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PB-09Alpha-synuclein Binds Unconventional Microtubules That Have a Unique Function

Microscopy 2017年第SUPPL_1期66卷 i37-i37页

作者： Toba, Shiori Jin, Mingyue Yamada, Masami Matsumoto, Sakiko Yasunaga, Takuo Fukunaga, Yuko Miyazawa, Atsuo Kojima, Hiroaki Arai, Yoshiyuki Nagai, Takeharu Hirotsune, Shinji Osaka City Univ. Grad. Sch. of Medicine Present address: Dept. of Emergency Medical Technology Hirosaki University of Health and Welfare Junior College Faculty of Computer Science and Systems Engineering Kyushu Inst. of Technology Fukuoka Japan JST-SENTAN JST-CREST Grad. Sch. of Life Science Univ. of Hyogo RSC-Univ. of Hyogo Leading Program Center RIKEN SPring-8 Center Advanced ICT Research Inst. National Inst. of Information and Communications Technology and Inst. of Scientific and Industrial Research Osaka Univ. Japan.

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Integrability of Goldilocks quantum cellular automata

arXiv

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arXiv 2024年

作者： Hillberry, Logan E. Piroli, Lorenzo Vernier, Eric Halpern, Nicole Yunger Prosen, Tomaž Carr, Lincoln D. Department of Physics The University of Texas at Austin AustinTX78712 United States Dipartimento di Fisica e Astronomia Università di Bologna INFN Sezione di Bologna via Irnerio 46 BolognaI-40126 Italy Laboratoire de Probabilités Statistique et Modélisation CNRS Université Paris Cité Sorbonne Université Paris France Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Faculty of Mathematics and Physics University of Ljubljana Jadranska 19 LjubljanaSI-1000 Slovenia Institute of Mathematics Physics and Mechanics Jadranska 19 LjubljanaSI-1000 Slovenia Quantum Engineering Program Colorado School of Mines GoldenCO80401 United States Department of Physics Colorado School of Mines GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States

Goldilocks quantum cellular automata (QCA) have been simulated on quantum hardware and produce emergent small-world correlation networks. In Goldilocks QCA, a single-qubit unitary is applied to each qubit in a one-dimensional chain subject to a balance constraint: a qubit is updated if its neighbors are in opposite basis states. Here, we prove that a subclass of Goldilocks QCA—including the one implemented experimentally—map onto free fermions and therefore can be classically simulated efficiently. We support this claim with two independent proofs, one involving a Jordan–Wigner transformation and one mapping the integrable six-vertex model to QCA. We compute local conserved quantities of these QCA and predict experimentally measurable expectation values. These calculations can be applied to test large digital quantum computers against known solutions. In contrast, typical Goldilocks QCA have equilibration properties and quasienergy-level statistics that suggest nonintegrability. Still, the latter QCA conserve one quantity useful for error mitigation. Our work provides a parametric quantum circuit with tunable integrability properties with which to test quantum hardware. © 2024, CC BY.

关键词： Qubits

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Perceptron Partition Model to Minimize Input Matrix

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IOP Conference Series: Materials science and Engineering 2019年第1期536卷

作者： Zulfian Azmi Mahyuddin K.M. Nasution Muhammad. Zarlis Herman Mawengkang Syahril Efendi Postgraduate Doctoral of Computer Science Universitas Sumatera Utara Padang Bulan USU Medan 20155 Indonesia. Information Technology Department Universitas Sumatera Utara Padang Bulan USU Medan 20155 Indonesia. Program Pasca Sarjana Doktoral Ilmu Komputer Universitas Sumatera Utara Indonesia. Department of Mathematics Universitas Sumatera Utara Padang BulanUSU Medan20155 Indonesia.

Implementation of Neuron Network model using Perceptron has not given optimal result in real time learning. The large number of inputs expressed in matrix form makes the process slower in pattern recognition. So, it takes characteristic to represent all the input matrices by using the partition method. By partitioning each input and with the best weight and using the activation function will produce an output value. And learning is valid in recognizing patterns only 1 iteration only. Further validation is done on the water mill control module with dissolved oxygen input, water pH, salinity and water temperature. With Perceptron Partition learning algorithm more real-time than perceptron model. Testing on the waterwheel input whether rotating or stopping by Matrix Laboratory software simulation.

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Nanoinformatics workshop report: Current resources, community needs and the proposal of a collaborative framework for data sharing and information integration

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Computational science and Discovery 2013年第1期6卷 14008页

作者： Harper, Stacey L. Hutchison, James E. Baker, Nathan Ostraat, Michele Tinkle, Sally Steevens, Jeffrey Hoover, Mark D. Adamick, Jessica Rajan, Krishna Gaheen, Sharon Cohen, Yoram Nel, Andre Cachau, Raul E. Tuominen, Mark Department of Environmental and Molecular Toxicology Oregon State University Corvallis OR United States School of Chemical Biological and Environmental Engineering Oregon State University Corvallis OR United States Department of Chemistry University of Oregon Eugene OR United States Pacific Northwest National Laboratory Richland WA United States RTI International Research Triangle Park NC United States Science and Technology Policy Institute Washington DC United States US Army Engineer Research and Development Center Vicksburg MS United States National Institute for Occupational Safety and Health Morgantown WV United States University Libraries University of Michigan Amherst MA United States Department of Materials Science and Engineering Iowa State University Ames IA United States SAIC Frederick Frederick MD United States Department of Chemical and Biomolecular Engineering University of California Los Angeles CA United States Center for Environmental Implications of Nanotechnology University of California Los Angeles CA United States Advanced Biomedical Computer Center Information Systems Program Frederick National Laboratory for Cancer Research Frederick MD United States Physics Department University of Massachusetts Amherst MA United States

The quantity of information on nanomaterial properties and behavior continues to grow rapidly. Without a concerted effort to collect, organize and mine disparate information coming out of current research efforts, the value and effective use of this information will be limited at best. Data will not be translated to knowledge. At worst, erroneous conclusions will be drawn and future research may be misdirected. Nanoinformatics can be a powerful approach to enhance the value of global information in nanoscience and nanotechnology. Much progress has been made through grassroots efforts in nanoinformatics resulting in a multitude of resources and tools for nanoscience researchers. In 2012, the nanoinformatics community believed it was important to critically evaluate and refine currently available nanoinformatics approaches in order to best inform the science and support the future of predictive nanotechnology. The Greener Nano 2012: Nanoinformatics Tools and Resources Workshop brought together informatics groups with materials scientists active in nanoscience research to evaluate and reflect on the tools and resources that have recently emerged in support of predictive nanotechnology. The workshop goals were to establish a better understanding of current nanoinformatics approaches and to clearly define immediate and projected informatics infrastructure needs of the nanotechnology community. The theme of nanotechnology environmental health and safety (nanoEHS) was used to provide real-world, concrete examples on how informatics can be utilized to advance our knowledge and guide nanoscience. The benefit here is that the same properties that impact the performance of products could also be the properties that inform EHS. From a decision management standpoint, the dual use of such data should be considered a priority. Key outcomes include a proposed collaborative framework for data collection, data sharing and information integration. © 2013 IOP Publishing Ltd.

关键词： Data integration

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Correction to: Pharmacological Rescue With SR8278, A Circadian Nuclear Receptor REV-ERBα Antagonist As A Therapy for Mood Disorders In Parkinson's Disease

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Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 2022年第4期19卷 1432页

作者： Jeongah Kim Inah Park Sangwon Jang Mijung Choi Doyeon Kim Woong Sun Youngshik Choe Ji-Woong Choi Cheil Moon Sung Ho Park Han Kyoung Choe Kyungjin Kim Department of Brain Sciences Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Korea. Department of Anatomy College of Medicine Korea University Seoul Korea. Program in Neurosciences & Mental Health The Hospital for Sick Children Toronto ON Canada. Korea Brain Research Institute (KBRI) Daegu Korea. Department of Electrical Engineering and Computer Science DGIST Daegu Korea. Convergence Research Advanced Centre for Olfaction DGIST Daegu Korea. Department of Biological Sciences Ulsan National Institute of Science and Technology (UNIST) Ulsan 44919 Korea. Department of Brain Sciences Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Korea. kyungjin@dgist.ac.kr. Convergence Research Advanced Centre for Olfaction DGIST Daegu Korea. kyungjin@dgist.ac.kr.

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An Open-Source Knowledge Graph Ecosystem for the Life sciences

arXiv

引用

arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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Quantum computing at the frontiers of biological sciences

arXiv

引用

arXiv 2019年

作者： Emani, Prashant S. Warrell, Jonathan Anticevic, Alan Bekiranov, Stefan Gandal, Michael McConnell, Michael J. Sapiro, Guillermo Aspuru-Guzik, Alán Baker, Justin Bastiani, Matteo McClure, Patrick Murray, John Sotiropoulos, Stamatios N. Taylor, Jacob Senthil, Geetha Lehner, Thomas Gerstein, Mark B. Harrow, Aram W. Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Yale School of Medicine Department of Psychiatry New HavenCT06511 United States University of Virginia School of Medicine Department of Biochemistry and Molecular Genetics CharlottesvilleVA22903 United States Department of Psychiatry Semel Institute David Geffen School of Medicine University of California–Los Angeles 695 Charles E. Young Drive South Los AngelesCA90095 United States Univ. of Virginia School of Medicine Department of Neuroscience CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry University of Toronto 80 St. George Street TorontoONM5S 3H6 Canada TorontoONM5S 1M1 Canada CIFAR Artificial Intelligence Research Chair Vector Institute TorontoONM5S 1M1 Canada Department of Computer Science University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada Schizophrenia and Bipolar Disorder Program McLean Hospital BelmontMA02478 United States Department of Psychiatry Harvard Medical School BostonMA02114 United States Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham NottinghamNG7 2RD United Kingdom Machine Learning Team National Institute of Mental Health BethesdaMD20892 United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD20892 United States Department of Psychiatry Yale University School of Medicine New HavenCT06511 United States Department of Physics Yale University New HavenCT06511 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States National Institute of Standards and Technology GaithersburgMD20899 United States Office of Genomics Research Coordination N

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Author Correction: Rare variant analyses in 51,256 type 2 diabetes cases and 370,487 controls reveal the pathogenicity spectrum of monogenic diabetes genes

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Nature genetics 2025年第4期57卷 1060页

作者： Alicia Huerta-Chagoya Philip Schroeder Ravi Mandla Jiang Li Lowri Morris Maheak Vora Ahmed Alkanaq Dorka Nagy Lukasz Szczerbinski Jesper G S Madsen Silvia Bonàs-Guarch Fanny Mollandin Joanne B Cole Bianca Porneala Kenneth Westerman Josephine H Li Toni I Pollin Jose C Florez Anna L Gloyn David J Carey Inês Cebola Uyenlinh L Mirshahi Alisa K Manning Aaron Leong Miriam Udler Josep M Mercader Programs in Metabolism and Medical & Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Diabetes Unit Massachusetts General Hospital Boston MA USA. Graduate Program in Genomics and Computational Biology University of Pennsylvania Philadelphia PA USA. Department of Genomic Health Geisinger Danville PA USA. Section of Genetics and Genomics Department of Metabolism Digestion and Reproduction Imperial College London London UK. National Heart and Lung Institute Faculty of Medicine London UK. Department of Endocrinology Diabetology and Internal Medicine Medical University of Bialystok Bialystok Poland. Clinical Research Centre Medical University of Bialystok Bialystok Poland. Institute of Mathematics and Computer Science University of Southern Denmark Odense Denmark. The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard Cambridge MA USA. Department of Biochemistry and Molecular Biology University of Southern Denmark Odense Denmark. Centre for Genomic Regulation The Barcelona Institute of Science and Technology Barcelona Spain. Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas Madrid Spain. Department of Metabolism Digestion and Reproduction Imperial College London London UK. Department of Medicine Harvard Medical School Boston MA USA. Division of Endocrinology Boston Children's Hospital Boston MA USA. Department of Biomedical Informatics University of Colorado School of Medicine Aurora CO USA. Division of General Internal Medicine Massachusetts General Hospital Boston MA USA. Department of Medicine Massachusetts General Hospital Boston MA USA. University of Maryland School of Medicine Baltimore MD USA. Endocrine Division Massachusetts General Hospital Boston MA USA. Department of Pediatrics Division of Endocrinology Stanford School of Medicine Stanford

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Quantifying myelin in brain tissue using color spatial light interference microscopy (cSLIM)

arXiv

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arXiv 2020年

作者： Fanous, Michael Caputo, Megan P. Lee, Young Jae Rund, Laurie A. Best-Popescu, Catherine Kandel, Mikhail E. Johnson, Rodney W. Das, Tapas Kuchan, Matthew J. Popescu, Gabriel Quantitative Light Imaging Laboratory Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Bioengineering University of Illinois at Urbana-Champaign 306 N. Wright Street UrbanaIL61801 United States Division of Nutritional Sciences University of Illinois Urbana-ChampaignIL61801 United States Neuroscience Program University of Illinois at Urbana-Champaign UrbanaIL61801 United States Laboratory of Integrative Immunology & Behavior Department of Animal Sciences University of Illinois Urbana-ChampaignIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign 306 N. Wright Street UrbanaIL61801 United States Abbott Nutrition Discovery Research ColumbusOH43219 United States Abbott Nutrition Strategic Research 3300 Stelzer Road ColumbusOH United States

Deficient myelination of the brain is associated with neurodevelopmental delays, particularly in high-risk infants, such as those born small in relation to their gestational age (SGA). New methods are needed to further study this condition. Here, we employ Color Spatial Light Interference Microscopy (cSLIM), which uses a brightfield objective and RGB camera to generate pathlength-maps with nanoscale sensitivity in conjunction with a regular brightfield image. Using tissue sections stained with Luxol Fast Blue, the myelin structures were segmented from a brightfield image. Using a binary mask, those portions were quantitatively analyzed in the corresponding phase maps. We first used the CLARITY method to remove tissue lipids and validate the sensitivity of cSLIM to lipid content. We then applied cSLIM to brain histology slices. These specimens are from a previous MRI study, which demonstrated that appropriate for gestational age (AGA) piglets have increased internal capsule myelination (ICM) compared to small for gestational age (SGA) piglets and that a hydrolyzed fat diet improved ICM in both. The identity of samples was blinded until after statistical analyses. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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