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Blind Image Quality Assessment Using A Deep Bilinear Convolutional neural Network

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arXiv 2019年

作者： Zhang, Weixia Ma, Kede Yan, Jia Deng, Dexiang Wang, Zhou IEEE Electronic Information School Wuhan University Wuhan China Center for Neural Science New York University New YorkNY10003 United States Department of Electrical and Computer Engineering University of Waterloo WaterlooON N2L 3G1 Canada

We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions. Our model consists of two convolutional neural networks (CNN), each of which specializes in one distortion scenario. For synthetic distortions, we pre-train a CNN to classify image distortion type and level, where we enjoy large-scale training data. For authentic distortions, we adopt a pre-trained CNN for image classification. The features from the two CNNs are pooled bilinearly into a unified representation for final quality prediction. We then fine-tune the entire model on target subject-rated databases using a variant of stochastic gradient descent. Extensive experiments demonstrate that the proposed model achieves superior performance on both synthetic and authentic databases. Furthermore, we verify the generalizability of our method on the Waterloo Exploration Database using the group maximum differentiation competition. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

CEPC Technical Design Report

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Radiation Detection Technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed Xiaocong Ai Abid Aleem Wolfgang Altmannshofer Fabio Alves Weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai Yu Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan Bao Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci Xiao-Jun Bi Yuanjie Bi Tianjian Bian Fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai Wenyong Cai Wujun Cai Yijian Cai Yizhou Cai Yuchen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance Xuejun Chang Yue Chang Zhe Chang Xinyuan Chang Wei Chao Auttakit Chatrabhuti Yimin Che Yuzhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-Yuan Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen Wei Chen Xiang Chen Xiaoyu Chen Xin Chen Xun Chen Xurong Chen Ye Chen Ying Chen Yukai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng Yunlong Chi Pietro Chimenti Wen Han Chiu Guk Cho Ming-Chung Chu Xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui Xiaohao Cui Zhaoyuan Cui Brunella D'Anzi Ling-Yun Dai Xinchen Dai Xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng Wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi Dong Jianin AGH University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakow Anhui University HefeiAnhui Applied Physics Department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu University Institute of ScienceVaranasi Bandirma Onyedi Eylul University Bandirma Beihang University Beijing Beijing Computational Science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of Technology Beijing Beijing Normal University Beijing Brown University ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad University Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo University Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central China Normal University WuhanHubei Central South University ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan China academy of engineering physics MianyangSichuan China building materials academy Beijing China Center of Advanced Science and Technology Beijing China Institute of Atomic Energy Beijing China Jiliang University HangzhouZhejiang China Nuclear Instrument CO. LTDBeijing China University of Geosciences Beijing China University of Geosciences WuhanHubei China University of Mining and Technology XuzhouJiangsu China West Normal University NanchongSichuan Chongqing University Department of PhysicsChongqing Chulalongkorn University Bangkok City University of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell University IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu University DalianLiaoning Dalian University of Technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East China University Of Science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by China,fostered through extensive collaboration with international *** complex comprises four accelerators:a 30 GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2015)and the Conceptual Design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-2030s.

关键词： initiated exceeding precise

Automatic detection of passable roads after floods in remote sensed and social media data

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arXiv 2019年

作者： Ahmad, Kashif Pogorelov, Konstantin Riegler, Michael Ostroukhova, Olga Halvorsen, Pål Conci, Nicola Dahyot, Rozenn Adapt Centre School of Computer Science and Statistics Trinity College Dublin Ireland Simula Research Laboratory University of Oslo Norway Simula Metropolitan Center for Digital Engineering University of Oslo Norway Research Institute of Multiprocessor Computation Systems n.a. A.V. Kalyaev Russia University of Trento Italy

This paper addresses the problem of floods classification and floods aftermath detection utilizing both social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods are carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Floods

Supervised discriminative sparse PCA for com-characteristic gene selection and tumor classification on multiview biological data

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arXiv 2019年

作者： Feng, Chun-Mei Xu, Yong Liu, Jin-Xing Gao, Ying-Lian Zheng, Chun-Hou Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Key Laboratory of Network Oriented Intelligent Computation Shenzhen518055 China School of Information Science and Engineering Qufu Normal University Rizhao276826 China Library of Qufu Normal University Qufu Normal University Rizhao276826 China School of Computer Science and Technology Anhui University Hefei230000 China

Principal component analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance of these methods is still far from satisfactory due to their limitation of using unsupervised learning methods;moreover, the class ambiguity within the sample is high. To overcome this problem, this paper developed a new PCA method, which is named the supervised discriminative sparse PCA (SDSPCA). The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model. Specifically, in contrast to the traditional sparse PCA, which imposes sparsity on the loadings, here, sparse components are obtained to represent the data. Furthermore, via the linear transformation, the sparse components approximate the given label information. On the one hand, sparse components improve interpretability over the traditional PCA, while on the other hand, they are have discriminative abilities suitable for classification purposes. A simple algorithm is developed, and its convergence proof is provided. SDSPCA has been applied to the common-characteristic gene selection and tumor classification on multiview biological data. The sparsity and classification performance of SDSPCA are empirically verified via abundant, reasonable, and effective experiments, and the obtained results demonstrate that SDSPCA outperforms other state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Principal component analysis

Objective comparison of methods to decode anomalous diffusion

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arXiv 2021年

作者： Muñoz-Gil, Gorka Volpe, Giovanni Garcia-March, Miguel Angel Aghion, Erez Argun, Aykut Hong, Chang Beom Bland, Tom Bo, Stefano Conejero, J. Alberto Firbas, Nicolás Orts, Òscar Garibo Gentili, Alessia Huang, Zihan Jeon, Jae-Hyung Kabbech, Hélène Kim, Yeongjin Kowalek, Patrycja Krapf, Diego Loch-Olszewska, Hanna Lomholt, Michael A. Masson, Jean-Baptiste Meyer, Philipp G. Park, Seongyu Requena, Borja Smal, Ihor Song, Taegeun Szwabinski, Janusz Thapa, Samudrajit Verdier, Hippolyte Volpe, Giorgio Widera, Arthur Lewenstein, MacIej Metzler, Ralf Anzo, Carlo 08860 Spain Department of Physics University of Gothenburg Origovägen 6B GothenburgSE-41296 Sweden Instituto Universitario de Matemática Pura y Aplicada Universitat Politècnica de València Spain Max Planck Institute for the Physics of Complex Systems Nöthnitzer Straße 38 DresdenDE-01187 Germany Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of Francis Crick Institute 1 Midland Road LondonNW1 1AT United Kingdom Department of Chemistry University College London 20 Gordon Street LondonWC1H 0AJ United Kingdom School of Physics and Electronics Hunan University Changsha410082 China Department of Cell Biology Erasmus Mc Rotterdam Netherlands Faculty of Pure and Applied Mathematics Hugo Steinhaus Center Wroclaw University of Science and Technology Wroclaw Poland Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80523 United States PhyLife Department of Physics Chemistry and Pharmacy University of Southern Denmark Odense MDK-5230 Denmark Institut Pasteur Decision and Bayesian Computation Lab Paris France Center for Ai and Natural Sciences Korea Institute for Advanced Study Seoul Korea Republic of Institute for Physics and Astronomy University of Potsdam Karl-Liebknecht-Str 24/25 Potsdam-GolmD-14476 Germany Sackler Center for Computational Molecular and Materials Science Tel Aviv University Tel Aviv69978 Israel School of Mechanical Engineering Tel Aviv University Tel Aviv69978 Israel Institut Pasteur Decision and Bayesian Computation Lab Paris France Department of Physics Research Center Optimas Technische Universität Kaiserslautern Kaiserslautern67663 Germany Icrea Pg. Lluís Companys 23 Barcelona08010 Spain C. de la Laura 13 Vic08500 Spain

Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics, playing a crucial role in phenomena from quantum physics to life sciences. The detection and characterization of anomalous diffusion from the measurement of an individual trajectory are challenging tasks, which traditionally rely on calculating the mean squared displacement of the trajectory. However, this approach breaks down for cases of important practical interest, e.g., short or noisy trajectories, ensembles of heterogeneous trajectories, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams independently applied their own algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, providing practical advice for users and a benchmark for developers. Copyright © 2021, The Authors. All rights reserved.

关键词： Trajectories

An algorithm for describing the convex and concave shape of protein surface 1

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4th International Conference of Pioneer computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Wang, Wei Li, Keliang Lv, Hehe Sun, Lin Zhang, Hongjun Shi, Jinling Zhang, Shiguang Zhou, Yun Zhao, Yuan Xv, Jingjing Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University XinxiangHenan Province453007 China Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining XinxiangHenan Province453007 China School of Aviation Engineering Anyang University AnyangHenan Province455000 China School of International Education Xuchang University XuchangHenan Province461000 China

ISBN: (数字)9789811322068

ISBN: (纸本)9789811322051

Protein surface plays a key role in many biological processes. Most proteins participate in the life activities of cells via binding to other proteins or ligand molecules. It is an important work to study protein structure and function by analyzing the protein surface shape. Based on the CX algorithm and the 2D fngerprint-base method, we proposed a FCX method to identify the morphology of bulges and depressions on the protein surface. The experimental results show that the FCX algorithm has a more desirable outcome than CX algorithm. The FCX algorithm has a higher correlation with the convex and concave features than CX values with solvent accessibility, solvent accessibility, and B-factor’s Pearson correlation coefficient. This result shows that the FCX algorithm can describe the shape of the protein surface residues more accurately than the CX algorithm. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Proteins

Large margin multi-modal multi-task feature extraction for image classification

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arXiv 2019年

作者： Luo, Yong Wen, Yonggang Tao, Dacheng Gui, Jie Xu, Chao School of Electronics Engineering and Computer Science Peking University Beijing China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Centre for Quantum Computation & Intelligent Systems Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We therefore propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization and each sub-problem can be efficiently solved. Experiments on two challenging real-world image datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

Establishment of a low-tumorigenic MDCK cell line and study of differential molecular networks

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Biologicals : journal of the International Association of Biological Standardization 2020年 68卷 112-121页

作者： Gui-Lan Ma Zi-Lin Qiao Dan He Jiao Wang Yan-Yan Kong Xiao-Yong Xin Feng-Qin Wen Shi-Jun Bao Zhong-Ren Ma Fu-Shuai Wang Jiang Xie Yong-Hao Hu College of Veterinary Medicine Gansu Agricultural University Lanzhou 730030 PR China Gansu Tech Innovation Center of Animal Cell Biomedical Research Center Northwest Minzu University Lanzhou 730030 PR China. Electronic address: cnsff_20@***. Gansu Tech Innovation Center of Animal Cell Biomedical Research Center Northwest Minzu University Lanzhou 730030 PR China. Electronic address: qiaozilin@***. Gansu Tech Innovation Center of Animal Cell Biomedical Research Center Northwest Minzu University Lanzhou 730030 PR China. Electronic address: hedan1018@***. Laboratory of Molecular Neural Biology School of Life Sciences Shanghai University Shanghai China. Electronic address: jo717@***. Huashan Hospital Affiliated to Fudan University Shanghai China. Electronic address: kongyanyan@***. College of Veterinary Medicine Gansu Agricultural University Lanzhou 730030 PR China. Electronic address: xingxy@***. College of Veterinary Medicine Gansu Agricultural University Lanzhou 730030 PR China. Electronic address: 46292083@***. College of Veterinary Medicine Gansu Agricultural University Lanzhou 730030 PR China. Electronic address: bsjgn@***. Gansu Tech Innovation Center of Animal Cell Biomedical Research Center Northwest Minzu University Lanzhou 730030 PR China. Electronic address: mazhr@***. Laboratory of Molecular Neural Biology School of Life Sciences Shanghai University Shanghai China. Electronic address: 768537285@***. School of Computer Engineering and Science Shanghai University Shanghai China. Electronic address: jiangx@***. College of Veterinary Medicine Gansu Agricultural University Lanzhou 730030 PR China. Electronic address: yhh0817@***.

Influenza is an acute respiratory infection caused by the influenza virus, and vaccination against influenza is considered the best way to prevent the onset and spread. MDCK (Madin-Darby canine kidney) cells are typically used to isolate the influenza virus, however, their high tumorigenicity is the main controversy in the production of influenza vaccines. Here, MDCK-C09 and MDCK-C35 monoclonal cell lines were established, which were proven to be low in tumorigenicity. RNA-seq of MDCK-C09, MDCK-C35, and MDCK-W73 cells was performed to investigate the putative tumorigenicity mechanisms. Tumor-related molecular interaction analysis of the differentially expressed genes indicates that hub genes, such as CUL3 and EGFR, may play essential roles in tumorigenicity differences between MDCK-C (MDCK-C09 and MDCK-C35) and MDCK-W (MDCK-W73) cells. Moreover, the analysis of cell proliferation regulation-associated molecular interaction shows that downregulated JUN and MYC, for instance, mediate increased proliferation of these cells. The present study provides a new low-tumorigenic MDCK cell line and describes the potential molecular mechanism for the low tumorigenicity and high proliferation rate.

关键词： MDCK cells Molecular interactions Monoclonal technique Tumorigenicity