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DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

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Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu Huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been *** optimal performance,they require the fragment ion spectra of targeted peptides as prior *** this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue *** build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on *** then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer *** extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)***,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was *** a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control *** expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by *** data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker *** is freely accessible at https://***/page/***?id=IPX0001400000.

关键词： Data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

Corrigendum: Ethanol-controlled peroxidation in liquid-anode discharges (2019 J. Phys. D: Appl. Phys. 52 425205)

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Journal of Physics D: Applied Physics 2021年第22期54卷

作者： Qiang Chen Jiao Lin Xinyi He Hailong Hu Junshuai Li Qing Xiong Bingyan Chen Zhengyong Song Hai Liu Qing Huo Liu Kostya (Ken) Ostrikov Institute of Electromagnetics and Acoustics Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Department of Electronic Science Xiamen University Xiamen 361005 People's Republic of China College of Physics and Information Engineering Fuzhou University Fuzhou 350116 People's Republic of China Key Laboratory of Special Function Materials and Structure Design of the Ministry of Education and School of Physical Science and Technology Lanzhou University Lanzhou 730000 People's Republic of China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing 400044 People's Republic of China Department of Mathematics and Physics Hohai University Changzhou 213022 People's Republic of China School of Civil Engineering Guangzhou University Guangzhou 510006 People's Republic of China Department of Electrical and Computer Engineering Duke University Durham NC 27708 United States of America Institute for Future Environments and School of Chemistry Physics and Mechanical Engineering Queensland University of Technology Brisbane QLD 4000 Australia CSIRO-QUT Joint Sustainable Processes and Devices Laboratory Commonwealth Scientific and Industrial Research Organization PO Box 218 Lindfield NSW 2070 Australia

Structural transition in interdependent networks with regular interconnections

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arXiv 2018年

作者： Wang, Xiangrong Kooij, Robert E. Moreno, Yamir van Mieghem, Piet Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands iTrust Centre for Research in Cyber Security Singapore University of Technology and Design Singapore Singapore University of Zaragoza Zaragoza50009 Spain Department of Theoretical Physics University of Zaragoza Zaragoza50009 Spain ISI Foundation Turin Italy

Networks are often made up of several layers that exhibit diverse degrees of interdependencies. A multilayer interdependent network consists of a set of graphs G that are interconnected through a weighted interconnection matrix B, where the weight of each inter-graph link is a non-negative real number p. Various dynamical processes, such as synchronization, cascading failures in power grids, and diffusion processes, are described by the Laplacian matrix Q characterizing the whole system. For the case in which the multilayer graph is a multiplex, where the number of nodes in each layer is the same and the interconnection matrix B = pI, being I the identity matrix, it has been shown that there exists a structural transition at some critical coupling, p∗. This transition is such that dynamical processes are separated into two regimes: if p > p∗, the network acts as a whole;whereas when p ∗, the network operates as if the graphs encoding the layers were isolated. In this paper, we extend and generalize the structural transition threshold p∗ to a regular interconnection matrix B (constant row and column sum). Specifically, we provide upper and lower bounds for the transition threshold p∗ in interdependent networks with a regular interconnection matrix B and derive the exact transition threshold for special scenarios using the formalism of quotient graphs. Additionally, we discuss the physical meaning of the transition threshold p∗ in terms of the minimum cut and show, through a counter-example, that the structural transition does not always exist. Our results are one step forward on the characterization of more realistic multilayer networks and might be relevant for systems that deviate from the topological constrains imposed by multiplex networks. Copyright © 2018, The Authors. All rights reserved.

关键词： Multilayers

On Algebraic Immunity of Trace Inverse Functions on Finite Fields of Characteristic Two

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Journal of Systems Science & Complexity 2016年第1期29卷 272-288页

作者： FENG Xiutao GONG Guang Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of Sciences State Key Laboratory of Information Security(Institute of Information Engineering Chinese Academy of Sciences) Department of Electrical and Computer Engineering University of Waterloo

The trace inverse functions Tr(λx^(-1)) over the finite field F_(2~n) are a class of very important Boolean functions and are used in many stream ciphers such as SFINKS,RAKAPOSHI,the simple counter stream cipher(SCSC) presented by Si W and Ding C(2012),*** order to evaluate the security of those ciphers in resistance to(fast) algebraic attacks,the authors need to characterize algebraic properties of Tr(λx^(-1)).However,currently only some bounds on algebraic immunity of Tr(λx^(-1)) are given in the public literature,for example,the NGG upper bound and the Bayev lower bound,*** paper gives the exact value of the algebraic immunity of Tr(λx^(-1)) over F_(2~n),that is,AI(Tr(λx^(-1))) =[2n^(1/2)]- 2,where n ≥ 2,A ∈ F_(2~n) and λ≠ 0,which shows that Dalai's conjecture on the algebraic immunity of Tr(λx^(-1)) is *** is more,the authors demonstrate some weak properties of Tr(λx^(-1)) against fast algebraic attacks.

关键词： Algebraic immunity boolean functions fast algebraic attacks trace inverse functions

Dynamical system with Markov parameters for modelling system security of threats in cyberspace

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AIP Conference Proceedings 2019年第1期2116卷

作者： Miroslava Růžičková Irada Dzhalladova Josef Diblík 1University of Białystok Faculty of Mathematics and Informatics K. Ciołkowskiego 1M 15-245 Białystok Poland 2National University of Economics Department of Computer Mathematics and Information Security Kyiv 03068 Peremogy 54/1 Ukraine 3Brno University of Technology Faculty of Electrical Engineering and Communication Department of Mathematics Technická 3058/10 61600 Brno Czech Republic

The moment equations are derived for a system of linear difference equations with Markov switching. One example illustrates the use of such systems for modeling the security of threats in cyberspace.

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A community-powered search of machine learning strategy space to find NMR property prediction models

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arXiv 2020年

作者： Bratholm, Lars A. Gerrard, Will Anderson, Brandon Bai, Shaojie Choi, Sunghwan Dang, Lam Hanchar, Pavel Howard, Addison Huard, Guillaume Kim, Sanghoon Kolter, Zico Kondor, Risi Kornbluth, Mordechai Lee, Youhan Lee, Youngsoo Mailoa, Jonathan P. Nguyen, Thanh Tu Popovic, Milos Rakocevic, Goran Reade, Walter Song, Wonho Stojanovic, Luka Thiede, Erik H. Tijanic, Nebojsa Torrubia, Andres Willmott, Devin Butts, Craig P. Glowacki, David R. Participants, Kaggle School of Chemistry University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom School of Mathematics University of Bristol Fry Building Woodland Road BristolBS1 1UG United Kingdom Dept. of Computer Science University of Bristol Merchant Venturer’s Building BristolBS8 1UB United Kingdom Intangible Realities Laboratory University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom Bosch Center for Artificial Intelligence PittsburghPA15222 United States Carnegie Mellon University PittsburghPA15213 United States National Institute of Supercomputing and Network Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of BNP Paribas Cardif Fyusion Inc. Kaggle Google Inc. Mountain View United States Ebay Korea 34F Gangnam Finance Center 152 Teheran Ro Gangnam Gu Seoul Korea Republic of Department of Computer Science University of Chicago 5730 S Ellis Ave ChicagoIL60637 United States Department of Statistics University of Chicago 5747 S Ellis Ave ChicagoIL60637 United States Center for Computational Mathematics Flatiron Institute 162 5th Ave. New YorkNY10010 United States Bosch Research and Technology Center CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of 111 Daedeok-daero 989beon-gil Yuseong-gu Daejeon Korea Republic of MINDS AND COMPANY 2621 Nambusunhwan-ro Gangnam-gu Seoul06267 Korea Republic of Totient Inc Sindjeliceva 9 Belgrade11000 Serbia KAIST Web Security & Privacy Lab 291 Daehak-ro Yuseong-guDaejeon34141 Korea Republic of *** Alicante Spain

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties. Copyright © 2020, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance

Advances in Intelligent Systems and Computing

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Preface

Advances in Intelligent Systems and Computing 2019年 842卷 v-vi页

作者： Abawajy, Jemal Choo, Kim-Kwang Raymond Islam, Rafiqul Xu, Zheng Atiquzzaman, Mohammed Faculty of Science Engineering and Built Environment Deakin University Geelong GeelongVIC Australia Department of Information Systems and Cyber Security The University of Texas at San Antonio San AntonioTX United States School of Computing and Mathematics Charles Sturt University AlburyNSW Australia Shanghai University Shanghai Shi China School of Computer Science University of Oklahoma NormanOK United States

A survey on security and privacy issues of bitcoin

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arXiv 2017年

作者： Conti, Mauro Kumar E, Sandeep Lal, Chhagan Ruj, Sushmita Department of Mathematics University of Padua Padua Italy Department of Telecommunication Engineering Ramaiah Institute of Technology Bengaluru India Cryptology and Security Research Unit Computer and Communication Sciences Division Indian Statistical Institute India

Bitcoin is a popular cryptocurrency that records all transactions in a distributed append-only public ledger called blockchain. The security of Bitcoin heavily relies on the incentivecompatible proof-of-work (PoW) based distributed consensus protocol, which is run by network nodes called miners. In exchange for the incentive, the miners are expected to honestly maintain the blockchain. Since its launch in 2009, Bitcoin economy has grown at an enormous rate, and it is now worth about 170 billions of dollars. This exponential growth in the market value of Bitcoin motivates adversaries to exploit weaknesses for profit, and researchers to discover new vulnerabilities in the system, propose countermeasures, and predict upcoming trends. In this paper, we present a systematic survey that covers the security and privacy aspects of Bitcoin. We start by presenting an overview of the Bitcoin protocol and its major components along with their functionality and interactions within the system. We review the existing vulnerabilities in Bitcoin and its underlying major technologies such as blockchain and PoW based consensus protocol. These vulnerabilities lead to the execution of various security threats to the normal functionality of Bitcoin. We then discuss the feasibility and robustness of the state-of-theart security solutions. Additionally, we present current privacy and anonymity considerations in Bitcoin and discuss the privacyrelated threats to Bitcoin users along with the analysis of the existing privacy-preserving solutions. Finally, we summarize the critical open challenges and suggest directions for future research towards provisioning stringent security and privacy techniques for Bitcoin. Copyright © 2017, The Authors. All rights reserved.

关键词： Bitcoin

Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size

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arXiv 2017年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences School of Computer Information Engineering Jiangxi Normal University Department of Mathematics Hong Kong University of Science and Technology Tencent Ai Lab

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are batch methods designed mainly based on the convex optimization, say, the projected gradient descent method. However, they are generally time-consuming due to that the singular value decomposition (SVD) is commonly adopted during the update, especially when the data size is very large. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: (a) SVD-free via dropping convexity, with good scalability by the use of stochastic algorithm, i.e., stochastic variance reduced gradient (SVRG), and (b) adaptive step size choice via introducing a new stabilized Barzilai-Borwein (SBB) method as the original version for convex problems might fail for the considered stochastic non-convex optimization problem. Moreover, we show that the proposed algorithm converges to a stationary point at a rate O( 1/T ) in our setting, where T is the number of total iterations. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm via comparing with the state-of-the-art methods, particularly, much lower computational cost with good prediction performance. MSC Codes *** Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems