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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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Escada: Predicting Virtual Machine Network Bandwidth Demands for Elastic Provisioning in IaaS Clouds 4

Escada: Predicting Virtual Machine Network Bandwidth Demands...

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4th IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017

作者： Marques, Jonatas Adilson Obelheiro, Rafael Rodrigues Institute of Informatics Federal University of Rio Grande Do sul Porto Alegre Brazil Graduate Program in Applied Computing Department of Computer Science Santa Catarina State University Joinville Brazil

ISBN: (纸本)9781538619391

In Infrastructure as a Service clouds, customers lease virtual resources (e.g., CPU, memory, network) offered by cloud providers, paying for the allocated capacity of resources, regardless of their effective use. In this context, it is in the interest of the customers to reserve resources with sufficient capacity so that their applications achieve good performance while, at the same time, minimizing their expenses with idle capacities. The correct provisioning of resources is also invaluable to providers, who seek to maximize the resource usage of their physical infrastructures, avoiding spending maintenance costs with unused hardware such as high capacity servers and networking devices. We introduce Escada, a novel model for network bandwidth prediction to guide the provisioning of virtual machines. Escada is application-oblivious and dynamically adapts to changes in a virtual machine workload profile. The network flows of a virtual machine are monitored and used to estimate its total required bandwidth over a time period. A prediction algorithm is then applied to the entire history of estimates to predict the virtual machine future required bandwidth. Our experimental evaluation shows that the estimates provided by Escada are useful for guiding the prediction of network bandwidth requirements, providing accurate bandwidth estimates even when the network is under-provisioned. Furthermore, Escada correctly adjusts its prediction algorithm faster than the state-of-the-art model and predicts more reliably during the adjusting period. © 2017 IEEE.

关键词： Virtual machine

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College 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 Ne

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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

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NATURE 2023年第7948期614卷 E42-E42页

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

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Geometry-based data generation

arXiv

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arXiv 2018年

作者： Lindenbaum, Ofir Stanley, Jay S. Wolf, Guy Krishnaswamy, Smita Department of Genetics Yale University New HavenCT United States Applied Mathematics Program Yale University New HavenCT United States Computational Biology & Bioinformatics Program Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States

We propose a new type of generative model of high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, and improves several downstream data analysis tasks such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations and reveal intrinsic patterns and mutual-information relationships between genes on a single cell dataset of hematopoiesis. Copyright © 2018, The Authors. All rights reserved.

关键词： Clustering algorithms

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The redshirt in engineering consortium: Progress and early insights 125

The redshirt in engineering consortium: Progress and early i...

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125th ASEE Annual Conference and Exposition

作者： Riskin, Eve A. Milford, Jana Callahan, Janet Cosman, Pamela Schneider, John B. Pitts, Kevin Knaphus-Soran, Emily Llewellyn, Donna C. Delaney, Ann E. Myers, Beth A. Tetrick, Katherine Christine Cunningham, Sonya Ennis, Tanya D. O'Connor, Kevin Ferrez, Michelle Pan, Tiffany D. Baldis, Jessica EE Department University of Washington United States Engineering GoldShirt Program University of Colorado Boulder United States Micron School of Materials Science and Engineering Boise State University United States Department of Electrical and Computer Engineering University of California San Diego United States Voiland College of Engineering and Architecture United States Department of Physics University of Illinois Urbana-Champaign United States University of Washington United States Institute for STEM and Diversity Initiatives Boise State University United States Public Policy and Administration Boise State University United States Department of Analytics Assessment and Accreditation University of Colorado Boulder United States Program Washington State University United States University of Washington STARS Program Diversity and Access College of Engineering United States Engineering GoldShirt Program University of Colorado Boulder College of Engineering and Applied Science United States Department of Educational Psychology and Learning Sciences University of Colorado Boulder United States United States

The NSF-funded Redshirt in Engineering Consortium was formed in 2016 with the goal of enhancing the ability of academically talented but underprepared students coming from low-income backgrounds to successfully graduate with engineering degrees. The Consortium takes its name from the practice of redshirting in college athletics, with the idea of providing an extra year and support to help promising engineering students complete a bachelor's degree. The Consortium builds on the success of three existing "academic redshirt" programs and expands the model to three new schools. The Existing Redshirt Institutions (ERIs) help mentor and train the new Student Success Partners (SSPs), and SSPs contribute their unique expertise to help ERIs improve existing Redshirt programs. The Redshirt model consists of seven main programmatic components aimed at improving the engagement, retention, and graduation of students underrepresented in engineering. These components include: "intrusive" academic advising and support services, an intensive first-year academic curriculum, community-building (including pre-matriculation summer programs), career awareness and vision, faculty mentorship, NSF S-STEM scholarships, and second-year support. Successful implementation of these activities is intended to produce two main long-term outcomes: a six-year graduation rate of 60%-75% for Redshirt students, and increased rates of enrollment and graduation of Pell-eligible, URM, and women students in engineering at participating universities. In the first year of the grant (AY 16-17), SSPs developed their own Redshirt programs, hired and trained staff, and got their programs off the ground. ERIs implemented faculty mentorship programs and expanded support to Redshirt students into their sophomore year. In the second year (AY 17-18), Redshirt programs were expanded at the ERIs while SSPs welcomed their first cohorts of Redshirt students. This Work in Progress paper describes the Redshirt programs at e

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A metasurface-inspired focusing collector for concentrated solar power applications

A metasurface-inspired focusing collector for concentrated s...

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Frontiers in Optics, FIO 2018

作者： Ding, Qing Barna, Shama Jacobs, Kyle Choubal, Aakash Mensing, Glennys Zhang, Zhong Yamada, Kaito Tirawat, Robert Kincaid, Nicholas Zhu, Guangdong Wendelin, Tim Guo, L. Jay Ferreira, Placid Toussaint, Kimani C. University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2102 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States National Renewable Energy Lab. GoldenCO80401 United States Solar Dynamics LLC United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We present a simple metasurface-inspired planar focusing collector for concentrated solar power. Fabrication is achieved using two-photon lithography, and subsequent nanoimprint lithography tests for scalability. Opti... 详细信息

ISBN: (纸本)9781943580460

关键词： Solar energy

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Valley-selective optical Stark effect probed by Kerr rotation

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Physical Review B 2018年第4期97卷 045307-045307页

作者： Trevor LaMountain Hadallia Bergeron Itamar Balla Teodor K. Stanev Mark C. Hersam Nathaniel P. Stern Applied Physics Program Northwestern University Evanston Illinois 60208 USA Department of Materials Science and Engineering Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Chemistry Northwestern University Evanston Illinois 60208 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA

The ability to monitor and control distinct states is at the heart of emerging quantum technologies. The valley pseudospin in transition metal dichalcogenide (TMDC) monolayers is a promising degree of freedom for such control, with the optical Stark effect allowing for valley-selective manipulation of energy levels in WS2 and WSe2 using ultrafast optical pulses. Despite these advances, understanding of valley-sensitive optical Stark shifts in TMDCs has been limited by reflectance-based detection methods where the signal is small and prone to background effects. More sensitive polarization-based spectroscopy is required to better probe ultrafast Stark shifts for all-optical manipulation of valley energy levels. Here, we show time-resolved Kerr rotation to be a more sensitive probe of the valley-selective optical Stark effect in monolayer TMDCs. Compared to the established time-resolved reflectance methods, Kerr rotation is less sensitive to background effects. Kerr rotation provides a fivefold improvement in the signal-to-noise ratio of the Stark effect optical signal and a more precise estimate of the energy shift. This increased sensitivity allows for observation of an optical Stark shift in monolayer MoS2 that exhibits both valley and energy selectivity, demonstrating the promise of this method for investigating this effect in other layered materials and heterostructures.

关键词： Dielectric properties Excitons Semiconductors Transition metal dichalcogenides Kerr effect Reflectivity

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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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