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Correction: The Effect of Co-Culture of Two Coral Species on Their Bacterial Composition Under Captive Environments

Marine biotechnology (New York, N.Y.) 2022年第6期24卷 1176页

作者： Keigo Ide Yoshikatsu Nakano Michihiro Ito Yohei Nishikawa Hiroyuki Fujimura Haruko Takeyama Department of Life Science and Medical Bioscience Waseda University Tokyo Japan. Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology Tokyo Japan. Tropical Biosphere Research Center University of the Ryukyus Tokyo Japan. Marine Science Section Research Support Division Okinawa Institute of Science and Technology Graduate University Okinawa Japan. Research Organization for Nano and Life Innovation Waseda University Tokyo Japan. Department of Chemistry Biology and Marine Science University of the Ryukyus Okinawa Japan. Department of Life Science and Medical Bioscience Waseda University Tokyo Japan. haruko-takeyama@waseda.jp. Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology Tokyo Japan. haruko-takeyama@waseda.jp. Research Organization for Nano and Life Innovation Waseda University Tokyo Japan. haruko-takeyama@waseda.jp. Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and Engineering Tokyo Japan. haruko-takeyama@waseda.jp.

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Petri Net Control Method for Pipe-line Systems and Its Implementation via CIF3 ⁎

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IFAC-PapersOnLine 2019年第24期52卷 261-266页

作者： YuHao Fu JiLiang Luo Huifeng Wu Jianhong Ye Yi-sheng Huang College Information Science and Engineering Huaqiao University Xiamen 361021 China College Information Science and Engineering Huaqiao University Xiamen 361021 China and also with Fujian Engineering Research Center of Motor Control and System Optimal Schedule Xiamen 361021 China Institute of Intelligent and Software Technology Hangzhou Dianzi University Hangzhou 310018 China Computer Science and Technology Huaqiao University Xiamen 361021 China and also with Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou 362000 China Department of Electrical Engineering National Ilan University Taiwan

As for a pipe-line system subjected to complex operations, an approach is proposed to synthesize the controller via Petri nets (PNs) such that the plant is run as concurrently as possible and the loads of equipments are balanced. A P-timed and labeled PN is designed to model the whole process of a pipe-line system, where tasks, including transporting material from one tank to another and cleaning tank, are represented by operational places, and level sensors amounted in tanks are represented by labels assigned to transitions. Further, monitor places are designed to resolve the conflict relations among tasks due to the shared valves and pipes. An method is presented to translate an PN controller into a CIF3 (Compositional Interchange Format) model, which can be converted into a PLC program in the CIF3 tool. A beer filtration plant is taken as an example to illustrate the approach, and its simulation experiments are carried out to verify the theoretic results.

关键词： Chemical plant Valve control Petri net CIF3

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WaveNano:a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets

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Frontiers of Electrical and Electronic engineering in China 2018年第4期6卷 359-368页

作者： Sheng Wang Zhen Li Yizhou YU Xin Gao Computational Bioscience Research Center (CBRC) King Abdullah University of Science and Technology (KAUST)Thuwa23955-6900Kingdom of Saudi Arabia Department of Computer Science University of Hong KongHong Kong SAR 999077China School of Science and Engineering Shenzhen Research Institute of Big DataThe Chinese University of Hong Kong Shenzhen 518172China

Background The Oxford MinION nanopore sequencer is the recently appealing third-generation genome sequencing device that is portable and no larger than a cellphone. Despite the benefits of MinION to sequence ultra-long reads in real-time, the high error rate of the existing base-calling methods, especially indels (insertions and deletions), prevents its use in a variety of *** In this paper, we show that such indel errors are largely due to the segmentation process on the input electrical current signal from MinION. All existing methods conduct segmentation and nucleotide label prediction in a sequential manner, in which the errors accumulated in the first step will irreversibly influence the final base-calling. We further show that the indel issue can be significantly reduced via accurate labeling of nucleotide and move labels directly from the raw signal, which can then be efficiently learned by a bi-directionalWaveNet model simultaneously through feature sharing. Our bi-directional WaveNet model with residual blocks and skip connections is able to capture the extremely long dependency in the raw signal. Taking the predicted move as the segmentation guidance, we employ the Viterbi decoding to obtain the final base-calling results from the smoothed nucleotide probability *** Our proposed base-caller, WaveNano, achieves good performance on real MinION sequencing data from Lambda *** The signal-level nanopore base-callerWaveNano can obtain higher base-calling accuracy, and generate fewer insertions/deletions in the base-called sequences.

关键词： nanopore sequencing bi-directional WaveNets base-calling third generation sequencing deep learning

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Flexible x-ray source with tunable polarization and orbital angular momentum from Hermite-Gaussian laser modes driven plasma channel wakefield

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Physical Review Accelerators and Beams 2019年第7期22卷 071302-071302页

作者： B. Lei T. Teter J. W. Wang V. Yu. Kharin C. B. Schroeder M. Zepf S. G. Rykovanov Helmholtz Institute Jena Fröbelstieg 3 07743 Jena Germany Faculty of Physics and Astronomy Friedrich-Schiller-Universität Jena 07743 Jena Germany Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences 201800 Shanghai China Lawrence Berkeley National Laboratory Berkeley California 94720 USA Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow 121205 Russia

A plasma channel undulator/wiggler may be created through the plasma wakefield excited by the beating of several Hermite-Gaussian laser modes propagating in a parabolic plasma channel. Control over both the betatron and undulator forces is conveniently achieved by tuning the amplitude ratios, colors, and order numbers of the modes. A special structure of the undulator/wiggler field without the focusing force near the propagation axis is generated inside the plasma wakefield by matching the strengths of the fundamental and first-order Hermite-Gaussian modes. The electron beam only experiences forced undulator oscillations in such a field, which significantly improves the quality of the emitted radiation. Since the value of the undulator strength parameter could be in a wide range, less or larger than unity, it is capable of generating narrow bandwidth x-ray, as well as the synchrotronlike high-energy x/γ-ray, radiation by harmonics. Additionally, controlling the relative phases between the laser modes allows for polarization control of the plasma undulator. High-order harmonics produced from a circularly polarized plasma undulator clearly show the vortex nature and carry well-defined orbital angular momentum.

关键词： Laser wakefield acceleration Laser-plasma interactions Plasma acceleration & new acceleration techniques Plasma waves X-ray generation in plasmas

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A network monitoring game with heterogeneous component criticality levels

arXiv

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arXiv 2019年

作者： Milošević, Jezdimir Dahan, Mathieu Amin, Saurabh Sandberg, Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Center for Computational Engineering Massachusetts Institute of Technology CambridgeMA United States Department of Civil and Environmental Engineering Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge United States

We consider an attacker-operator game for monitoring a large-scale network that is comprised on components that differ in their criticality levels. In this zero-sum game, the operator seeks to position a limited number of sensors to monitor the network against an attacker who strategically targets a network component. The operator (resp. attacker) seeks to minimize (resp. maximize) the network loss. To study the properties of mixed-strategy Nash Equilibria of this game, we first study two simple instances: (i) When component sets monitored by individual sensor locations are mutually disjoint;(ii) When only a single sensor is positioned, but with possibly overlapping monitoring component sets. Our analysis reveals new insights on how criticality levels impact the players equilibrium strategies. Next, we extend a previously known approach to obtain an approximate Nash equilibrium for the general case of the game. This approach uses solutions to minimum set cover and maximum set packing problems to construct an approximate Nash equilibrium. Finally, we implement a column generation procedure to improve this solution and numerically evaluate the performance of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Criticality (nuclear fission)

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Ab initio evaluation of complexation energies for cyclodextrin drug inclusion complexes

arXiv

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arXiv 2020年

作者： Oqmhula, Kenji Hongo, Kenta Maezono, Ryo Ichibha, Tom School of Materials Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan School of Information Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan

We examined the reliability of exchange-correlation functionals for molecular encapsulations combined by van der Waals forces, comparing their predictions with those of diffusion Monte Carlo method. We established that functionals with D3 dispersion force correction and including sufficient proportion of exact-exchange in long-ranged interaction can comparatively reliably estimate the binding strength. Our finding agrees with a previous ab initio study on argon dimer. However we found that even such functionals may not be able to distinguish the energy differences among different conformations. Copyright © 2020, The Authors. All rights reserved.

关键词： Van der Waals forces

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Atlas of Proteomic signatures of brain structure and its links to brain disorders

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Nature communications 2025年第1期16卷 5092页

作者： Peng Ren Xiao-He Hou Zeyu Li Jia You Yuzhu Li Wei Zhang Weikang Gong Bei Zhang Bangsheng Wu Linbo Wang Chun Shen Yujie Zhao Qing Ma Jujiao Kang Yuchao Jiang Neil Roberts Fan Xu Yong He Jin-Tai Yu Meiyun Wang Wei Cheng Institute of Science and Technology for Brain-Inspired Intelligence State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan University Shanghai China. Department of Neurology Huashan Hospital Fudan University Shanghai China. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education Shanghai PR China. Department of Neurology Qingdao Hospital University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital) Qingdao China. School of Data Science Fudan University Shanghai China. Centre for Reproductive Health (CRH) Institute for Regeneration and Repair (IRR) University of Edinburgh 4-5 Little France Drive Edinburgh EH15 4UU UK. Institute of Mechanics and Computational Engineering Department of Aeronautics and Astronautics Fudan University Shanghai China. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. Beijing Key Laboratory of Brain Imaging and Connectomics Beijing Normal University Beijing China. Institute of Science and Technology for Brain-Inspired Intelligence State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan University Shanghai China. jintai_yu@***. Department of Neurology Huashan Hospital Fudan University Shanghai China. jintai_yu@***. Department of Medical Imaging Henan Provincial People's Hospital & Zhengzhou University People's Hospital Zhengzhou China. mywang@***. Institute of Science and Technology for Brain-Inspired Intelligence State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan University Shanghai China. wcheng@***. Department of Neurology Huashan Hospital Fudan University Shanghai China. wcheng@***. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education Shanghai PR China. wcheng@fudan.edu.cn. Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspi

Individual variation in brain structure influences deterioration due to disease and comprehensive profiling of the associated proteomic signature advances mechanistic understanding. Here, using data from 4997 UK Biobank participants, we analyzed the associations between 2920 plasma proteins and 272 neuroimaging-derived brain structure measures. We identified 5358 associations between 1143 proteins and 256 brain structure measures, with NCAN and LEP proteins showing the most associations. Functional enrichment implicated these proteins in neurogenesis, immune/apoptotic processes and neurons. Furthermore, bidirectional Mendelian randomization revealed 33 associations between 32 proteins and 23 brain structure measures, and 21 associations between nine brain structure associated proteins and ten brain disorders. Moreover, the significant associations between the identified proteins and mental health were mediated by brain volume and surface area. In summary, this study generates a comprehensive atlas mapping the patterns of association between proteome and brain structure, highlighting their potential value for studying brain disorders.

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Erratum to: Long-read metagenomics of multiple displacement amplified DNA of low-biomass human gut phageomes by SACRA pre-processing chimeric reads

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DNA research : an international journal for rapid publication of reports on genes and genomes 2021年第6期28卷 dsab025页

作者： Yuya Kiguchi Suguru Nishijima Naveen Kumar Masahira Hattori Wataru Suda Cooperative Major in Advanced Health Science Graduate School of Advanced Science and Engineering Waseda University Tokyo 169-8555 Japan. Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology Tokyo 169-8555 Japan. Laboratory for Microbiome Sciences RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan. Integrated Institute for Regulatory Science Waseda University Tokyo 169-8555 Japan. Structural and Computational Biology Unit European Molecular Biology Laboratory Heidelberg Germany.

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Topology analysis for anomalous Hall effect in the noncollinear antiferromagnetic states of Mn3AN (A=Ni, Cu, Zn, Ga, Ge, Pd, In, Sn, Ir, Pt)

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Physical Review B 2019年第9期100卷 094426-094426页

作者： Vu Thi Ngoc Huyen Michi-To Suzuki Kunihiko Yamauchi Tamio Oguchi Institute of Scientific and Industrial Research Osaka University 8-1 Mihogaoka Ibaraki Osaka 567-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan Graduate School of Engineering Science Osaka University Toyonaka Osaka 560-8531 Japan Center for Computational Materials Science Institute for Materials Research Tohoku University Sendai Miyagi 980-8577 Japan

We investigate topological features of electronic structures which produce large anomalous Hall effect in the noncollinear antiferromagnetic metallic states of antiperovskite manganese nitrides by first-principles calculations. We first predict the stable magnetic structures of these compounds to be noncollinear antiferromagnetic structures characterized by either T1g or T2g irreducible representation by evaluating the total energy for all of the magnetic structures classified according to the symmetry and multipole moments. The topology analysis is next performed for the Wannier tight-binding models obtained from the first-principles band structures. Our results reveal the small Berry curvature induced through the coupling between occupied and unoccupied states with the spin-orbit coupling, which is widely spread around the Fermi surface in the Brillouin zone, dominantly contributes after the k-space integration to the anomalous Hall conductivity, while the local divergent Berry curvature around Weyl points has a rather small contribution to the anomalous Hall conductivity.

关键词： Anomalous Hall effect Antiferromagnetism First-principles calculations Topology

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