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China CDC weekly 2019年第2期1卷 17-18页

作者： zijian feng China CDC Weekly Chinese Center for Disease Control and Prevention

Public health is applying science to help people live safer,more productive,and healthier *** evidencebased policy recommendations on the use of vaccines to prevent,control,and even eliminate vaccine-preventable infec... 详细信息

关键词： Action policy Message

Protection of Omicron Sub-Lineage Infection Against Reinfection with Another Omicron Sub-Lineage:Systematic Review,Meta-Analysis,and Meta-Regression-Worldwide,2022-2023

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China CDC weekly 2024年第26期6卷 624-628,I0001-I0009页

作者： Xu Guo Zuyao Cheng Junhong Li Yudan Song Hui Zheng Yamin Wang Chao Ma zijian feng National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Disease National Immunization ProgramChinese Center for Disease Control and PreventionBeijingChina Chinese Preventive Medicine Association BeijingChina

Summary What is already known about this topic?Both the decline in immunity over time and the evolution of the virus play a role in the level of protection offered by a prior *** is added by this report?Point estimates indicated variations in protection levels based on the initial infecting variant and the reinfecting *** was a consistent correlation between real-world protection,antigenic distance,and humoral immunity ***,shorter antigenic distances and higher humoral immunity levels corresponded to enhanced real-world *** are the implications for public health practice?Our findings suggest that virological and immunological studies could help identify and assess the epidemic risk posed by new variants before they become *** incorporation of the latest variants into the antigen components of the coronavirus disease 2019(COVID-19)vaccines can significantly contribute to effective epidemic prevention and control measures.

关键词： immunity prevention shorter

A Lambda Layer-Based Convolutional Sequence Embedding Model for Click-Through Rate Prediction

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Wuhan University Journal of Natural Sciences 2024年第3期29卷 198-208页

作者： ZHOU Liliang YUAN Shili feng zijian DAI Guilan ZHOU Guofu Tenth Research Institute China Electronics Technology Group CorporationChengdu 610000SichuanChina Department of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023JiangsuChina Research Institution of Information Technology Tsinghua UniversityBeijing 100084China School of Computer Science Wuhan UniversityWuhan 430072HubeiChina

In the era of intelligent economy, the click-through rate(CTR) prediction system can evaluate massive service information based on user historical information, and screen out the products that are most likely to be favored by users, thus realizing customized push of information and achieve the ultimate goal of improving economic benefits. Sequence modeling is one of the main research directions of CTR prediction models based on deep learning. The user's general interest hidden in the entire click history and the short-term interest hidden in the recent click behaviors have different influences on the CTR prediction results, which are highly important. In terms of capturing the user's general interest, existing models paid more attention to the relationships between item embedding vectors(point-level), while ignoring the relationships between elements in item embedding vectors(union-level). The Lambda layer-based Convolutional Sequence Embedding(LCSE) model proposed in this paper uses the Lambda layer to capture features from click history through weight distribution, and uses horizontal and vertical filters on this basis to learn the user's general preferences from union-level and point-level. In addition, we also incorporate the user's short-term preferences captured by the embedding-based convolutional model to further improve the prediction results. The AUC(Area Under Curve) values of the LCSE model on the datasets Electronic, Movie & TV and MovieLens are 0.870 7, 0.903 6 and 0.946 7, improving 0.45%, 0.36% and 0.07% over the Caser model, proving the effectiveness of our proposed model.

关键词： click-through rate prediction deep learning attention mechanism convolutional neural network

The COVID-19 Vaccines Evaluation Program:Implementation,Management,and Experiences,2021-2023

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China CDC weekly 2024年第26期6卷 642-648页

作者： Tingting Yan Hui Zheng Mingshuang Li Chao Ma Xuanyi Wang Xiaoqi Wang Zhenjun Li Yuansheng Chen Wenshang Hu Lance Rodewald Zhijie An Zundong Yin zijian feng National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases(NITFID).National Immunization Program Chinese Center for Disease Control and PreventionBeijingChina Institutes of Biomedical Sciences Fudan UniversityShanghaiChina Office of International Cooperation Chinese Center for Disease Control and PreventionBeijingChina Office of Science and Technology Chinese Center for Disease Control and PreventionBeijingChina Office of Finance Chinese Center for Disease Control and PreventionBeijingChina Chinese Preventive Medicine Association BeijingChina

In 2021,China’s domestically produced coronavirus disease 2019(COVID-19)vaccines received approval from regulatory bodies and were administered *** to a low number of infections within China during that period,it became imperative to evaluate the vaccines’real-world effectiveness through international *** facilitate this,China CDC launched the COVID-19 Vaccines Evaluation Program(COVEP).This program formed research collaboration agreements with health institutes across five World Health Organization regions,addressing key questions about vaccine performance through ten cooperative *** findings from COVEP projects reinforced confidence,both domestically and globally,in the effectiveness of the vaccines produced in ***,the outcomes observed internationally were frequently mirrored by later studies conducted within *** thus pioneered a novel approach for fostering cross-national research collaborations,addressing significant public health issues and exemplifying a framework for international *** approach is in line with the strategic objectives and other development efforts of China CDC’s national disease control and prevention initiatives.

关键词： approval COV prevention

Caliper-based precise positioning of the target(CALIPPOT)for transcranial magnetic stimulation without neuronavigation system

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General Psychiatry 2024年第2期37卷 311-314页

作者： Yunsong Hu Rong Zeng Juan Yue Qiu Ge Hongxiao Wang zijian feng Jue Wang Yufeng Zang Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiangChina Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments HangzhouZhejiangChina Institutes of Psychological Sciences Hangzhou Normal UniversityHangzhouZhejiangChina Transcranial Magnetic Stimulation Center Deqing Hospital of Hangzhou Normal UniversityDeqingZhejiangChina Institute of Sports Medicine and Health Chengdu Sport UniversityChengduSichuanChina Department of Neurology The Affiliated Hospital of Hangzhou Normal UniversityHangzhouZhejiangChina

To the editor:Transcranial magnetic stimulation(TMS)is a non-invasive brain modulation *** important usage of TMS is the transient interruption of cognitive brain function(also named virtual lesion)for investigating precisely where and when a specific cortical region contributes to a specific cognitive function.1 A more important usage of TMS is the treatment of brain disorders by repetitive TMS(rTMS).

关键词： stimulation technique. precise

大国竞争语境下美国战略传播体系:演进特征、运作模式及经验思考

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情报杂志 2024年第8期43卷 18-24,101页

作者：冯子剑陈曦朱启超国防科技大学外国语学院南京210039 国防科技大学国防科技战略研究智库长沙410003

[研究目的]美国的战略传播体系是其国家战略的重要支撑,也是其在所谓“大国竞争”时代对其他国家进行意识形态渗透与舆论攻击的重要手段。该文拟通过对美国战略传播体系演进特征与运作模式的系统研究,在指出其为霸权护持目的服务的同时... 详细信息

[研究目的]美国的战略传播体系是其国家战略的重要支撑,也是其在所谓“大国竞争”时代对其他国家进行意识形态渗透与舆论攻击的重要手段。该文拟通过对美国战略传播体系演进特征与运作模式的系统研究,在指出其为霸权护持目的服务的同时,总结其优势特点,为中国国际传播能力建设提供相关借鉴。[研究方法]采用实证研究法,通过系统梳理新世纪以来美国颁布的战略传播文件分析其演进特征,并结合美国开展战略传播行动的具体案例剖析其在大国竞争语境下战略传播体系的具体运作模式,最后通过分析其开展战略传播行动的效能总结相关优势,提出对我国际传播体系建设的有关思考。[研究结论]从概念提出到体系成熟,美国的战略传播体系在演进过程中逐步实现了从军方主导到军民一体,从单一部门负责到跨职能部门整合运转,从单一目标信息投送到全领域舆论攻势的发展,形成了全政府协同、政府——社会联动、盟友追随配合的运作模式,并在政府机制协调、国家舆论力量统筹等方面具有明显优势,值得我们深入研究。

关键词：战略传播大国竞争国际传播舆论战略传播体系美国

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基于多模型集成算法的光纤陀螺温度补偿及实现

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半导体光电 2024年第1期45卷 96-100页

作者：仇海涛王开石海洋冯子健北京信息科技大学高动态导航技术北京市重点实验室北京100101 北京航天时代光电科技有限公司北京100094

为降低光纤陀螺因温度效应产生的零偏漂移,以基于最小二乘法的多项式补偿模型和经遗传算法优化后的BP神经网络模型(GA-BP)为基学习器,通过集成学习算法建立了光纤陀螺的温度补偿模型,并对补偿后的光纤陀螺进行在线温度补偿实验。实验结... 详细信息

为降低光纤陀螺因温度效应产生的零偏漂移,以基于最小二乘法的多项式补偿模型和经遗传算法优化后的BP神经网络模型(GA-BP)为基学习器,通过集成学习算法建立了光纤陀螺的温度补偿模型,并对补偿后的光纤陀螺进行在线温度补偿实验。实验结果表明,该模型在-40~+60℃温变环境下将光纤陀螺的全过程零偏漂移降低了85%以上,且补偿后的启动段零偏输出均值更接近零位。

关键词：光纤陀螺温度补偿遗传算法 BP神经网络集成学习

基于分裂注意力机制的DNA转录因子结合位点预测

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软件导刊 2024年第2期23卷 32-39页

作者：姜博文冯子健黄伟鸿浙江理工大学计算机科学与技术学院浙江理工大学信息科学与工程学院浙江杭州310018

准确识别DNA序列中的转录因子结合位点对于基因表达解析和药物设计等具有重要意义。基于深度学习的各种预测方法已被应用于转录因子结合位点任务中,但预测性能尚有提升空间。为此,提出一种新的深度学习方法ResNest-TFBS,用于预测690个Ch... 详细信息

准确识别DNA序列中的转录因子结合位点对于基因表达解析和药物设计等具有重要意义。基于深度学习的各种预测方法已被应用于转录因子结合位点任务中,但预测性能尚有提升空间。为此,提出一种新的深度学习方法ResNest-TFBS,用于预测690个ChIP-seq数据集上的转录因子结合位点。该方法首先在序列One-hot编码的基础上通过引入DNA的分子动力学特征与静电势能特征提取DNA的空间结构特性;其次利用分裂注意力机制与残差结构组成ResNest模型进行训练,从而将通道注意力机制应用于不同通道分支,以捕获其在全局数据集上学习到的特征间交互与多通道表示;最后将上述先验知识迁移至690个ChIP-seq数据集上,并进行广泛测试。实验结果表明,ResNest-TFBS性能优异,平均AUC为0.929。此外,通过SHAP工具验证不同特征在该任务中的贡献程度,证实所引入的特征为转录因子结合位点预测提供了更具价值的生物学线索。

关键词： DNA 转录因子结合位点深度学习迁移学习分裂注意力机制

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基于动态图卷积与迁移学习的蛋白质质量评估

软件导刊 2024年第1期23卷 69-74页

作者：冯子健黄伟鸿姜博文浙江理工大学信息科学与工程学院浙江杭州310018

蛋白质质量评估是指对计算手段预测的蛋白质模型进行评分,从而挑选更接近天然结构的优秀模型。图结构可直观表示蛋白质模型,因此近年来图卷积神经网络(GCN)在质量评估领域得到了广泛应用,然而图节点的固定邻接关系限制了GCN对节点特征... 详细信息

蛋白质质量评估是指对计算手段预测的蛋白质模型进行评分,从而挑选更接近天然结构的优秀模型。图结构可直观表示蛋白质模型,因此近年来图卷积神经网络(GCN)在质量评估领域得到了广泛应用,然而图节点的固定邻接关系限制了GCN对节点特征深入挖掘的能力。为此,提出一种动态图卷积的质量评估方法 DGCQA,预测蛋白质模型的全局质量分数。该方法根据节点的特征距离动态获取邻域,结合多尺度卷积模块提取残基对特征,以增强网络的表达能力。此外,基于迁移学习思想,引入蛋白质预训练模型ESM-1b编码特征,使DGCQA在多个指标上的表现有所提升。实验表明,所提方法相较于CASP13数据集的12种质量评估方法,具有很强的竞争力。

关键词：蛋白质模型质量评估动态图卷积迁移学习 ESM-1b

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Combining Residual Attention Mechanisms and Generative Adversarial Networks for Hippocampus Segmentation

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Tsinghua Science and Technology 2022年第1期27卷 68-78页

作者： Hongxia Deng Yuefang Zhang Ran Li Chunxiang Hu zijian feng Haifang Li the School of Information and Computer Taiyuan University of TechnologyTaiyuan 030600China

This research discussed a deep learning method based on an improved generative adversarial network to segment the *** convolutional configurations were proposed to capture information obtained by a segmentation *** addition,a generative adversarial network based on Pixel2Pixel was *** generator was a codec structure combining a residual network and an attention mechanism to capture detailed *** discriminator used a convolutional neural network to discriminate the segmentation results of the generated model and that of the *** the continuously transmitted losses of the generator and discriminator,the generator reached the optimal state of hippocampus segmentation.T1-weighted magnetic resonance imaging scans and related hippocampus labels of 130 healthy subjects from the Alzheimer’s disease Neuroimaging Initiative dataset were used as training and test data;similarity coefficient,sensitivity,and positive predictive value were used as evaluation *** showed that the network model could achieve an efficient automatic segmentation of the hippocampus and thus has practical relevance for the correct diagnosis of diseases,such as Alzheimer’s disease.

关键词： magnetic resonance imaging generative adversarial network residual network attention mechanism