生的判读结果和95台乳腺X线摄影设备。本研究纳入自2007年—2013年间共792 808名妇女的1 682 504次乳腺数字化X线筛查。绩效评估参照美国放射学会第5版乳腺影像报告和数据系统(bI-RADS),并与bCSC已发布的国家基准、国家乳腺影像数据库和专家意见相比较。基准来源于放射科医师的性能指标分布,分别用第

关键词：乳腺癌乳腺肿瘤筛查标准放射科医师

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

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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

Multiple sclerosis is at a checkpoint: advancing the program

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Neural Regeneration Research 2025年第3期20卷 811-812页

作者： brandon C.Smith Jessica l.Williams Department of Neurosciences Lerner Research InstituteCleveland ClinicClevelandOHUSA Department of biological Geologicaland Environmental SciencesCleveland State UniversityClevelandOHUSA

Multiple sclerosis(MS) is a chronic inflammatory and demyelinating disease of the central nervous system(CNS). Patients with MS experience sensory and motor function loss due to myelin and/or axon damage perpetuated b... 详细信息

关键词： Hauser sclerosis damage

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