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iTRAQ-based proteomics reveals the mechanism of action of Yinlai decoction in treating pneumonia in mice consuming a high-calorie diet

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Journal of Traditional Chinese Medical Sciences 2024年第1期11卷 21-32页

作者： Qianqian Li Tiegang Liu Chen Bai Xueyan ma Hui Liu Zi’an Zheng Yuxiang Wan He Yu yuling ma Xiaohong Gu School of Traditional Chinese Medicine Beijing University of Chinese MedicineBeijing 102488China Institute of Chinese Medicine Epidemic Disease Beijing University of Chinese MedicineBeijing 102488China School of Traditional Chinese Medicine Guizhou University of Traditional Chinese MedicineGuiyang 550025China Department of Acupuncture and Mini-invasive Oncology Beijing University of Chinese Medicine Third Affiliated HospitalBeijing 100029China OCMRC Center University of OxfordOxford OX13PTUnited Kingdom

Objective:To uncover the underlying mechanisms of action of the Yinlai decoction on high-calorie dietinduced pneumonia through proteomics analysis.Methods:Based on the Gene Expression Omnibus(GEO)database,lung tissue samples from normal and high-fat diet(HFD)fed mice in the GSE16377 dataset were selected as test cohorts to identify differentially expressed genes and conduct bioinformatics analyses.In the animal experiments,mice were randomly divided into the control(N),high-calorie diet pneumonia(M),and Yinlai decoction treatment(Y)groups.Mice in the M group received high-calorie feed and a 0.5 mg/mL lipopolysaccharide solution spray for 30 min for 3 d.The mice in the Y group were intragastrically administered 2 mL/10 g Yinlai decoction twice daily for 3 d.Pathological evaluation of the lung tissue was performed.Differentially expressed proteins(DEPs)in the lung tissue were identified using quantitative proteomics and bioinformatics analyses.The drug-target relationships between Yinlai decoction and core DEPs in the lung tissue were verified using AutoDock Vina and Molecular Graphics Laboratory(MGL)Tools.DEPs were verified by western blot.Results:GEO data mining showed that an HFD altered oxidative phosphorylation in mouse lung tissue.The Yinlai decoction alleviated pathological damage to lung tissue and pneumonia in mice that were fed a high-calorie diet.A total of 47 DEPs were identified between the Y and M groups.Enrichment analysis revealed their association with energy metabolism pathways such as the tricarboxylic acid cycle(TCA)and oxidative phosphorylation.The protein-protein interaction network revealed that Atp5a1,Pdha1,and Sdha were the target proteins mediating the therapeutic effects of Yinlai decoction.Molecular docking results suggested that the mechanism of the therapeutic effect of Yinlai decoction involves the binding of brassinolide,praeruptorin B,chrysoeriol,and other components in Yinlai decoction to Atp5a1.Conclusion:The Yinlai decoction alleviated lung tissue damage and pneumonia in mice tha

关键词： Yinlai decoction High-calorie diet Pneumonia iTRAQ proteomics Energy metabolism

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面向标签噪声的联合训练框架

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中国科学：信息科学 2024年第1期54卷 144-158页

作者：魏琦孙皓亮马玉玲尹义龙山东大学软件学院济南250101 山东建筑大学计算机科学与技术学院济南250101

当前面向标签噪声的鲁棒性学习通常依赖样本选择和标签修正两种策略,但是这两类方法均存在缺陷.基于样本选择的方法忽略了被过滤掉的样本中的有效信息,进而降低了模型的性能.基于标签修正的方法常使用自标签技术而引起模型的错误积累问... 详细信息

当前面向标签噪声的鲁棒性学习通常依赖样本选择和标签修正两种策略,但是这两类方法均存在缺陷.基于样本选择的方法忽略了被过滤掉的样本中的有效信息,进而降低了模型的性能.基于标签修正的方法常使用自标签技术而引起模型的错误积累问题.对此,本文提出了一个集成样本选择、标签修正的联合训练框架.针对样本选择模块,本文设计了一种新的选择标准,通过在线选择的方法对所挑选的样本集合进行更新.相较于现有选择标准,本文提出的标准可保留更多边界样本,提升了模型对决策边界的学习性能,增强了模型的泛化性能.针对标签修正模块,本文提出了一种联合标签修正策略.相比于传统的自标签修正技术,该模块通过联合特征空间视角,对噪声样本进行多视角的标签修正,解决了传统自标签技术的错误累积问题.此外,本文引入对比学习正则化项,提升了标签修正效果和模型表征学习能力.本文方法在4个测试基准上取得了当前最好分类效果,验证了所提训练框架的有效性.

关键词：标签噪声学习样本选择标签修正对比学习

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Multi-task MIML learning for pre-course student performance prediction

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Frontiers of Computer Science 2020年第5期14卷 113-121页

作者： yuling ma Chaoran Cui Jun Yu Jie Guo Gongping Yang Yilong Yin School of Software Shandong UniversityJinan250100China School of Information Engineering Shandong Yingcai CollegeJinan250104China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan250014China

In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning activities.However,given the large size of a course’s students at universities,it has become impossible for teachers to keep track of the performance of individual students.In this circumstance,an academic early warning system is desirable,which automatically detects students with difficulties in learning(i.e.,at-risk students)prior to a course starting.However,previous studies are not well suited to this purpose for two reasons:1)they have mainly concentrated on e-learning platforms,e.g.,massive open online courses(MOOCs),and relied on the data about students’online activities,which is hardly accessed in traditional teaching scenarios;and 2)they have only made performance prediction when a course is in progress or even close to the end.In this paper,for traditional classroom-teaching scenarios,we investigate the task of pre-course student performance prediction,which refers to detecting at-risk students for each course before its commencement.To better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)problem.Besides,given the problem of data scarcity,we propose a novel multi-task learning method,i.e.,MIML-Circle,to predict the performance of students from different specialties in a unified framework.Extensive experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods.

关键词： educational data mining academic early warning system student performance prediction multi-instance multi-label learning multi-task learning

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Pre-course student performance prediction with multi-instance multi-label learning

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Science China(Information Sciences) 2019年第2期62卷 200-205页

作者： yuling ma Chaoran CUI Xiushan NIE Gongping YANG Kashif SHAHEED Yilong YIN Software College Shandong University School of Information Engineering Shandong Yingcai College School of Computer Science and Technology Shandong University of Finance and Economics

Dear editor,Studying courses is one of the most basic and important tasks for college students. For each new course, the initial period of learning is crucial for students, and seriously influences subsequent learning activities. However, given a large number of classes in universities, it has become impossible for teachers to keep track of the individual performance of each student. In these circumstances, it is desirable to predict each student’s performance on a certain course prior to its commencement.

关键词： KNN DT Pre-course student performance prediction with multi-instance multi-label learning SVM Figure BCS

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原型对齐和域感知的零样本哈希

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计算机工程 2024年第5期50卷 260-271页

作者：董峰王永欣马玉玲王奎奎山东建筑大学计算机科学与技术学院山东济南250101

为了实现对未见类别图像的有效检索,零样本哈希(ZSH)方法通常将类别属性中的监督知识从已见类转移到未见类。然而,获取类别属性需要花费额外的计算资源,并且视觉特征和类别属性之间存在跨模态的异构鸿沟。此外,现有方法忽视了强偏差问题... 详细信息

为了实现对未见类别图像的有效检索,零样本哈希(ZSH)方法通常将类别属性中的监督知识从已见类转移到未见类。然而,获取类别属性需要花费额外的计算资源,并且视觉特征和类别属性之间存在跨模态的异构鸿沟。此外,现有方法忽视了强偏差问题,导致模型错误地将已见类样本识别为未见类,从而降低了检索精度。与此同时,ZSH在保持哈希码和原始数据语义一致性以及实现哈希码的离散优化等方面也面临着挑战。为此,提出一种原型对齐和域感知的ZSH方法,其不依赖类别属性等特殊监督知识,能够节省注释属性的花销同时避免跨模态异构鸿沟的影响。首先计算各类样本在海明空间中的原型,然后通过对齐哈希码和类原型来学习语义一致的哈希码。为了避免松弛策略造成的量化误差,提出一种离散优化算法来求解哈希码的离散约束,并且实现线性的计算复杂度。同时,设计一个域感知策略用于分离源域和目标域样本,以缓解强偏差问题的影响。在a PY、AWA2和Image Net数据集上的实验结果表明,该方法的检索精度相较对比方法中的最优值分别提升了2.6、9.4和14.9个百分点,训练时间也远低于大部分对比方法。

关键词：哈希图像检索零样本学习原型对齐域感知

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Facile Synthesis of Mo2C Nanocrystals Embedded in Nanopo- rous Carbon Network for Efficient Hydrogen Evolution

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Chinese Journal of Chemistry 2017年第6期35卷 911-917页

作者： Yuyun Chen Yun Zhang yuling ma Tang Tang Zhihui Dai Jinsong Hu Lijun Wan Jiangsu Key Laboratory of Biofunctional materials School of Chemistry and Materials Science Nanjing Normal University Nanjing Jiangsu 210023 China CAS Key Laboratory of Molecular Nanostructure and Nanotechnology Institute of Chemistry Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Exploring facile and easily-scalable methods for synthesizing earth-abundant, cost-effective and efficient hy- drogen evolution reaction （HER） electrocatalysts is essential for the mass production of hydrogen as a clean and sustainable energy carrier. We report here a simple strategy to produce Mo2C nanocrystals embedded in carbon network （Mo2C@C） by the direct pyrolysis of ammonium molybdate and polyvinylpyrrolidone （PVP）. It is found that PVP can be effectively used as a single source to form carbides and carbon network. The long polymer chain and coordinating capability with transition metal of PVP make it possible to form connected porous carbon network and well-dispersed MozC nanocrystals in several nanometers. The carbonization of PVP not only effectively in-situ prevents the aggregation of Mo2C nanocrystals during their formation, but also provides conductive porous matrix. As a result, the Mo2C@C composite exhibits the superior electrocatalytic performance for HER, which can be as- cribed to the large number of active sites from plenty of small MORC nanocrystals and the efficient mass and elec- tron transport network from carbon matrix. This strategy may inspire the exploration of cost-effective functional polymer as single source for both carbon precursor and nanostructure-directed reagent to mass-produce well-defined metal carbides nanostructures embedded in porous carbon network for energy applications.

关键词： molybdenum carbides electrocatalysis electrolysis PVP HER

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无人机辅助边缘计算安全通信能力最大化方案

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计算机科学 2024年第S1期51卷 949-955页

作者：薛建彬豆俊王涛马玉玲兰州理工大学计算机与通信学院兰州730050

针对无人机辅助移动边缘计算系统下用户信息容易泄露的问题,设计了一种基于非正交多址接入技术(Non-orthogonal Multiple Access,NOma)的无人机辅助边缘计算系统的安全通信方案。在保证每个地面用户的最小安全计算要求下,通过联合优化... 详细信息

针对无人机辅助移动边缘计算系统下用户信息容易泄露的问题,设计了一种基于非正交多址接入技术(Non-orthogonal Multiple Access,NOma)的无人机辅助边缘计算系统的安全通信方案。在保证每个地面用户的最小安全计算要求下,通过联合优化信道系数、发射功率、中央处理单元计算频率、本地计算和无人机轨迹来最大化系统的平均安全计算能力。由于窃听者位置的不确定性、多变量的耦合以及问题的非凸性,利用逐次凸逼近和块坐标下降方法来解决该问题。仿真结果表明,与基准方案相比,所提方案在系统安全计算性能方面优于基准方案。

关键词：移动边缘计算非正交多址无人机物理层安全通信安全

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短视频场景分类方法综述

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山东大学学报（工学版） 2024年第3期54卷 1-11页

作者：聂秀山巩蕊董飞郭杰马玉玲山东建筑大学计算机科学与技术学院山东济南250101 山东师范大学新闻与传播学院山东济南250358

传统的视频场景分类方法习惯于从视觉模态中提取表现图像场景的特征,结合支持向量机等有监督学习方法,实现对某些类别的场景分类。随着各种短视频在各大平台迅速涌现,基于短视频特性的场景特征表示越来越受到研究者们的关注。由于短视... 详细信息

传统的视频场景分类方法习惯于从视觉模态中提取表现图像场景的特征,结合支持向量机等有监督学习方法,实现对某些类别的场景分类。随着各种短视频在各大平台迅速涌现,基于短视频特性的场景特征表示越来越受到研究者们的关注。由于短视频数据具有噪声、数据缺失、各模态语义强度不一致等问题,导致传统的视频场景表征方法无法学习具有丰富语义的短视频场景表征。近年来,部分短视频场景分类的研究考虑上述挑战,并提出相应的方法。本研究综述短视频场景分类的研究现状,介绍短视频场景特征表示和分类方法,对不同数据集上的场景分类方法进行分析。针对现有方法存在的问题,分析未来短视频场景分类中需要解决的挑战性问题。

关键词：视频场景特征表示短视频场景分类多模态融合深度学习

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Traditional Chinese medicine in COVID-19

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Acta Pharmaceutica Sinica B 2021年第11期11卷 3337-3363页

作者： Ming Lyu Guanwei Fan Guangxu Xiao Taiyi Wang Dong Xu Jie Gao Shaoqin Ge Qingling Li yuling ma Han Zhang Jigang Wang Yuanlu Cui Junhua Zhang Yan Zhu Boli Zhang State Key Laboratory of Component-based Chinese Medicine Tianjin University of Traditional Chinese MedicineTianjin 301617China Artemisinin Research Center Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijing 100700China National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion First Teaching Hospital of Tianjin University of Traditional Chinese MedicineTianjin 300193China Oxford Chinese Medicine Research Centre University of OxfordOxford OX13PTUK College of Traditional Chinese Medicine Hebei UniversityBaoding 071002China Institute of Basic Medicine and Cancer the Cancer Hospital of the University of Chinese Academy of Sciences(Zhejiang Cancer Hospital)Chinese Academy of SciencesHangzhou 310022China

COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2(SARSCo V-2)has spread across the globe,posing an enormous threat to public health and safety.Traditional Chinese medicine(TCM),in combination with Western medicine(WM),has made important and lasting contributions in the battle against COVID-19.In this review,updated clinical effects and potential mechanisms of TCM,presented in newly recognized three distinct phases of the disease,are summarized and discussed.By integrating the available clinical and preclinical evidence,the efficacies and underlying mechanisms of TCM on COVID-19,including the highly recommended three Chinese patent medicines and three Chinese medicine formulas,are described in a panorama.We hope that this comprehensive review not only provides a reference for health care professionals and the public to recognize the significant contributions of TCM for COVID-19,but also serves as an evidence-based in-depth summary and analysis to facilitate understanding the true scientific value of TCM.

关键词： COVID-19 SARS-CoV-2 Traditional Chinese medicine Clinical evidence Potential mechanism Viral infection Cytokine storm Multiple organ dysfunction

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基于ConvLSTM-CNN预测太平洋长鳍金枪鱼时空分布趋势

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海洋通报 2024年第2期43卷 174-187页

作者：杜艳玲马玉玲汪金涛陈珂林泓羽陈刚上海海洋大学信息学院上海201306 上海海洋大学海洋科学学院上海201306 自然资源部东海预报减灾中心上海200136 国家海洋信息中心天津300171

海洋渔场的变动由空间与环境因子共同驱动,渔场时空演变信息的精准预测是海洋捕捞的关键。本研究利用1995-2018年太平洋海域长鳍金枪鱼(Thunnus alalunga)的渔业生产统计数据,结合同期海洋环境数据包括海表面温度(Sea Surface Temperatu... 详细信息

海洋渔场的变动由空间与环境因子共同驱动,渔场时空演变信息的精准预测是海洋捕捞的关键。本研究利用1995-2018年太平洋海域长鳍金枪鱼(Thunnus alalunga)的渔业生产统计数据,结合同期海洋环境数据包括海表面温度(Sea Surface Temperature,SST)、海表面盐度(Sea Surface Salinity,SSS)、初级生产力(Primary Productivity,PP)和溶解氧浓度(Dissolved Oxygen Concentration,DO),提出了一种融合卷积长短期记忆网络(Convolutional Long Short-Term Memory Networks,ConvLSTM)和卷积神经网络(Convolutional Neural Networks,CNN)的渔场时空分布预测模型。该模型引入特征提取模块,对时空因子进行编码,提取时空特征信息,同时采用CNN提取海洋环境变量的抽象特征,采用ConvLSTM提取渔业数据的高层时空关联信息,最后融合多种特征对渔场时空演变趋势进行预测。结果表明,模型的均方根误差为0.1036,较随机森林、BP神经网络和长短期记忆网络(Long Short Term Memory,LSTM)等传统渔场预报模型的预测误差降低15%~40%,预测的高产渔区与实际作业的高渔获量区匹配度为89%。该研究构建的渔场时空预测模型能够准确地预测出太平洋长鳍金枪鱼的时空分布,为太平洋长鳍金枪鱼的延绳钓渔业提供科学参考依据。

关键词：长鳍金枪鱼时空分布融合卷积长短期记忆网络卷积神经网络太平洋

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