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Correction: Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes

Genetics in medicine : official journal of the American College of Medical Genetics 2020年第8期22卷 1427页

作者： Willie H Chang Pouria Mashouri Alexander X Lozano Brittney Johnstone Mia Husić Annie Olry Sylvie Maiella Tugce B Balci Sarah L Sawyer Peter N Robinson Ana Rath Michael Brudno Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada. Department of Computer Science Princeton University Princeton NJ USA. Faculty of Medicine University of Toronto Toronto ON Canada. Department of Materials Science & Engineering Stanford University Stanford CA USA. Sunnybrook Health Sciences Centre Toronto ON Canada. Orphanet Institut national de la santé et de la recherche médicale Paris France. Medical Genetics Program of Southwestern Ontario London Health Sciences Centre London ON Canada. Department of Genetics Children's Hospital of Eastern Ontario and Children's Hospital of Eastern Ontario Research Institute University of Ottawa Ottawa ON Canada. The Jackson Laboratory for Genomic Medicine Farmington CT USA. Institute for Systems Genomics University of Connecticut Farmington CT USA. Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada. brudno@cs.toronto.edu. Department of Computer Science University of Toronto Toronto ON Canada. brudno@cs.toronto.edu. Genetics and Genome Biology Program The Hospital for Sick Children Toronto ON Canada. brudno@cs.toronto.edu. University Health Network Toronto ON Canada. brudno@cs.toronto.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks

arXiv

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arXiv 2017年

作者： Harirchi, Farshad Kim, Doohyun Khalil, Omar Liu, Sijia Elvati, Paolo Hero, Alfred Violi, Angela Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2125 United States Department of Mechanical Engineering University of Michigan Ann ArborMI48109-2125 United States Biophysics Program Departments of Chemical Engineering Biomedical Engineering Macromolecular Science and Engineering University of Michigan Ann ArborMI48109-2125 United States

Chemical kinetic mechanisms can be represented by sets of elementary reactions that are easily translated into mathematical terms using physicochemical relationships. The schematic representation of reactions captures the interactions between reacting species and products. Determining the minimal chemical interactions underlying the dynamic behavior of systems is a major task. In this paper, we introduce a novel approach for the identification of the inuential reactions in chemical reaction networks for combustion applications, using a data-driven sparse-learning technique. The proposed approach identifies a set of inuential reactions using species concentrations and reaction rates, with minimal computational cost without requiring additional data or simulations. The new approach is applied to analyze the combustion chemistry of H2andC3H8in a constant-volume homogeneous reactor. The inuential reactions identified by the sparse-learning method are consistent with the current kinetics knowledge of chemical mechanisms. Additionally, we show that a reduced version of the parent mechanism can be generated as a combination of the inuential reactions identified at different times and conditions and that for both H2andC3H8this reduced mechanism performs closely to the parent mechanism as a function of ignition delay over a wide range of conditions. Our results demonstrate the potential of the sparse-learning approach as an effective and efficient tool for mechanism analysis and mechanism reduction. Copyright © 2017, The Authors. All rights reserved.

关键词： Learning systems

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Design of an exchange information component of O/D matrix for mass transportation systems based on DATEX II approach 8

Design of an exchange information component of O/D matrix fo...

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8th Euro American Conference on Telematics and Information Systems, EATIS 2016

作者： Melo-Castillo, Angie Canon-Lozano, Yeimmy Herrera-Quintero, Luis Felipe Bureš, Petr Banse, Klaus Czech Technical University in Prague Faculty of Transportation Sciences Czech Republic Computer Science Program Telecomunication Engineering Program Universidad Piloto de Colombia Colombia ITS Colombia Colombia

ISBN: (纸本)9781509024360

Nowadays, the mobility is a growing problem that worries the governments in Latin American countries. In fact, to improve the mobility the transportation planning process and the ITS technologies are key areas, which should work together to give solutions to solve or mitigate the problem. One of the most used tools in transportation planning to face this problem is the origin/destination (O/D) matrix, which is used to estimate the moving of users and future demand on transportation systems. Nevertheless, the current technologies for the generation of traffic data information, the matrix O/D in this case, create a new problem for handle the heterogeneous data especially for the traffic centers, which use these data for traffic management. The novelty of this paper is focused on the design of an exchange information component, which send the O/D matrix information to the traffic centers based on DATEX II approach. © 2016 IEEE.

关键词： Matrix algebra

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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

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Evolutionary computation and swarm intelligence for the inference of gene regulatory networks

Evolutionary computation and swarm intelligence for the infe...

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作者： Hattori, Leandro Takeshi Lopes, Heitor Silvério Lopes, Fabrício Martins Graduate Program in Electrical and Computer Engineering Federal University of Technology ParanáCuritiba Brazil Graduate Program in Bioinformatics Federal University of Technology ParanáCornélio Procópio Brazil

The inference of gene regulatory networks (GRNs) from expression profiles is still an important challenge in bioinformatics research. The main difficulty of this problem is associated to the huge number of genes and the small number of samples available, as well as the intrinsic noise in the data acquisition process. In this context, this paper presents a feature selection approach to the identification of GRNs using optimisation strategies from evolutionary computation and swarm intelligence. As a case-study we used an artificial gene network (AGN) based on the scale-free topology. This AGN has 1,000 genes and was simulated with 500 temporal expression samples. The methods compared were: differential evolution (DE), bat algorithm (BAT) and artificial bee colony (ABC) algorithms. All algorithms used their standard control parameters and the same criterion function: the mean conditional entropy (MCE). This is an information theory measure, commonly adopted for various feature selection problems in the pattern recognition research field. The results showed that DE algorithm leaded to the best results than BAT and ABC in all comparisons, and the inferred network was more similar to the original network. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Evolutionary algorithms

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Design of an Autonomous Aerator

Design of an Autonomous Aerator

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IEEE International Conference on Emerging Technologies and Factory Automation

作者： Rilene Carolina Goelzer Letieri ávila Deliel Oliveira Vinícius M. de Oliveira Control and Automation Engineering Federal University of Rio Grande Rio Grande RS Brasil Graduate Program in Computer Engineering Federal University of Rio Grande Rio Grande RS Brasil Universidade Federal do Rio Grande Rio Grande RS BR

Aquaculture has resorted to the use of technology to make control of important variables and improve production. There were two problems observed in the Marine Aquaculture Station - EMA of the Federal University of Rio Grande, more specifically related to the aeration of the culture tanks to pisciculture and carniciculture. Firstly, the high energy consumption of the paddle aerators was identified, due to the continuous need to oxygenate the water. Subsequently, it was noted that static aerators do not aerate the water evenly and they require that their placement be done manually by technicians or assistants. Thus, the article proposes a design of an autonomous aerator with electric power coming from a photovoltaic solar energy system. In the sequence, a navigation system for the aerator is proposed along the tank, based on a differential performance of the motors connected to the paddle.

关键词： Solar Energy Aquaculture Aerator Control

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Galaxy clusters at z∼1 imaged by ALMA with the Sunyaev-Zel’dovich effect

arXiv

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arXiv 2022年

作者： Kitayama, Tetsu Ueda, Shutaro Okabe, Nobuhiro Akahori, Takuya Hilton, Matt Hughes, John P. Ichinohe, Yuto Kohno, Kotaro Komatsu, Eiichiro Lin, Yen-Ting Miyatake, Hironao Oguri, Masamune Sifón, Cristóbal Takakuwa, Shigehisa Takizawa, Motokazu Tsutsumi, Takahiro van Marrewijk, Joshiwa Wollack, Edward J. Department of Physics Toho University Chiba Funabashi274-8510 Japan No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Department of Physics Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Core Research for Energetic Universe Hiroshima University Department of Physics Hiroshima University 1-3-1 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Physics Program Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Mizusawa VLBI observatory National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Operation Division SKA Observatory Jodrell Bank Lower Withington MacclesfieldSK11 9DL United Kingdom Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa Wits Centre for Astrophysics School of Physics University of the Witwatersrand Private Bag 3 Johannesburg2050 South Africa Department of Physics and Astronomy Rutgers The State University of New Jersey PiscatawayNJ08854-8019 United States Department of Physics Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo171-8501 Japan Institute of Astronomy School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Research Center for the Early Universe School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Max-Planck-Institut für Astrophysik Karl-Schwarzschild Str. 1 GarchingD-85741 Germany The University of Tokyo Institutes for Advanced Study The University of Tokyo 5-1-5 Kashiwanoha Chiba277-8583 Japan Nagoya University Nagoya464-8

We present high angular-resolution measurements of the thermal Sunyaev-Zel’dovich effect (SZE) toward two galaxy clusters, RCS J2319+0038 at z = 0.9 and HSC J0947−0119 at z = 1.1, by the Atacama Large Millimeter/submillimeter Array (ALMA) in Band 3. They are supplemented with available Chandra X-ray data, optical data taken by Hyper Suprime-Cam on Subaru, and millimeter-wave SZE data from the Atacama Cosmology Telescope. Taking into account departures from spherical symmetry, we have reconstructed non-parametrically the inner pressure profile of two clusters as well as electron temperature and density profiles for RCS J2319+0038. This is one of the first such measurements for an individual cluster at z >∼ 0.9. We find that the inner pressure profile of both clusters is much shallower than that of local cool-core clusters. Our results consistently suggest that RCS J2319+0038 hosts a weak cool core, where radiative cooling is less significant than in local cool cores. On the other hand, HSC J0947−0119 exhibits an even shallower pressure profile than RCS J2319+0038 and is more likely a non-cool-core cluster. The SZE centroid position is offset by more than 140 h−170kpc from the peaks of galaxy distribution in HSC J0947−0119, suggesting a stronger influence of mergers in this cluster. We conclude that these distant clusters are at a very early stage of developing the cool cores typically found in clusters at lower redshifts. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

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

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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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Practical design of PID-type controllers with constraints 123

Practical design of PID-type controllers with constraints

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123rd ASEE Annual Conference and Exposition

作者： Tzouanas, Vassilios Falar, Steely Earl Varon University of Houston Computer Science and Engineering Technology Downtown HoustonTX United States Controls and Instrumentation Engineering Technology Program University of Houston Downtown United States

The proportional-integral-derivative (PID) controller has extensively been used in the process industries and is taught in most undergraduate engineering and engineering technology programs. Various PID design/tuning methods have been proposed over the years such as the famous Ziegler-Nichols method, the Internal Model Control (IMC) method, and the many variations of it. Given a process model, these methods estimate values for the PID tuning parameters: proportional gain, integral time, and derivative time. Many of the tuning methods include a tunable parameter, for instance the filter time constant in the case of the IMC method that the user must "tune". Furthermore, none of these techniques considers process constraints in the PID design. However, from a practical viewpoint, process and final control element constraints must be accounted for. Recently, a methodology based on has been developed to design PID controllers subject to controlled variable as well as manipulated variable (size and rate) constraints while a performance criterion is optimized. This paper extends this methodology to determine the "tunable" parameters of other PID design methods while process and equipment constraints are satisfied. Estimation of the IMC filter time constant is considered. Simulation and experimental results demonstrate the practicality of the new PID design method. © American Society for engineering Education, 2016.

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Modular real-time spectroscopy instrument 54

Modular real-time spectroscopy instrument

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54th Annual Rocky Mountain Bioengineering Symposium, RMBS 2017 and 54th International ISA Biomedical Sciences Instrumentation Symposium 2017

作者： Striker, Ryan Vetter, Andrew Weberg, Christopher Staab, Stacy Kaste, Branden Schroeder, Meredith Hasan, Raquib Nodland, Jordan Brooks, Amanda E. Brooks, Ben D. Ewert, Daniel L. Department of Electrical and Computer Engineering North Dakota State University FargoND58102 United States Department of Mechanical Engineering North Dakota State University FargoND58102 United States Materials and Nanotechnology Program North Dakota State University FargoND58102 United States Department of Microbiology North Dakota State University FargoND58102 United States Department of Pharmaceutical Sciences North Dakota State University FargoND58102 United States

ISBN: (纸本)9781945541193

Whether researching new antibiotics, cancer treatments, or biofilm inhibitors, spectroscopy affords unique insight into nanoscale biochemical processes. Spectroscopy allows both the quantification and characterization of proteins, enzymes, DNA, and bacteria. Existing lab devices contain advanced optical systems which support selection of wavelengths. However, shortcomings of present equipment include high cost, the assumption of an on-site operator at each measurement interval, and limited sensitivity under particular circumstances. Furthermore, existing optical systems are incompatible with hybrid experimental arrangements such as automated fluid exchange or sample exposure to electromagnetic fields. A modular spectroscopy system is presented, centered around the popular Raspberry Pi single-board computer, which may be configured for multiple spectroscopy readings, including absorbance and fluorescence. Data collection on the Raspberry Pi is superior to existing systems because readings are automatically taken at a programmable interval, and the results are remotely available in real time. Mechanical flexibility is achieved by housing the sample cuvette in a 3D printed plastic block. Electromagnetic energy easily passes through the plastic material to reach the sample, and optoelectronics or fluidics may access the cuvette through identical ports on each of the block's six faces. Finally, the block can be modified and reprinted in a single day if new features are desired. The modular ports enable quantifiable interdisciplinary experiments such as real-time measurement of both drug concentration and bacterial inhibition;researchers may monitor a drug's release kinetics from a polymer matrix (absorbance) while also measuring the drug's inhibitory effect on bacterial growth (fluorescence). Copyright 2017, ISA All Rights Reserved.

关键词： Absorbance

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