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Response to Comment on "Cannabis and the Environment: What science Tells Us and What We Still Need to Know"

ENVIRONMENTAL science & TECHNOLOGY LETTERS 2021年第6期8卷 486-486页

作者： Wartenberg, Ariani C. Holden, Patricia A. Bodwitch, Hekia Parker-Shames, Phoebe Novotny, Thomas Harmon, Thomas C. Hart, Stephen C. Beutel, Marc Gilmore, Michelle Hoh, Eunha Butsic, Van Department of Environmental Science Policy and Management University of California Berkeley California 94720 United States Bren School of Environmental Science & Management University of California Santa Barbara California 93117 United States School of Public Health San Diego State University San Diego California 92182 United States Environmental Systems Graduate Program School of Engineering University of California Merced California 95343 United States Department of Life & Environmental Sciences and Sierra Nevada Research Institute University of California Merced California 95343 United States

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Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology

arXiv

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arXiv 2024年

作者： Remedios, Lucas W. Bao, Shunxing Remedios, Samuel W. Lee, Ho Hin Cai, Leon Y. Li, Thomas Deng, Ruining Newlin, Nancy R. Saunders, Adam M. Cui, Can Li, Jia Liu, Qi Lau, Ken S. Roland, Joseph T. Washington, Mary K. Coburn, Lori A. Wilson, Keith T. Huo, Yuankai Landman, Bennett A. Vanderbilt University Department of Computer Science Nashville United States Vanderbilt University Department of Electrical and Computer Engineering Nashville United States Johns Hopkins University Department of Computer Science Baltimore United States National Institutes of Health Department of Radiology and Imaging Sciences Bethesda United States Vanderbilt University Department of Biomedical Engineering Nashville United States Vanderbilt University Medical Center Department of Biostatistics Nashville United States Vanderbilt University Medical Center Center for Quantitative Sciences Nashville United States Vanderbilt University Medical Center Epithelial Biology Center Nashville United States Vanderbilt University School of Medicine Department of Cell and Developmental Biology Nashville United States Vanderbilt University Medical Center Division of Gastroenterology Hepatology and Nutrition Department of Medicine Nashville United States Vanderbilt University Medical Center Vanderbilt Center for Mucosal Inflammation and Cancer Nashville United States Vanderbilt University School of Medicine Program in Cancer Biology Nashville United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Medical Center Department of Pathology Microbiology and Immunology NashvilleTN United States

Purpose: Cells are building blocks for human physiology;consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both health and disease. Hematoxylin and eosin (H&E) is the standard stain used in histological analysis of tissues. Microscope slides of H&E-stained tissues are widely available in both clinical and research settings. While H&E is ubiquitous and reveals tissue microanatomy, the classification and tracking of cell subtypes often requires expert knowledge and the use of specialized stains, such as immunofluorescence staining. To reduce both the manual annotation burden and reliance on specialized staining technologies, artificial intelligence has been proposed for the automatic classification of cell types on H&E slides. For example, the recent Colon Nucleus Identification and Classification (CoNIC) Challenge focused on labeling 6 cell types on imaging of H&E stains from the human colon. However, this is a very small fraction of the number of potential cell subtypes within the intestines. Specifically, the CoNIC Challenge was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). To approach this problem, we propose to use inter-modality learning to label previously un-labelable cell types on H&E. Approach: We take advantage of the cell classification information inherent in whole slide images (WSIs) of multiplexed immunofluorescence (MxIF) histology to create cell level annotations for 14 subclasses. We performed style transfer on the same MxIF tissues to synthesize realistic virtual H&E which we paired with the MxIF-derived cell subclassification labels. We evaluated the efficacy of using a supervised learning scheme where the input was realistic-quality virtual H&E and the labels were MxIF-derived cell subclasses. We assessed our model on a testing

关键词： Tissue

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Low energy performance boost through a hardware stereoscopic trigger between CTA LST-1 and MAGIC 38

Low energy performance boost through a hardware stereoscopic...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Baxter, J. Barrio, J.A. Bitossi, M. Donini, A. Mazin, D. Paoletti, R. Pérez Aguilera, A. Tejedor Álvarez, L.Á. Abe, H. Abe, K. Abe, S. Abhir, J. Acciari, V.A. Aguasca-Cabot, A. Agudo, I. Alvarez Crespo, N. Aniello, T. Ansoldi, S. Antonelli, L.A. Aramo, C. Arbet-Engels, A. Cornelia, A. Artero, M. Asano, K. Aubert, P. Baack, D. Babić, A. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bednarek, W. Bernardini, E. Bernardos, M.I. Bernete Medrano, J. Berti, A. Besenrieder, J. Bhattacharjee, P. Biederbeck, N. Bigongiari, C. Biland, A. Bissaldi, E. Blanch, O. Bonnoli, G. Bordas, P. Bošnjak, Ž. Bulgarelli, A. Burelli, I. Burmistrov, L. Buscemi, M. Busetto, G. Campoy-Ordaz, A. Cardillo, M. Caroff, S. Carosi, A. Carosi, R. Carrasco, M.S. Carretero-Castrillo, M. Cassol, F. Castro-Tirado, A.J. Cauz, D. Cerasole, D. Ceribella, G. Chai, Y. Cheng, K. Chiavassa, A. Chikawa, M. Chilingarian, A. Chytka, L. Cifuentes, A. Cikota, S. Colombo, E. Contreras, J.L. Cortina, J. Costantini, H. Covino, S. D’Amico, G. Dalchenko, M. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. de Bony de Lavergne, M. De Lotto, B. De Lucia, M. de Menezes, R. Del Peral, L. Deleglise, G. Delfino, M. Delgado, C. Delgado Mengual, J. della Volpe, D. Dellaiera, M. Del Popolo, A. Depaoli, D. Di Piano, A. Di Pierro, F. Di Pilato, A. Di Tria, R. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Eisenberger, L. Elsässer, D. Emery, G. Escudero, J. Fallah Ramazani, V. Fariña, L. Fattorini, A. Ferrara, G. Ferrarotto, F. Fiasson, A. Foffano, L. Font, L. Freixas Coromina, L. Fröse, S. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Garczarczyk, M. Gasbarra, C. Gasparrini, D. Gasparyan, S. Gaug, M. Geyer, D. Giesbrecht Paiva, J. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Grau, R. Green, J. Green, D. Gunji, S. Günther, P. Hackfeld, J. Hadasch, D. Hahn, A. Hashiyama, K. Hassan, T. Hayashi, K. Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid Spain Sezione di Pisa Pisa56127 Italy Osservatorio Astronomico di Roma Italy Max-Planck-Institut für Physik Föhringer Ring 6 München80805 Germany Università degli Studi di Siena via Roma 56 Siena53100 Italy Department of Physics Tokai University Japan ETH Zürich Switzerland Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Spain EMFTEL department IPARCOS Universidad Complutense de Madrid Spain Italy INFN Sezione di Trieste Università degli Studi di Udine Italy Italy INAF Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy INFN Sezione di Padova Università degli Studi di Padova Italy The Barcelona Institute of Science and Technology Spain LAPP Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 Annecy France Department of Physics TU Dortmund University Germany Croatia Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan Universidad del Azuay Ecuador INAF Osservatorio di Astrofisica e Scienza dello spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil Faculty of Physics and Applied Informatics University of Lodz Poland CIEMAT Spain INFN Sezione di Bari Politecnico di Bari Italy INAF Osservatorio Astronomico di Brera Italy University of Geneva Département de physique nucléaire et corpusculaire Switzerland INFN Sezione di Catania Italy Departament de Física CERES-IEEC Universitat Autónoma de Barcelona Spain Italy Università di Pisa INFN Pisa Italy Aix Marseille Univ CNRS IN2P3 CPPM France INFN Sezione di Bari Università di Bari Italy INFN Sezione di Torino Italy ICRANet-Armenia at

The next generation facility for gamma-ray ground-based observations is the Cherenkov Telescope Array (CTA) observatory, which comprises three types of imaging atmospheric Cherenkov telescopes (IACTs). The Large-Sized Telescopes (LSTs) of CTA are the largest telescope type with a mirror dish of 23 m diameter. They cover the low energy end of the accessible gamma-ray energies for IACTs, starting from about 20 GeV up to a few TeV. The first LST prototype, known as LST-1, was officially inaugurated at the Observatorio del Roque de Los Muchachos in La Palma (Canary Islands, Spain) in 2018 and has since performed calibration observations of various known gamma-ray sources. Additionally, the site houses the MAGIC telescopes, two 17 m IACTs situated approximately 100 m away from LST-1, which have been performing stereo observations since 2009. Currently, joint observations between LST-1 and MAGIC are being carried on, and the data taken independently by the two IACT systems is analyzed by combining events via software. However, this method increases the energy threshold, as it discards all but the relatively high-energy events triggered by all three telescopes. To address this issue, we have developed a novel hardware stereo trigger system between LST-1 and MAGIC, which is capable of handling events triggered by any-two out of the three telescopes. In this contribution, we will report on the performance estimation of joint LST-1 and MAGIC observations using the hardware trigger. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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Observation of Active Galactic Nuclei with the Large-Sized Telescope prototype of the Cherenkov Telescope Array 38

Observation of Active Galactic Nuclei with the Large-Sized T...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Takeishi, R. Álvarez Crespo, N. Arbet-Engels, A. Baquero Larriva, A. Baxter, J. Biederbeck, N. Caroff, S. Di Marco, G. Fallah Ramazani, V. Green, D. Heckmann, L. Láinez, M. Nickel, L. Nievas Rosillo, M. Pons, E. Priyadarshi, C. Sanchez, D.A. Vázquez Acosta, M. Abe, K. Abe, S. Aguasca-Cabot, A. Agudo, I. Antonelli, L.A. Aramo, C. Arcaro, C. Artero, M. Asano, K. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardini, E. Bernardos, M.I. Bernete Medrano, J. Berti, A. Bhattacharjee, P. Bigongiari, C. Bissaldi, E. Blanch, O. Bonnoli, G. Bordas, P. Bulgarelli, A. Burelli, I. Burmistrov, L. Buscemi, M. Cardillo, M. Carosi, A. Carrasco, M.S. Cassol, F. Cauz, D. Cerasole, D. Ceribella, G. Chai, Y. Cheng, K. Chiavassa, A. Chikawa, M. Chytka, L. Cifuentes, A. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. Dazzi, F. De Angelis, A. de Bony de Lavergne, M. De Lotto, B. De Lucia, M. de Menezes, R. Del Peral, L. Deleglise, G. Delgado, C. Delgado Mengual, J. della Volpe, D. Dellaiera, M. Di Piano, A. Di Pierro, F. Di Pilato, A. Di Tria, R. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Eisenberger, L. Elsässer, D. Emery, G. Escudero, J. Ferrara, G. Ferrarotto, F. Fiasson, A. Foffano, L. Freixas Coromina, L. Fröse, S. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Gasbarra, C. Gasparrini, D. Geyer, D. Giesbrecht Paiva, J. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Grau, R. Gunji, S. Günther, P. Hackfeld, J. Hadasch, D. Hahn, A. Hashiyama, K. Hassan, T. Hayashi, K. Heller, M. HerreraLlorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Iarlori, M. Imazawa, R. Inada, T. Inome, Y. Ioka, K. Iori, M. Ishio, K. Jimenez Martinez, I. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kohri, K. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Grupo de Electronica Universidad Complutense de Madrid Av. Complutense s/n Madrid28040 Spain Max-Planck-Institut für Physik Föhringer Ring 6 München80805 Germany IPARCOS-UCM Instituto de Física de Partículas y del Cosmos EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain Faculty of Science and Technology Universidad del Azuay Cuenca Ecuador Department of Physics TU Dortmund University Otto-Hahn-Str. 4 Dortmund44227 Germany Univ. Savoie Mont Blanc CNRS Laboratoire d’Annecy de Physique des Particules - IN2P3 Annecy74000 France Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Tenerife La Laguna Spain Institut für Theoretische Physik Lehrstuhl IV: Plasma-Astroteilchenphysik Ruhr-Universität Bochum Universitätsstraße 150 Bochum44801 Germany The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Department of Physics Tokai University Japan Departament de Física Quàntica i Astrofísica Institut de Ciències delCosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Spain EMFTEL department IPARCOS Universidad Complutense de Madrid Spain INAF - Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy INFN Sezione di Padova Università degli Studi di Padova Italy Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan Universidad del Azuay Ecuador INAF - Osservatorio di Astrofisica e Scienza dello spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil CIEMAT Spain INFN Sezione di Bari Politecnico di Bari Italy INAF - Osservatorio Astronomico di Brera Italy INFN Sezione di Trieste Università degli Studi di Udine Italy University of Geneva Département de physique nucléaire et corpusculaire Switzerland INFN Sezione di Catania Italy Italy Aix Marseille Uni

Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory. It will consist of three different sizes of telescopes, with a collective improvement of about an order of magnitude in sensitivity, compared with the current generation gamma-ray telescopes. LargeSized Telescopes (LSTs) will be the most sensitive at energies from tens of GeV up to a few TeVs, and will be the best suited to observe gamma-ray sources such as Active Galactic Nuclei (AGN). The first Large-Sized Telescope prototype (LST-1), inaugurated in 2018 and currently in commissioning phase, has accumulated more than one thousand hours of data to date. From 2020 to 2022, we have detected various gamma-ray sources, and prominent among them are several well-known AGN, like Mrk 421, Mrk 501, 1ES 1959+650, 1ES 0647+250, PG 1553+113, etc. We report on light curves and energy spectra reconstructed out of these observations down to energies of tens of GeV, which is very close to the energy threshold of LST-1. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Active Galactic Nuclei

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Nucleus subtype classification using inter-modality learning

arXiv

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arXiv 2024年

作者： Remedios, Lucas W. Bao, Shunxing Remedios, Samuel W. Lee, Ho Hin Cai, Leon Y. Li, Thomas Deng, Ruining Cui, Can Li, Jia Liu, Qi Lau, Ken S. Roland, Joseph T. Washington, Mary K. Coburn, Lori A. Wilson, Keith T. Huo, Yuankai Landman, Bennett A. Vanderbilt University Department of Computer Science Nashville United States Vanderbilt University Department of Electrical and Computer Engineering Nashville United States Johns Hopkins University Department of Computer Science Baltimore United States National Institutes of Health Department of Radiology and Imaging Sciences Bethesda United States Vanderbilt University Department of Biomedical Engineering Nashville United States Vanderbilt University Medical Center Department of Biostatistics Nashville United States Vanderbilt University Medical Center Center for Quantitative Sciences Nashville United States Vanderbilt University Medical Center Epithelial Biology Center Nashville United States Vanderbilt University School of Medicine Department of Cell and Developmental Biology Nashville United States Vanderbilt University Medical Center Division of Gastroenterology Hepatology and Nutrition Department of Medicine Nashville United States Vanderbilt University Medical Center Vanderbilt Center for Mucosal Inflammation and Cancer Nashville United States Vanderbilt University School of Medicine Program in Cancer Biology Nashville United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Medical Center Department of Pathology Microbiology and Immunology NashvilleTN United States

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon. However, this is a very small fraction of the number of potential cell classification types. Specifically, the CoNIC Challenge is unable to classify epithelial subtypes (progenitor, endocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes (fibroblasts, stromal). In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E. We leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify 14 subclasses of cell types. We performed style transfer to synthesize virtual H&E from MxIF and transferred the higher density labels from MxIF to these virtual H&E images. We then evaluated the efficacy of learning in this approach. We identified helper T and progenitor nuclei with positive predictive values of 0.34 ± 0.15 (prevalence 0.03 ± 0.01) and 0.47 ± 0.1 (prevalence 0.07 ± 0.02) respectively on virtual H&E. This approach represents a promising step towards automating annotation in digital pathology. © 2024, CC BY.

关键词： Cell culture

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Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities

arXiv

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arXiv 2024年

作者： Guo, Boyi Ling, Wodan Kwon, Sang Ho Panwar, Pratibha Ghazanfar, Shila Martinowich, Keri Hicks, Stephanie C. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Division of Biostatistics Department of Population Health Sciences Weill Cornell Medicine NY United States Lieber Institute for Brain Development Johns Hopkins Medical Campus BaltimoreMD United States Solomon H. Snyder Department of Neuroscience Johns Hopkins School of Medicine BaltimoreMD United States Biochemistry Cellular and Molecular Biology Graduate Program Johns Hopkins School of Medicine BaltimoreMD United States School of Mathematics and Statistics The University of Sydney NSW2006 Australia Sydney Precision Data Science Centre University of Sydney NSW2006 Australia Charles Perkins Centre The University of Sydney NSW2006 Australia Department of Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine BaltimoreMD United States Johns Hopkins Kavli Neuroscience Discovery Institute BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Malone Center for Engineering in Healthcare Johns Hopkins University BaltimoreMD United States

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future. © 2024, CC BY-NC-ND.

关键词： Biotic

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Coarse particulate matter air quality in East Asia: implications for fine particulate nitrate

arXiv

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arXiv 2022年

作者： Zhai, Shixian Jacob, Daniel J. Shah, Viral Yang, Laura Hyesung Zhang, Qiang Sun, Yele Wang, Shuxiao Luo, Gan Yu, Fangqun Woo, Jung-Hun Kim, Younha Kim, Hwajin Dibb, Jack E. Lee, Taehyoung Han, Jin-Seok Li, Ke Liao, Hong John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Earth System Science Tsinghua University Beijing100084 China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China Atmospheric Sciences Research Center University at Albany AlbanyNY12226 United States Department of Civil and Environmental Engineering Konkuk University Seoul05029 Korea Republic of Laxenburg 2361 Austria Department of Environmental Health Sciences Graduate School of Public Health Seoul National University Seoul08826 Korea Republic of Institute for the Study of Earth Oceans and Space University of New Hampshire DurhamNH03824 United States Department of Environmental Science Hankuk University of Foreign Studies Yongin449791 Korea Republic of Department of Environmental and Energy Engineering Anyang University Anyang14028 Korea Republic of Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China

Air quality network data in China and South Korea show very high year-round mass concentrations of coarse particulate matter (PM) between 2.5 μm and 10 μm aerodynamic diameter, as inferred by the difference between PM10 and PM2.5 observations. This coarse PM averages 47 μg m-3 in the North China Plain (NCP) region in 2015-2019 and 21 μg m-3 in the Seoul Metropolitan Area (SMA). It is dominantly contributed by urban fugitive dust, rather than by natural dust as is often presumed. Concentrations decreased by 21% from 2015 to 2019 and further dropped abruptly in 2020 due to COVID-19 reductions in construction and vehicle traffic. This anthropogenic coarse PM is generally not included in air quality models but acts as a sink of nitric acid (HNO3), thus affecting fine particulate nitrate which is a major air quality concern in China and South Korea. GEOS-Chem model simulation of surface and aircraft observations from the KORUS-AQ campaign over the SMA in May-June 2016 shows that consideration of anthropogenic coarse PM largely resolves the previous model overestimate of PM1 nitrate. Anthropogenic coarse PM in the model increases the sensitivity of PM2.5 nitrate to ammonia (NH3) emission in winter. In summer, anthropogenic coarse PM directly affects PM2.5 nitrate by HNO3 uptake, and we find that the decrease of anthropogenic coarse PM over 2015-2019 offset the PM2.5 nitrate reductions expected from decreasing NOx and NH3 emissions. Copyright © 2022, The Authors. All rights reserved.

关键词： Nitrates

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Technical debt and agile software development practices and processes: An industry practitioner survey

arXiv

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arXiv 2021年

作者： Holvitie, Johannes Licorish, Sherlock A. Spínola, Rodrigo O. Hyrynsalmi, Sami MacDonell, Stephen G. Mendes, Thiago S. Buchan, Jim Leppänen, Ville Turku Centre for Computer Science Software Development Laboratory Turku Finland University of Turku Department of Information Technology Turku Finland University of Otago Department of Information Science Otago Dunedin New Zealand Salvador University Graduate Program in Systems and Computer Bahia Salvador Brazil Federal University of Bahia Fraunhofer Project Center for Software and System Engineering Salvador Brazil Tampere University of Technology Pori Campus Pori Finland Federal Institute of Bahia Information Technology Department Bahia Santo Amaro Brazil Auckland University of Technology School of Engineering Computer and Mathematical Sciences Auckland New Zealand

Context: Contemporary software development is typically conducted in dynamic, resource-scarce environments that are prone to the accumulation of technical debt. While this general phenomenon is acknowledged, what remains unknown is how technical debt specifically manifests in and affects software processes, and how the software development techniques employed accommodate or mitigate the presence of this debt. Objectives: We sought to draw on practitioner insights and experiences in order to classify the effects of agile method use on technical debt management, given the popularity and perceived success of agile methods. We explore the breadth of practitioners' knowledge about technical debt;how technical debt is manifested across the software process;and the perceived effects of common agile software development practices and processes on technical debt. In doing so, we address a research gap in technical debt knowledge and provide novel and actionable managerial recommendations. Method: We designed, tested and executed a multi-national survey questionnaire to address our objectives, receiving 184 responses from practitioners in Brazil, Finland, and New Zealand. Results: Our findings indicate that: 1) Practitioners are aware of technical debt, although, there was under utilization of the concept, 2) Technical debt commonly resides in legacy systems, however, concrete instances of technical debt are hard to conceptualize which makes it problematic to manage, 3) Queried agile practices and processes help to reduce technical debt;in particular, techniques that verify and maintain the structure and clarity of implemented artifacts (e.g., Coding standards and Refactoring) positively affect technical debt management. Conclusions: The fact that technical debt instances tend to have characteristics in common means that a systematic approach to its management is feasible. However, notwithstanding the positive effects of some agile practices on technical debt management, comp

关键词： Legacy systems

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Lower Bounds to the Q factor of Electrically Small Resonators through Quasistatic Modal Expansion

arXiv

引用

arXiv 2021年

作者： Pascale, Mariano Mann, Sander A. Tzarouchis, Dimitrios C. Miano, Giovanni Alù, Andrea Forestiere, Carlo ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain Department of Electrical Engineering and Information Technology Università degli Studi di Napoli Federico II via Claudio 21 Napoli80125 Italy Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States Department of Electrical and System Engineering University of Pennsylvania PhiladelphiaPA19104 United States

The problem of finding the optimal current distribution supported by small radiators yielding the minimum quality (Q) factor is a fundamental problem in electromagnetism. Q factor bounds constrain the maximum operational bandwidth of devices including antennas, metamaterials, and nanoresonators, and have been featured in seminal papers in the past decades. Here, we determine the lower bounds of Q factors of small-size plasmonic and high-permittivity dielectric resonators, which are characterized by quasi-electrostatic and quasi-magnetostatic natural modes, respectively. We expand the induced current density field in the resonator in terms of these modes, leading to closed-form analytical expressions for the electric and magnetic polarizability tensors, whose largest eigenvalue is directly linked to the minimum Q factor. Our results allow also to determine in closed form the corresponding optimal current density field. In particular, when the resonator exhibits two orthogonal reflection symmetries the minimum Q factor can be simply obtained from the Q factors of the single current modes with non-vanishing dipole moments aligned along the major axis of the resonator. Overall, our results open exciting opportunities in the context of nano-optics and metamaterials, facilitating the analysis and design of optimally shaped resonators for enhanced and tailored light-matter interactions. Copyright © 2021, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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