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The need to implement FAIR principles in biomolecular simulations

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arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein Data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

A deduplication algorithm based on data similarity and delta encoding 4th

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A deduplication algorithm based on data similarity and delta...

4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Song, Bin Xiao, Limin Qin, Guangjun Ruan, Li Qiu, Shida State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China National Engineering Research Center for Science and Technology Resources Sharing Service Beijing100191 China Space Star Technology Co. Ltd. Beijing100086 China

ISBN: (纸本)9789811039683

Satellite applications such as remote sensing application are overwhelmed with vast quantities of data. Nevertheless, the storage resources in the satellite are so limited that it should be used more efficient. The similarity between the remote sensing data is high, but the dissimilar parts of the data distribute irregularly. When using the traditional deduplication algorithm to split the file into chunks, a large amount of chunks are exactly similar but not the same, which results in the bad effect of data deduplication. We propose a deduplication algorithm based on data similarity and delta encoding to reduce the usage of storage resources. The data similarity analysis can find out the similar data. The delta encoding technology can reduce the usage of storage resources. Through experiments on remote sensing application data, we have achieved deduplication ratios up to 30:1, and analyzed how the chunksize affect the experiment results. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Satellites

A Theorem for Secrecy in Tagged Protocols Using the Theory of Witness-Functions

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arXiv 2018年

作者： Fattahi, Jaouhar Valcartier Research Centre Defence Research and Development Canada 2459 de la Bravoure Road QuébecG3J 1X5 Canada Laboratory of Computer Security Department of Computer Science and Software Engineering. Laval University Pavillon Adrien-Pouliot local 3770 QuébecG1K 7P4 Canada

—In this paper, we enunciate the theorem of secrecy in tagged protocols using the theory of witness-functions and we run a formal analysis on a new tagged version of the Needham-Schroeder public-key protocol using this theorem. We discuss the significance of tagging in securing cryptographic protocols as well. Copyright © 2018, The Authors. All rights reserved.

关键词： Cryptography

A Test Language for Avionics System

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A Test Language for Avionics System

2017 IEEE 8th International Conference on software engineering and Service Science

作者： Yanfang Liu Jianghua Lv Wei Wang Tao Li Shilong Ma Department of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment System Engineering Research Institute of China State Shipbuilding Corporation

In this paper, we proposed an automated test framework, logical test device which makes complex avionics systems transparent and jump machines which ensure data security of SUT(System Under Test). Based on this test framework, we mainly proposed a universal test language for avionics systems. The key feature of our test language is to introduce device type data and device collaboration operations which support automatic collaboration between logical test device and jump machines, meanwhile make test language more universal. This test language has been applied to the actual avionics system testing in China Academy of Electronics and Information Technology. Relative to avionics system testing before automation, our test language changes work pattern of avionics system testing and improves the test efficiency.

关键词： Avionics system test framework test language automated testing device collaboration

Plastic optical fiber biosensors 26

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Plastic optical fiber biosensors

26th International Conference on Plastic Optical Fibres, POF 2017 - Proceedings

作者： Werneck, Marcelo Lopes, Rafaela Rodrigues, Domingos Costa, Natália Soares, Larissa Arcas, Ariadny Dutra, Fabio Costa, Greice Landi, Louise Werneck, Arthur Allil, Regina Electrical Engineering Program and Nanotechnology Engineering Program Photonics and Instrumentation Laboratory Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil PETROBRAS Research and Development Center Rio de Janeiro Brazil Department of Physics Universidade Federal Rural do Rio de Janeiro Seropédica Rio de Janeiro Brazil Photonics and Instrumentation Laboratory - Department of Electronic and Computing School of Engineering Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Photonics and Instrumentation Laboratory Software Engineering Instituto Infinet Rio de Janeiro Brazil

ISBN: (纸本)9789899734524

The need for robust, rapid, disposable and low-cost detection of viruses and bacteria has become paramount for medical diagnosis, environment control or food industry. Refractive index (RI) sensing functionalized with specific antibodies offers a promising detection mechanism. With this technique in mind we developed sensors based either in surface plasmon resonance or in evanescent wave, driven by a LED-photodiode pair or smartphone technology. Good results were obtained for measuring refractive index and bacteria such as Desulfovibrio alaskensis and Escherichia coli. The use of a simple U-bent sensor allows a viable alternative for mass production, leading to a highly robust and machinable sensor and ease probe fabrication allowing low cost and good repeatability.

关键词： Plastic optical fibers

A fine-grained data set and analysis of tangling in bug fixing commits

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arXiv 2020年

作者： Herbold, Steffen Trautsch, Alexander Ledel, Benjamin Aghamohammadi, Alireza Ghaleb, Taher Ahmed Chahal, Kuljit Kaur Bossenmaier, Tim Nagaria, Bhaveet Makedonski, Philip Ahmadabadi, Matin Nili Szabados, Kristof Spieker, Helge Madeja, Matej Hoy, Nathaniel G. Lenarduzzi, Valentina Wang, Shangwen Rodríguez-Pérez, Gema Colomo-Palacios, Ricardo Verdecchia, Roberto Singh, Paramvir Qin, Yihao Chakroborti, Debasish Davis, Willard Walunj, Vijay Wu, Hongjun Marcilio, Diego Alam, Omar Aldaeej, Abdullah Amit, Idan Turhan, Burak Eismann, Simon Wickert, Anna-Katharina Malavolta, Ivano Sulír, Matúš Fard, Fatemeh Henley, Austin Z. Kourtzanidis, Stratos Tuzun, Eray Treude, Christoph Shamasbi, Simin Maleki Pashchenko, Ivan Wyrich, Marvin Davis, James C. Serebrenik, Alexander Albrecht, Ella Aktas, Ethem Utku Strüber, Daniel Erbel, Johannes Institute for Software and Systems Engineering TU Clausthal Clausthal-Zellefeld Germany Institute of Computer Science University of Goettingen Goettingen Germany Department of Computer Engineering Sharif University of Technology Tehran Iran School of Computing Queen's University Kingston Canada Department of Computer Science Guru Nanak Dev University Amritsar India Karlsruhe Germany Brunel University London Uxbridge United Kingdom Institute of Computer Science University of Goettingen Germany University of Tehran Tehran Iran Ericsson Hungary Ltd. Budapest Hungary Simula Research Laboratory Fornebu Norway Technical University of Košice Košice Slovakia LUT University Finland National University of Defense Technology Changsha China University of British Columbia Kelowna Canada Østfold University College Halden Norway Vrije Universiteit Amsterdam Amsterdam Netherlands University of Auckland Auckland New Zealand University of Saskatchewan Saskatoon Canada IBM Boulder United States University of Missouri-Kansas City Kansas City United States Università della Svizzera italiana Lugano Switzerland Trent University Peterborough Canada University of Maryland Baltimore County United States Imam Abdulrahman Bin Faisal University Saudi Arabia The Hebrew University Acumen Jerusalem Israel University of Oulu Oulu Finland Monash University Melbourne Australia University of Würzburg Würzburg Germany Technische Universität Darmstadt Darmstadt Germany University of Tennessee Knoxville United States University of Macedonia Thessaloniki Greece Department of Computer Engineering Bilkent University Ankara Turkey University of Melbourne Melbourne Australia Independent Researcher Tehran Iran University of Trento Trento Italy University of Stuttgart Stuttgart Germany Purdue University West LafayetteIN United States Eindhoven University of Technology Eindhoven Netherlands Softtech Inc. Research and Development Center Istanbul34947 Turkey Radboud Universi

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

Framework for Social Relationship Recommendation

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Framework for Social Relationship Recommendation

2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Jia Chen Tongge Xu Zhang Xiong The State Key Laboratory of Software Development Environment Beihang University Research Institute of Beihang University in Shenzhen Lab 673 School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781510845541

Find out the potential favorite data is the essence of recommendation techniques, which facilitates the recommendation techniques become a vital issue in the big data researches and applications. The traditional recommendation research and application centers on the commercial items and resources. We apply the recommendation to social relationships in social networks. We design a framework which can analyze and recommend various types of relationships. The framework has four modules and three work modes. Furthermore, we design a hybrid recommendation approach in the framework for the balancing between accuracy and efficiency. The framework can recommend the social relationships with high accuracy and low cost.

关键词： big data framework recommendation social relationships

Unsupervised learning for cell-level visual representation in histopathology images with generative adversarial networks

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arXiv 2017年

作者： Hu, Bo Tang, Ye Chang, Eric I-Chao Fan, Yubo Lai, Maode Xu, Yan State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute Beihang University in Shenzhen Beijing Advanced Innovation Centre for Biomedical Engineering Beihang University Beijing100191 Department of Pathology School of Medicine Zhejiang University Microsoft Research Beijing100080 China

The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis. By learning cell-level visual representation, we can obtain a rich mix of features that are highly reusable for various tasks, such as cell-level classification, nuclei segmentation, and cell counting. In this paper, we propose a unified generative adversarial networks architecture with a new formulation of loss to perform robust cell-level visual representation learning in an unsupervised setting. Our model is not only label-free and easily trained but also capable of cell-level unsupervised classification with interpretable visualization, which achieves promising results in the unsupervised classification of bone marrow cellular components. Based on the proposed cell-level visual representation learning, we further develop a pipeline that exploits the varieties of cellular elements to perform histopathology image classification, the advantages of which are demonstrated on bone marrow datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Cells

Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device

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arXiv 2017年

作者： Zhang, Xin Kou, Weixuan Chang, Eric I-Chao Gao, He Fan, Yubo Xu, Yan State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology Ministry of Education and Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Centre for Biomedical Engineering Beihang University Beijing100191 Microsoft Research Beijing100080 Clinical Sleep Medicine Center General Hospital of the Air Force Beijing100142

This paper proposes a practical approach for automatic sleep stage classification based on a multi-level feature learning framework and Recurrent Neural Network (RNN) classifier using heart rate and wrist actigraphy derived from a wearable device. The feature learning framework is designed to extract low- and mid-level features. Low-level features capture temporal and frequency domain properties and mid-level features learn compositions and structural information of signals. Since sleep staging is a sequential problem with long-term dependencies, we take advantage of RNNs with Bidirectional Long Short-Term Memory (BLSTM) architectures for sequence data learning. To simulate the actual situation of daily sleep, experiments are conducted with a resting group in which sleep is recorded in resting state, and a comprehensive group in which both resting sleep and non-resting sleep are included. We evaluate the algorithm based on an eight-fold cross validation to classify five sleep stages (W, N1, N2, N3, and REM). The proposed algorithm achieves weighted precision, recall and F1score of 58.0%, 60.3%, and 58.2% in the resting group and 58.5%, 61.1%, and 58.5% in the comprehensive group, respectively. Various comparison experiments demonstrate the effectiveness of feature learning and BLSTM. We further explore the influence of depth and width of RNNs on performance. Our method is specially proposed for wearable devices and is expected to be applicable for long-term sleep monitoring at home. Without using too much prior domain knowledge, our method has the potential to generalize sleep disorder detection. Copyright © 2017, The Authors. All rights reserved.

关键词： Heart