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arXiv 2017年

作者： LaMountain, Trevor Bergeron, Hadallia Balla, Itamar Stanev, Teodor K. Hersam, Mark C. Stern, Nathaniel P. Applied Physics Program Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL60208 United States

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 five-fold 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. Copyright © 2017, The Authors. All rights reserved.

关键词： Landforms

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Use of hand biometric information in gender identification: Integration of anthropometric and electromyographic data

Use of hand biometric information in gender identification: ...

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Human Factors and Ergonomics Society 2016 International Annual Meeting, HFES 2016

作者： Jee, Soo-Chan Rhie, Ye Lim Kim, Minjee Kim, Yong Min Ahn, Sung Hee Yun, Myung Hwan Republic of Korea Air Force Headquarters Chungnam132827 Korea Republic of Department of Industrial Engineering Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Cognitive Science Seoul National University Seoul08826 Korea Republic of

These days, mobile devices are expected to act as biometric readers due to their increasing number of sensors. However, few studies have suggested a method to identify the user's demographic profile by collecting biometric information through smart devices, whereas demographic characteristics are an important factor that determines the overall preferences of the interface and content. Therefore, this paper suggests a method to identify the user's gender based on the biometric information of users. Since the hand is one of the most frequently used body parts in touch interfaces, four hand dimensions and the EMG activities of four hand muscles were measured. The hand length, hand breadth, hand thickness, and hand circumference of 321 Koreans were collected as hand dimensions, and the EMG activities of 27 participants when using mock-ups of mobile phones were observed. As a result of discriminant analysis, hand dimensions could predict males and females with over 75% accuracy, while %MVC showed relatively lower accuracy. The results revealed that simple hand dimensions are a more efficient method to recognize the gender of users. In a further study, other biometric data could be utilized in sensing various types of demographic information considering that identification technologies are growing fast. Copyright 2016 by Human Factors and Ergonomics Society.

关键词： Discriminant analysis

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BHEL Smart Wall Blowing System: New Product Development in Manufacturing Industry

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Vikalpa 2021年第4期45卷 240 - 249页

作者： Abhishek Kumar R. Dhanuskodi R. Kaliappan K. Nandakumar Abhishek Kumar teaches design philosophies at Anant National University Ahmedabad. He earned his Ph.D in Management from Pondicherry University. He is an Economics graduate from Calcutta University and MBA from BIM Trichy. He has published more than 20 articles in reputed international journals has authored two books written articles and columns for newspapers and is quoted on issues related to leadership and marketing by various media platforms. His research work comprises construction of brand personality scale for media aesthetics and phenomenological design. His recent publications are on philosophy of a photograph hermeneutic reality of product and on philosophy of intimate spaces. R. Dhanuskodi has nearly 40 years of R&D experience at BHEL India in technical areas applicable for thermal power plants. He is a life member of The Institution of Engineers (India) and The Combustion Institute. He has won two BHEL’s Excel awards under the best author category for technical papers. He has visited France Netherlands and Germany under Indo-Europe Clean Coal Development Program. He has guided 42 UG PG and PhD project works. He holds 11 patents 40 copyrights and 2 design registrations. He has presented papers in 20 conferences and published in 10 national and international journals. R. Kaliappan completed his bachelors in electrical and electronics engineering and Masters in Computer Science. He has 36 years of research experience in different fields of power generation and power plant subsystems such as heat transfer studies on boiler circulation efficiency improvements of boiler subsystems product improvements/ enhancements and setting up test facilities for research studies. He has published a number of technical papers on MHD power generation and heat transfer studies in various national and international journals. He has more than 25 patents and copyrights on products development related to power boilers. He has won BHEL’S gold medal for product development for Smart Wall Blowing system. K. Nandakumar

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Publisher Correction: Early-onset colorectal cancer: initial clues and current views

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Nature reviews. Gastroenterology & hepatology 2020年第8期17卷 517页

作者： Lorne J Hofseth James R Hebert Anindya Chanda Hexin Chen Bryan L Love Maria M Pena E Angela Murphy Mathew Sajish Amit Sheth Phillip J Buckhaults Franklin G Berger Center for Colon Cancer Research University of South Carolina Columbia SC USA. hofseth@cop.sc.edu. Cancer Prevention and Control Program University of South Carolina Columbia SC USA. hofseth@cop.sc.edu. Department of Drug Discovery and Biomedical Sciences College of Pharmacy University of South Carolina Columbia SC USA. hofseth@cop.sc.edu. Center for Colon Cancer Research University of South Carolina Columbia SC USA. Cancer Prevention and Control Program University of South Carolina Columbia SC USA. Department of Epidemiology & Biostatistics University of South Carolina Columbia SC USA. Department of Environmental Health Sciences Arnold School of Public Health University of South Carolina Columbia SC USA. Department of Biology College of Arts and Sciences University of South Carolina Columbia SC USA. Department of Clinical Pharmacy and Outcomes Sciences College of Pharmacy University of South Carolina Columbia SC USA. Department of Pathology Microbiology & Immunology School of Medicine University of South Carolina Columbia SC USA. Department of Drug Discovery and Biomedical Sciences College of Pharmacy University of South Carolina Columbia SC USA. Department of Computer Science and Engineering College of Engineering University of South Carolina Columbia SC USA.

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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Asteroid Measurements at Millimeter Wavelengths with the South Pole Telescope

arXiv

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arXiv 2022年

作者： Chichura, P.M. Foster, A. Patel, C. Ossa-Jaen, N. Ade, P.A.R. Ahmed, Z. Anderson, A.J. Archipley, M. Austermann, J.E. Avva, J.S. Balkenhol, L. Barry, P.S. Thakur, R. Basu Beall, J.A. Benabed, K. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Bouchet, F.R. Bryant, L. Byrum, K. Carlstrom, J.E. Carter, F.W. Cecil, T.W. Chang, C.L. Chaubal, P. Chen, G. Chiang, H.C. Cho, H.-M. Chou, T.-L. Citron, R. Cliche, J.-F. Crawford, T.M. Crites, A.T. Cukierman, A. Daley, C.M. Denison, E.V. Dibert, K. Ding, J. Dobbs, M.A. Dutcher, D. Everett, W. Feng, C. Ferguson, K.R. Fu, J. Galli, S. Gallicchio, J. Gambrel, A.E. Gardner, R.W. George, E.M. Goeckner-Wald, N. Gualtieri, R. Guns, S. Gupta, N. Guyser, R. de Haan, T. Halverson, N.W. Harke-Hosemann, A.H. Harrington, N.L. Henning, J.W. Hilton, G.C. Hivon, E. Holder, G.P. Holzapfel, W.L. Hood, J.C. Howe, D. Hrubes, J.D. Huang, N. Hubmayr, J. Irwin, K.D. Jeong, O.B. Jonas, M. Jones, A. Khaire, T.S. Knox, L. Kofman, A.M. Korman, M. Kubik, D.L. Kuhlmann, S. Kuo, C.-L. Lee, A.T. Leitch, E.M. Li, D. Lowitz, A. Lu, C. Marrone, D.P. McMahon, J.J. Meyer, S.S. Michalik, D. Millea, M. Mocanu, L.M. Montgomery, J. Moran, C. Corbett Nadolski, A. Natoli, T. Nguyen, H. Nibarger, J.P. Noble, G. Novosad, V. Omori, Y. Padin, S. Pan, Z. Paschos, P. Patil, S. Pearson, J. Phadke, K.A. Posada, C.M. Prabhu, K. Pryke, C. Quan, W. Rahlin, A. Reichardt, C.L. Riebel, D. Riedel, B. Rouble, M. Ruhl, J.E. Saliwanchik, B.R. Sayre, J.T. Schaffer, K.K. Schiappucci, E. Shirokoff, E. Sievers, C. Smecher, G. Sobrin, J.A. Springmann, A. Stark, A.A. Stephen, J. Story, K.T. Suzuki, A. Tandoi, C. Thompson, K.L. Thorne, B. Tucker, C. Umilta, C. Vale, L.R. Veach, T. Vieira, J.D. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Yoon, K.W. Young, M.R. Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics Case Western Reserve University ClevelandOH44106 United States Department of Physics University of Illinois Urbana-Champaign 1110 West Green Street UrbanaIL61801 United States Department of Physics University of Redlands 1267 East Colton Ave RedlandsCA92374 United States School of Physics and Astronomy Cardiff University CardiffCF24 3YB United Kingdom Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States Center for AstroPhysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Department of Physics University of California BerkeleyCA94720 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue. ArgonneIL60439 United States California Institute of Technology 1200 East California Boulevard. PasadenaCA91125 United States Institut d'Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United St

We present the first measurements of asteroids in millimeter wavelength (mm) data from the South Pole Telescope (SPT), which is used primarily to study the cosmic microwave background (CMB). We analyze maps of two ∼ 270 deg2 sky regions near the ecliptic plane, each observed with the SPTpol camera ∼ 100 times over one month. We subtract the mean of all maps of a given field, removing static sky signal, and then average the mean-subtracted maps at known asteroid locations. We detect three asteroids-(324) Bamberga, (13) Egeria, and (22) Kalliope-with signal-to-noise ratios (S/N) of 11.2, 10.4, and 6.1, respectively, at 2.0 mm (150 GHz);we also detect (324) Bamberga with S/N of 4.1 at 3.2 mm (95 GHz). We place constraints on these asteroids' effective emissivities, brightness temperatures, and light curve modulation amplitude. Our flux density measurements of (324) Bamberga and (13) Egeria roughly agree with predictions, while our measurements of (22) Kalliope suggest lower flux, corresponding to effective emissivities of 0.64±0.11 at 2.0 mm and Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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Erratum: “Growth and characterization of β-Ga2O3 thin films by molecular beam epitaxy for deep-UV photodetectors” [J. Appl. Phys. 122, 095302 (2017)]

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Journal of Applied Physics 2018年第13期124卷

作者： Susmita Ghose Shafiqur Rahman Liang Hong Juan Salvador Rojas-Ramirez Hanbyul Jin Kibog Park Robert Klie Ravi Droopad 1Material Science Engineering and Commercialization Program Texas State University San Marcos Texas 78666 USA 2Department of Physics University of Illinois at Chicago Chicago Illinois 60607 USA 3Ingram School of Engineering Texas State University San Marcos Texas 78666 USA 4School of Electrical and Computer Engineering Ulsan National Institute of Science and Technology (UNIST) Ulsan 44919 South Korea 5Department of Physics Ulsan National Institute of Science and Technology (UNIST) Ulsan 44919 South Korea

We noticed an omission of the acknowledgment and provide it here with apologies:1

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models

arXiv

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arXiv 2017年

作者： Samek, Wojciech Wiegand, Thomas Müller, Klaus-Robert Dept. of Video Coding & Analytics Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Dept. of Computer Science Technische Universität Berlin Berlin10587 Germany Dept. of Brain & Cognitive Engineering Korea University Seoul136-713 Korea Republic of Max Planck Institute for Informatics Saarbrücken66123 Germany

With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching or even exceeding the human level on an increasing number of complex tasks. Impressive examples of this development can be found in domains such as image classification, sentiment analysis, speech understanding or strategic game playing. However, because of their nested non-linear structure, these highly successful machine learning and artificial intelligence models are usually applied in a black box manner, i.e., no information is provided about what exactly makes them arrive at their predictions. Since this lack of transparency can be a major drawback, e.g., in medical applications, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This paper summarizes recent developments in this field and makes a plea for more interpretability in artificial intelligence. Furthermore, it presents two approaches to explaining predictions of deep learning models, one method which computes the sensitivity of the prediction with respect to changes in the input and one approach which meaningfully decomposes the decision in terms of the input variables. These methods are evaluated on three classification tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Reference Data Based Insights Expand Understanding of Human Metabolomes

SSRN

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SSRN 2020年

作者： Gauglitz, Julia M. Bittremieux, Wout Williams, Candace L. Weldon, Kelly C. Panitchpakdi, Morgan Di Ottavio, Francesca Aceves, Christine M. Brown, Elizabeth Sikora, Nicole C. Jarmusch, Alan K. Martino, Cameron Tripathi, Anupriya Sayyari, Erfan Shaffer, Justin P. Coras, Roxana Vargas, Fernando DeRight Goldasich, Lindsay Schwartz, Tara Bryant, MacKenzie Humphrey, Gregory Johnson, Abigail J. Spengler, Katharina Belda-Ferre, Pedro Diaz, Edgar McDonald, Daniel Zhu, Qiyun Nguyen, Dominic S. Elijah, Emmanuel O. Wang, Mingxun Marotz, Clarisse Sprecher, Kate E. Robles, Daniela Vargas Withrow, Dana Ackermann, Gail Herrera, Lourdes Bradford, Barry J. Marques, Lucas Maciel Mauriz Amaral, Juliano Geraldo Silva, Rodrigo Moreira Veras, Flávio Protaso Cunha, Thiago Mattar Oliveira, Rene Donizeti Ribeiro Louzada-Junior, Paulo Mills, Robert H. Galasko, Douglas Dulai, Parambir S. Wittenberg, Curt Gonzalez, David J. Terkeltaub, Robert Doty, Megan M. Kim, Jae H. Rhee, Kyung E. Beauchamp-Walters, Julia Wright, Kenneth P. Dominguez-Bello, Maria Gloria Manary, Mark Oliveira, Michelli F. Boland, Brigid S. Lopes, Norberto Peporine Guma, Monica Swafford, Austin D. Dutton, Rachel J. Knight, Rob Dorrestein, Pieter C. Collaborative Mass Spectrometry Innovation Center University of California San Diego San DiegoCA92093 United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego San DiegoCA92093 United States Department of Computer Science University of Antwerp Antwerpen2020 Belgium Reproductive Sciences Institute for Conservation Research San Diego Zoo Global EscondidoCA92027 United States Center for Microbiome Innovation Joan and Irwin Jacobs School of Engineering University of California San Diego San DiegoCA92093 United States Division of Biological Sciences University of California San Diego San DiegoCA92093 United States Department of Pediatrics School of Medicine University of California San Diego San DiegoCA92093 United States Bioinformatics and Systems Biology Program University of California San Diego San DiegoCA92093 United States Division of Rheumatology Allergy & Immunology Department of Medicine University of California San Diego San DiegoCA92093 United States BioTechnology Institute University of Minnesota Saint PaulMN55108 United States Department of Integrative Physiology University of Colorado Boulder BoulderCO80309 United States Department of Population Health Sciences University of Wisconsin-Madison MadisonWI53726 United States Servicio Autónomo Centro Amazónico de Investigación y Control de Enfermedades Tropicales Simón Bolívar Amazonas Puerto Ayacucho7101 Venezuela Department of Pediatrics Wake Forest School of Medicine Winston-SalemNC27101 United States Department of Animal Science Michigan State University East LansingMI48824 United States Department of Pharmacology School of Medicine of Ribeirao Preto Center of Research in Inflammatory Diseases University of Sao Paulo SP Ribeirao PretoCEP 14049-900 - Brazil Multidisciplinary Health Institute Federal University of Bahia BA Vitória da Conquista45029094 Brazil NPPNS Department of Biomolecular Sciences School of Pharmaceutica

The human metabolome has remained largely unknown, with most studies annotating ~10% of features. In nucleic acid sequencing, annotating transcripts by source has proven essential for understanding gene function. Here we generalize this concept to stool, plasma, urine and other human metabolomes, discovering that food-based annotations increase the interpreted fraction of molecular features 7-fold, providing a general framework for expanding the interpretability of human metabolomic "dark matter." © 2020, The Authors. All rights reserved.

关键词： Nucleic acids

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SPT clusters with DES and HST weak lensing. II. Cosmological constraints from the abundance of massive halos

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Physical Review D 2024年第8期110卷 083510页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr T. Schrabback T. M. C. Abbott P. A. R. Ade M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon A. J. Anderson J. Annis B. Ansarinejad J. E. Austermann S. Avila D. Bacon M. Bayliss J. A. Beall K. Bechtol M. R. Becker A. N. Bender B. A. Benson G. M. Bernstein S. Bhargava F. Bianchini M. Brodwin D. Brooks L. Bryant A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero F. J. Castander R. Cawthon C. L. Chang C. Chang P. Chaubal R. Chen H. C. Chiang A. Choi T-L. Chou R. Citron C. Corbett Moran J. Cordero M. Costanzi T. M. Crawford A. T. Crites L. N. da Costa M. E. S. Pereira C. Davis T. M. Davis J. DeRose S. Desai T. de Haan H. T. Diehl M. A. Dobbs S. Dodelson C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett W. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. Gallicchio J. García-Bellido M. Gatti E. M. George G. Giannini M. D. Gladders D. Gruen R. A. Gruendl N. Gupta G. Gutierrez N. W. Halverson I. Harrison W. G. Hartley K. Herner S. R. Hinton G. P. Holder D. L. Hollowood W. L. Holzapfel K. Honscheid J. D. Hrubes N. Huang J. Hubmayr E. M. Huff D. Huterer K. D. Irwin D. J. James M. Jarvis G. Khullar K. Kim L. Knox R. Kraft E. Krause K. Kuehn N. Kuropatkin F. Kéruzoré O. Lahav A. T. Lee P.-F. Leget D. Li H. Lin A. Lowitz N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. J. McMahon J. Mena-Fernández F. Menanteau S. S. Meyer R. Miquel J. Montgomery J. Myles T. Natoli A. Navarro-Alsina J. P. Nibarger G. I. Noble V. Novosad R. L. C. Ogando Y. Omori S. Padin S. Pandey P. Paschos S. Patil A. Pieres A. A. Plazas Malagón A. Porredon J. Prat C. Pryke M. Raveri C. L. Reichardt J. Roberson R. P. Rollins C. Romero A. Roodman J. E. Ruhl E. S. Rykoff B. R. Saliwanchik L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro K. K. Schaffer L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin C. Sievers G. Smecher M. Smith T. Somboonpanyakul M. Sommer B. Stalder A. A. Stark J. Stephen V. Straz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstraße 25/8 6020 Innsbruck Austria High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Max Planck Institute for Extraterrestrial Physics Gießenbachstraße 1 85748 Garching Germany Argelander-Institut für Astronomie Auf dem Hügel 71 53121 Bonn Germany Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile School of Physics and Astronomy Cardiff University Cardiff CF24 3YB United Kingdom Laboratório Interinstitucional de e-Astronomia—LIneA Rua Gal. José Cristino 77 Rio de Janeiro Rio de Janeiro—20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia NIST Quantum Devices Group 325 Broadway Mailcode 817.03 Boulder Colorado 80305 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology Campus UAB 08193 Bellaterra (Barcelona) Spain Institute of Cosmology and Gravitation University of Portsmouth Portsmouth

We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel’dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range 0.25–1.78 over a total sky area of 5200 deg2. We use DES Year 3 weak-lensing data for 688 clusters with redshifts z<0.95 and HST weak-lensing data for 39 clusters with 0.6

关键词： Cosmological parameters Dark energy Large scale structure of the Universe Galaxy clusters

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