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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Creating bus timetables with maximal synchronization by a new algorithm based on the PSO 17

Creating bus timetables with maximal synchronization by a ne...

引用

17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and innovation, CICTP 2017

作者： Tian, Daxin Liu, Wenhao Guo, Peng Rong, Hui Wang, Wenyang Gong, Jinfeng Wang, Yunpeng Liu, He Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing100191 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Nanjing210096 China Quartermaster Equipment Institute of the General Logistics Dept. of CPLA Beijing100010 China

ISBN: (数字)9780784480915

ISBN: (纸本)9780784480915

This paper addresses the problem of receiving a timetable for a settled network with maximal synchronization. In this paper, a model has been developed to solve the problem which attaches importance to creating a timetable for maximizing their synchronization and enables the transfer of passengers from one route to another with least waiting times at transfer nodes. Next, this problem is formulated as a mixed integer linear programming problem, and a new algorithm based on the traditional particle swarm optimization (PSO) has been developed to solve the problem in a short time. Finally, the relationship of iterative time and the complexity of the given network is qualitatively established. © ASCE.

关键词： Synchronization

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Inland port location planning based on user equilibrium 17

Inland port location planning based on user equilibrium

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17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and innovation, CICTP 2017

作者： Wei, Jingyi Tian, Daxin Rong, Hui Guo, Peng Wang, Wenyang Gong, Jinfeng Wang, Yunpeng Liu, He Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing100191 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Nanjing210096 China Automotive Engineering Research Institute China Automotive Technology and Research Center Tianjin300300 China Quartermaster Equipment Institute of the General Logistics Dept. Of CPLA Beijing100010 China

ISBN: (数字)9780784480915

ISBN: (纸本)9780784480915

With stable development of container transportation in shipping industry, the transit shipment from road to railway of inland ports plays a significant role in multimodal transport. This paper presents the method of inland port location planning based on user equilibrium. In the study, the virtual connection simulating the process of transfer in inland ports is conducted to measure the fitness of inland port location through the cargo volume on virtual connection. At last, the methodology was applied into practical experiment with the results showing that the method can locate the suit inland port location. © ASCE.

关键词： Location

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Model learning: A survey on foundation, tools and applications

arXiv

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arXiv 2018年

作者： Ali, Shahbaz Sun, Hailong Zhao, Yongwang School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

The quality and correct functioning of software components embedded in electronic systems are of utmost concern especially for safety and mission-critical systems. Model-based testing and formal verification techniques can be employed to enhance the reliability of software systems. Formal models form the basis and are prerequisite for the application of these techniques. An emerging and promising model learning technique can complement testing and verification techniques by providing learned models of black box systems fully automatically. This paper surveys one such state of the art technique called model learning which recently has attracted much attention of researchers especially from the domains of testing and verification. This survey paper reviews and provides comparison summaries highlighting the merits and shortcomings of learning techniques, algorithms, and tools which form the basis of model learning. This paper also surveys the successful applications of model learning technique in multidisciplinary fields making it promising for testing and verification of realistic systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

引用

NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Higher key rate practical twin field quantum key distribution through sending or not sending

arXiv

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arXiv 2019年

作者： Jiang, Cong Hu, Xiao-Long Xu, Hai Yu, Zong-Wen Wang, Xiang-Bin State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Data Communication Science and Technology Research Institute Beijing100191 China Jinan Institute of Quantum technology SAICT Jinan250101 China Shenzhen Institute for Quantum Science and Engineering Physics Department Southern University of Science and Technology Shenzhen518055 China Center for Atomic and Molecular Nanosciences Tsinghua University Beijing100084 China

The sending-or-not-sending (SNS) protocol of the twin-field quantum key distribution (TF-QKD) has its advantage of unconditional security proof under whatever coherent attack and fault tolerance to large misalignment error, and hence has attracted lots of attentions. In this paper, we study the finite key effect of SNS protocol with two way communication and obtain the key rate formula by taking all the finite key effect into consideration. Besides, we show how to apply our method to the asymmetric SNS protocol. The numerical results show that in the case of finite key size, our method can greatly improve the key rate of SNS protocol with both symmetric channel and asymmetric channel, and unconditionally break the absolute key rate limit of repeater-less quantum key distribution. Comparison shows that the simulated key rates presented here are the best known results of practical QKD. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum cryptography

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Improved results for sending-or-not-sending twin-field quantun key distribution: Breaking the absolute limit of repeaterless key rate

arXiv

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arXiv 2019年

作者： Xu, Hai Yu, Zong-Wen Jiang, Cong Hu, Xiao-Long Wang, Xiang-Bin State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Data Communication Science and Technology Research Institute Beijing100191 China Jinan Institute of Quantum Technology SAICT Jinan250101 China Shenzhen Institute for Quantum Science and Engineering Physics Department Southern University of Science and Technology Shenzhen518055 China Center for Atomic and Molecular Nanosciences Tsinghua University Beijing100084 China

We present improved results of twin-field quantum key distribution (TF-QKD) by using the structure of sending-or-not-sending (SNS) protocol and error rejection through two-way classical communications. Taking a typical experimental parameter setting, our method here improves the secure distance by 70 kilometers to more than 100 kilometers in comparison with the prior art results. Comparative study also shows advantageous in key rates at regime of long distance and large misalignment error rate for our method here. The numerical results show that our method here can have an advantageous key rates higher than various prior art results by 10 to 20 times. Taking all finite-key effects into consideration, it breaks the absolute repeater-less key rate bound with only 1011pulses. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum cryptography

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Shared causal paths underlying Alzheimer's dementia and type 2 diabetes

arXiv

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arXiv 2019年

作者： Hu, Zixin Jiao, Rong Wang, Jiucun Wang, Panpan Zhu, Yun Zhao, Jinying de Jager, Phil Bennett, David A. Jin, Li Xiong, Momiao State Key Laboratory of Genetic Engineering Innovation Center of Genetics and Development School of Life Sciences Fudan University Shanghai China Department of Biostatistics and Data Science School of Public Health University of Texas Health Science Center at Houston HoustonTX United States Human Phenome Institute Fudan University Shanghai China Department of Epidemiology University of Florida Florida United States Center for Translational & Computational Neuroimmunology Department of Neurology Columbia University Medical Center New York10033 United States Rush Alzheimer's Disease Center Rush University Medical Center ChicagoIL60612 United States

Background: Although Alzheimer's disease (AD) is a central nervous system disease and type 2 diabetes mellitus (T2DM) is a metabolic disorder, an increasing number of genetic epidemiological studies show clear link between AD and T2DM. The current approach to uncovering the shared pathways between AD and T2DM involves association analysis;however, such analyses lack power to discover the mechanisms of the diseases. Methods: We develop novel statistical methods to shift the current paradigm of genetic analysis from association analysis to deep causal inference for uncovering the shared mechanisms between AD and T2DM, and develop pipelines to infer multilevel omics causal networks which lead to shifting the current paradigm of genetic analysis from genetic analysis alone to integrated causal genomic, epigenomic, transcriptional and phenotypic data analysis. To discover common causal paths from genetic variants to AD and T2DM, we also develop algorithms that can automatically search the causal paths from genetic variants to diseases and Results: The proposed methods and algorithms are applied to ROSMAP dataset with 432 individuals who simultaneously had genotype, RNA-seq, DNA methylation and some phenotypes. We construct multi-omics causal networks and identify 13 shared causal genes, 16 shared causal pathways between AD and T2DM, and 754 gene expression and 101 gene methylation nodes that were connected to both AD and T2DM in multi-omics causal networks. Conclusions: The results of application of the proposed pipelines for identifying causal paths to real data analysis of AD and T2DM provided strong evidence to support the link between AD and T2DM and unraveled causal mechanism to explain this link. Copyright © 2019, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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Low Noise VCSEL Driver for SERF Atomic Magnetometer

引用

IOP Conference Series: Materials science and engineering 2020年第1期711卷

作者： Feihu Wang Zhaohui Hu Xin Liu School of Instrumentation and Optoelectronic Engineering Beihang University Beijing 100191 China Science and Technology on Inertial Laboratory Beihang University Beijing 100191 China Research Institute of Frontier Science Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing 100191 China

A new type of VCSEL (Vertical-Cavity Surface-Emitting Laser) which uses Pt1000 as a temperature sensitive component and non-magnetic heating membrane as a temperature actuator is designed for SERF (Spin-Exchange Relation-Free) atomic magnetometer, which can decrease the magnetic interference produced while heating or cooling VCSEL. Therefore a low noise driver for this kind of VCSEL is necessary. A driver for the novel VCSEL is designed in this paper. The output current is adjustable from 0 to 4.5 mA, and RMS (Root Mean Square) noise of current is less than 80 nA. The operating temperature of the VCSEL is adjustable between 30-110 °C and RMS noise of temperature is less than 0.6 mK. So that it can be ensured that the VCSEL outputs a laser with stable optical power and wavelength.

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Mobile laser scanning systems for GPS/GNSS-denied environment mapping

Mobile laser scanning systems for GPS/GNSS-denied environmen...

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2018 ISPRS Technical Commission I Midterm Symposium on Innovative Sensing - From Sensors to Methods and Applications

作者： Wen, Chenglu Xia, Yan Lian, Yuhan Dai, Yudi Tan, Jinbin Wang, Cheng Li, Jonathan Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Xiamen361005 China Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou350003 China Faculty of Environment University of Waterloo WaterlooONN2L 3G1 Canada

Indoor 3D mapping provides a useful three-dimensional structure via an indoor map for many applications. To acquire highly efficient and relatively accurate mapping for large-scale GPS/GNSS-denied scene, we present an upgraded backpacked laser scanning system and a car-mounted indoor mobile laser scanning system. The systems provide both 3D laser scanning point cloud and camera images. In this paper, a simultaneous extrinsic calibration approach for multiple multi-beam LIDAR and multiple cameras is also proposed using the Simultaneous Localization and Mapping (SLAM)-based algorithm. The proposed approach uses the SLAM-based algorithm to achieve a large calibration scene using mobile platforms, registers an acquired multi-beam LIDAR point cloud to the terrestrial LIDAR point cloud to acquire denser points for corner feature extraction, and finally achieves simultaneous calibration. With the proposed mapping and calibration algorithms, we can provide centimetre-lever coloured point cloud with relatively high efficiency and accuracy. © Authors 2018. CC BY 4.0 License.

关键词： Mapping

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