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Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study

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The Lancet Planetary Health 2021年第4期5卷 e209-e219页

作者： Lowe, Rachel Lee, Sophie A Brady, Oliver J Bastos, Leonardo Carrasco-Escobar, Gabriel de Castro Catão, Rafael Colón-González, Felipe J Barcellos, Christovam Carvalho, Marilia Sá Blangiardo, Marta Rue, Håvard Gasparrini, Antonio Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London United Kingdom Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London United Kingdom Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London United Kingdom Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom Centre for Statistical Modelling London School of Hygiene & Tropical Medicine London United Kingdom Scientific Computing Program Oswaldo Cruz Foundation Rio de Janeiro Brazil Health Information and Communication Institute Oswaldo Cruz Foundation Rio de Janeiro Brazil Scripps Institution of Oceanography University of California San Diego San Diego CA United States Health Innovation Laboratory Institute of Tropical Medicine Alexander von Humboldt Universidad Peruana Cayetano Heredia Lima Peru Department of Geography Federal University of Espírito Santo Vitória Brazil MRC Centre for Environment and Health Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Background: Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model. Methods: We combined distributed lag non-linear models with a spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the relative risk (RR) of dengue and a drought severity index. We fit the model to monthly dengue case data for the 558 microregions of Brazil between January, 2001, and January, 2019, accounting for unobserved confounding factors, spatial autocorrelation, seasonality, and interannual variability. We assessed the variation in RR by level of urbanisation through an interaction between the drought severity index and urbanisation. We also assessed the effect of hydrometeorological hazards on dengue risk in areas with a high frequency of water supply shortages. Findings: The dataset included 12 895 293 dengue cases reported between 2001 and 2019 in Brazil. Overall, the risk of dengue increased between 0–3 months after extremely wet conditions (maximum RR at 1 month lag 1·56 [95% CI 1·41–1·73]) and 3–5 months after drought conditions (maximum RR at 4 months lag 1·43 [1·22–1·67]). Including a linear interaction between the drought severity index and level of urbanisation improved the model fit and showed the risk of dengue was higher in more rural areas than highly urbanised areas during extremely wet conditions (maximum RR 1·77 [1·32–2·37] at 0 months lag vs maximum RR 1·58 [1·39–1·81] at 2 months lag), but higher in highly urbanised areas than rural areas after extreme drought (maximum RR 1·60 [1·33–1·92] vs 1·15 [1·08–1·22], both at 4 months lag). We also found the den

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Ioanna Skampardoni Ilya M. Nasrallah Junhao Wen Yuhan Cui Ahmed Abdulkadir Zhijian Yang Guray Erus Elizabeth Mamourian Ashish Singh Haochang Shou Li Shen Konstantina Nikita Christos Davatzikos iSTAGING consortium School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Correspondece Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland 1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

Background Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data-driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium. Method Structural brain morphologic patterns of CU individuals were independently examined in four decade-long intervals spanning ages 45 to 85. Within each interval, Smile-GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile-GAN probability scores were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019). Result Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previo

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Discovery and confirmation of the shortest gamma ray burst from a collapsar

arXiv

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arXiv 2021年

作者： Ahumada, Tomás Singer, Leo P. Anand, Shreya Coughlin, Michael W. Kasliwal, Mansi M. Ryan, Geoffrey Andreoni, Igor Cenko, S. Bradley Fremling, Christoffer Kumar, Harsh Pang, Peter T.H. Burns, Eric Cunningham, Virginia Dichiara, Simone Dietrich, Tim Svinkin, Dmitry S. Almualla, Mouza Castro-Tirado, Alberto J. De, Kishalay Dunwoody, Rachel Gatkine, Pradip Hammerstein, Erica Iyyani, Shabnam Mangan, Joseph Perley, Dan Purkayastha, Sonalika Bellm, Eric Bhalerao, Varun Bolin, Bryce Bulla, Mattia Cannella, Christopher Chandra, Poonam Duev, Dmitry A. Frederiks, Dmitry Gal-Yam, Avishay Graham, Matthew Ho, Anna Y.Q. Hurley, Kevin Karambelkar, Viraj Kool, Erik C. Kulkarni, S.R. Mahabal, Ashish Masci, Frank McBreen, Sheila Pandey, Shashi B. Reusch, Simeon Ridnaia, Anna Rosnet, Philippe Rusholme, Benjamin Carracedo, Ana Sagués Smith, Roger Soumagnac, Maayane Stein, Robert Troja, Eleonora Tsvetkova, Anastasia Walters, Richard Valeev, Azamat F. Department of Astronomy University of Maryland College ParkMD20742 United States Astrophysics Science Division NASA Goddard Space Flight Center MC 661 GreenbeltMD20771 United States Center for Research and Exploration in Space Science and Technology NASA Goddard Space Flight Center GreenbeltMD20771 United States Joint Space-Science Institute University of Maryland College ParkMD20742 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Indian Institute of Technology Bombay Powai Mumbai400076 India LSSTC Data Science Fellow Nikhef Science Park 105 Amsterdam1098 XG Netherlands Department of Physics Utrecht University Princetonplein 1 Utrecht3584 CC Netherlands Louisiana State University Baton RougeLA70803 United States Institut für Physik und Astronomie Universität Potsdam Haus 28 Karl-Liebknecht-Str. 24/25 Potsdam14476 Germany Ioffe Institute Polytekhnicheskaya 26 St. Petersburg194021 - Russia American University of Sharjah Physics Department PO Box 26666 Sharjah United Arab Emirates Glorieta de la Astronomía s/n GranadaE-18008 Spain Departamento de Ingeniería de Sistemas y Automática Escuela de Ingenieros Industriales Universidad de Málaga Unidad Asociada al CSIC C. Dr. Ortiz Ramos sn Málaga29071 Spain School of Physics University College Dublin Dublin 4 Ireland Inter-University Centre for Astronomy and Astrophysics Pune411007 India Astrophysics Research Institute Liverpool John Moores University IC2 Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom National Centre for Radio Astrophysics Tata Institute of Fundamental Research Pune University Campus Ganeshkhind Pune411 007 India DIRAC Institute Department of Physics and Astronomy University of Washington 3910 15th Avenue NE SeattleWA98195 United States Nordita KTH Royal Institute of

Gamma-ray bursts (GRBs) are among the brightest and most energetic events in the universe. The duration and hardness distribution of GRBs has two clusters1, now understood to reflect (at least) two different progenitors2. Short-hard GRBs (SGRBs;T90 90 >2 s) have been attributed to the collapse of peculiar massive stars (collapsars)3. The discovery of SN 1998bw/GRB 9804254 marked the first association of a LGRB with a collapsar and AT 2017gfo5/GRB 170817A/GW1708176 marked the first association of a SGRB with a binary neutron star merger, producing also gravitational wave (GW). Here, we present the discovery of ZTF20abwysqy (AT2020scz), a fast-fading optical transient in the Fermi Satellite and the InterPlanetary Network (IPN) localization regions of GRB 200826A;X-ray and radio emission further confirm that this is the afterglow. Follow-up imaging (at rest-frame 16.5 days) reveals excess emission above the afterglow that cannot be explained as an underlying kilonova (KN), but is consistent with being the supernova (SN). Despite the GRB duration being short (rest-frame T90 of 0.65 s), our panchromatic follow-up data confirms a collapsar origin. GRB 200826A is the shortest LGRB found with an associated collapsar;it appears to sit on the brink between a successful and a failed collapsar. Our discovery is consistent with the hypothesis that most collapsars fail to produce ultra-relativistic jets. © 2021, CC BY-NC-ND.

关键词： Gamma rays

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Kitchen-based light tomography - a DIY toolkit for advancing tomography - by and for the tomography community

Tomography of Materials and Structures

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Tomography of Materials and Structures 2023年 1卷

作者： Emanuel Larsson Doğa Gürsoy Stephen A. Hall Division of Solid Mechanics Lund University Ole Römers väg 1 22100 Lund Sweden LUNARC Centre for Scientific and Technical Computing Lund University John Ericssons väg 1 22100 Lund Sweden Lund Institute of Advanced Neutron and X-ray Science (LINXS) Scheelevägen 19 22370 Lund Sweden Institute for Globally Distributed Open Research and Education (IGDORE) Advanced Photon Source Argonne National Laboratory 9700 S Cass Ave Lemont 60439 IL USA Department of Electrical Engineering and Computer Science Northwestern University 2145 Sheridan Rd Evanston 60208 IL USA

We present a recipe for building a portable DIY toolkit, entitled Kitchen-Based Light Tomography (KBLT) for performing tomography using visible light with low-cost and easily accessible components. We also present different use cases to mimic different challenges in tomography, such as imaging time evolving samples. All the software for motor controls, image acquisition, image reconstruction and analysis is open-sourced and available online. The fast acquisition of KBLT datasets permits 4D scanning (3D plus time), also in combination with so-called sample environments, which can support the advancement of improved image reconstruction algorithms. We believe this ‘ Do it yourself ’ (DIY) toolkit will be useful to tomography users, beamline scientists and computational researchers, and the tomography community in general.

关键词： Kitchen-based light tomography KBLT Educational training tool Synchrotron X-ray microtomography SR μ CT XCT Neutron tomography NCT

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Comment on 'Inherent security of phase coding quantum key distribution systems against detector blinding attacks' (2018 Laser Phys. Lett. 15 095203)

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Laser Physics Letters 2018年第1期16卷

作者： Aleksey Fedorov Ilja Gerhardt Anqi Huang Jonathan Jogenfors Yury Kurochkin Antía Lamas-Linares Jan-Åke Larsson Gerd Leuchs Lars Lydersen Vadim Makarov Johannes Skaar Russian Quantum Center Skolkovo Moscow 143025 Russia QRate Skolkovo Moscow 143025 Russia QApp Skolkovo Moscow 143025 Russia Institute of Physics University of Stuttgart and Institute for Quantum Science and Technology Pfaffenwaldring 57 D-70569 Stuttgart Germany Max Planck Institute for Solid State Research Heisenbergstraße 1 D-70569 Stuttgart Germany Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 People's Republic of China Department of Electrical Engineering Linköping University SE-58183 Linköping Sweden Texas Advanced Computing Center The University of Texas at Austin Austin Texas United States of America Max Planck Institute for the Science of Light and University of Erlangen-Nürnberg D-91058 Erlangen Germany Kringsjåvegen 3E NO-7032 Trondheim Norway National University of Science and Technology MISIS Moscow 119049 Russia Department of Technology Systems University of Oslo Box 70 NO-2027 Kjeller Norway

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Roadmap on Electronic Structure Codes in the Exascale Era

arXiv

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arXiv 2022年

作者： Gavini, Vikram Baroni, Stefano Blum, Volker Bowler, David R. Buccheri, Alexander Chelikowsky, James R. Das, Sambit Dawson, William Delugas, Pietro Dogan, Mehmet Draxl, Claudia Galli, Giulia Genovese, Luigi Giannozzi, Paolo Giantomassi, Matteo Gonze, Xavier Govoni, Marco Gulans, Andris Gygi, François Herbert, John M. Kokott, Sebastian Kühne, Thomas D. Liou, Kai-Hsin Miyazaki, Tsuyoshi Motamarri, Phani Nakata, Ayako Pask, John E. Plessl, Christian Ratcliff, Laura E. Richard, Ryan M. Rossi, Mariana Schade, Robert Scheffler, Matthias Schütt, Ole Suryanarayana, Phanish Torrent, Marc Truflandier, Lionel Windus, Theresa L. Xu, Qimen Yu, Victor W.-Z. Perez, Danny Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States SISSA ISAS Trieste Trieste34136 Italy IOM-CNR Trieste Trieste34136 Italy Duke University DurhamNC27708 United States London Centre for Nanotechnology LondonWC1H 0AH United Kingdom WPI-MANA National Institute for Materials Science Ibaraki305-0044 Japan Humboldt-Universität zu Berlin Berlin10117 Germany McKetta Department of Chemical Engineering University of Texas at Austin AustinTX78712 United States Center for Computational Materials Oden Institute University of Texas at Austin AustinTX78712 United States Department of Physics University of Texas at Austin AustinTX78712 United States RIKEN Center for Computational Science Hyogo Kobe650-0047 Japan Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Université Grenoble Alpes MEM L_Sim GrenobleF-38000 France University of Udine Udine33100 Italy IMCN Université catholique de Louvain Louvain-la-neuve Belgium University of Latvia RīgaLV-1586 Latvia University of California DavisCA95616 United States Department of Chemistry & Biochemistry The Ohio State University ColumbusOH43210 United States Berlin14195 Germany Dynamics of Condensed Matter Center for Sustainable Systems Design Chair of Theoretical Chemistry Paderborn University D-33098 Germany Paderborn Center for Parallel Computing Paderborn University D-33098 Germany Department of Computational and Data Sciences Indian Institute of Science Karnataka Bengaluru560012 India Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science Paderborn University D-33098 Germany Centre for Computational Chemistry School of Chemistry University of Bristol BristolBS8 1TS United Kingdom Ames National Laboratory AmesIA50011 United States Iowa State University AmesIA50

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Electronic structure

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The parkes pulsar timing array project: Second data release

arXiv

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arXiv 2020年

作者： Kerr, M. Reardon, D.J. Hobbs, G. Shannon, R.M. Manchester, R.N. Dai, S. Russell, C.J. Zhang, S.-B. van Straten, W. Oslowski, S. Parthasarathy, A. Spiewak, R. Bailes, M. Bhat, N.D.R. Cameron, A.D. Coles, W.A. Dempsey, J. Deng, X. Goncharov, B. Kaczmarek, J.F. Keith, M.J. Lasky, P.D. Lower, M.E. Preisig, B. Sarkissian, J.M. Toomey, L. Wang, H. Wang, J. Zhang, L. Zhu, X. Space Science Division Naval Research Laboratory WashingtonDC20375-5352 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia CSIRO Astronomy and Space Science Australia Telescope National Facility PO Box 76 EppingNSW1710 Australia CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia University of Chinese Academy of Sciences Beijing100049 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210008 China International Centre for Radio Astronomy Research University of Western Australia CrawleyWA6009 Australia Institute for Radio Astronomy & Space Research Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Department of Electrical and Computer Engineering University of California San DiegoCA92093 United States CSIRO Information Management & Technology GPO Box 1700 CanberraACT2601 Australia School of Physics and Astronomy Monash University VIC3800 Australia CSIRO Astronomy and Space Science Parkes Observatory 473 Telescope road ParkesNSW2870 Australia Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester Alan Turing Building Oxford Road ManchesterM13 9PL United Kingdom School of Physics and Electronic Engineering Guangzhou University Guangzhou510006 China Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Urumqi Xinjiang830011 China National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China

We describe 14 years of public data from the Parkes Pulsar Timing Array (PPTA), an ongoing project that is producing precise measurements of pulse times of arrival from 26 millisecond pulsars using the 64-m Parkes radio telescope with a cadence of approximately three weeks in three observing bands. A comprehensive description of the pulsar observing systems employed at the telescope since 2004 is provided, including the calibration methodology and an analysis of the stability of system components. We attempt to provide full accounting of the reduction from the raw measured Stokes parameters to pulse times of arrival to aid third parties in reproducing our results. This conversion is encapsulated in a processing pipeline designed to track provenance. Our data products include pulse times of arrival for each of the pulsars along with an initial set of pulsar parameters and noise models. The calibrated pulse profiles and timing template profiles are also available. These data represent almost 21,000 hrs of recorded data spanning over 14 years. After accounting for processes that induce time-correlated noise, 22 of the pulsars have weighted root-mean-square timing residuals of Copyright © 2020, The Authors. All rights reserved.

关键词： Pulsars

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Multi-scale stamps for real-time classification of alert streams

arXiv

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arXiv 2023年

作者： Reyes-Jainaga, Ignacio Förster, Francisco Muñoz Arancibia, Alejandra M. Cabrera-Vives, Guillermo Bayo, Amelia Bauer, Franz E. Arredondo, Javier Reyes, Esteban Pignata, Giuliano Mourão, A.M. Silva-Farfán, Javier Galbany, Lluís Álvarez, Alex Astorga, Nicolás Castellanos, Pablo Gallardo, Pedro Moya, Alberto Rodríguez, Diego Center for Mathematical Modeling Universidad de Chile Beauchef 851 Santiago8370456 Chile Millennium Institute of Astrophysics Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Departamento de Astronomía Universidad de Chile Chile Department of Computer Science Universidad de Concepción Concepción Chile Data Science Unit Universidad de Concepción Concepción Chile Instituto de Física y Astronomía Universidad de Valparaíso Chile European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany Instituto de Astrofísica Centro de Astroingeniería Facultad de Física Pontificia Universidad Católica de Chile Casilla 306 Santiago22 Chile NeuralWorks Santiago Chile Fintual Administradora General de Fondos S.A. Santiago Chile Instituto de Astrofísica Facultad de Ciencias Exactas Universidad Andres Bello Avda. Fernéndez Concha 700 Las Condes Chile Departamento de Física and CENTRA Center for Astrophysics and Gravitation Instituto Superior Técnico Universidade de Lisboa Portugal Campus UAB Carrer de Can Magrans s/n BarcelonaE-08193 Spain BarcelonaE-08034 Spain Department of Electrical Engineering Universidad de Chile Av. Tupper 2007 Santiago8320000 Chile University of Chile Chile Data Observatory Foundation Santiago Chile

In recent years, automatic classifiers of image cutouts (also called "stamps") have shown to be key for fast supernova discovery. The Vera C. Rubin Observatory will distribute about ten million alerts with their respective stamps each night, enabling the discovery of approximately one million supernovae each year. A growing source of confusion for these classifiers is the presence of satellite glints, sequences of point-like sources produced by rotating satellites or debris. The currently planned Rubin stamps will have a size smaller than the typical separation between these point sources. Thus, a larger field of view stamp could enable the automatic identification of these sources. However, the distribution of larger stamps would be limited by network bandwidth restrictions. We evaluate the impact of using image stamps of different angular sizes and resolutions for the fast classification of events (AGNs, asteroids, bogus, satellites, SNe, and variable stars), using data from the Zwicky Transient Facility. We compare four scenarios: three with the same number of pixels (small field of view with high resolution, large field of view with low resolution, and a multi-scale proposal) and a scenario with the full stamp that has a larger field of view and higher resolution. Compared to small field of view stamps, our multi-scale strategy reduces misclassifications of satellites as asteroids or supernovae, performing on par with high-resolution stamps that are 15 times heavier. We encourage Rubin and its science Collaborations to consider the benefits of implementing multi-scale stamps as a possible update to the alert specification. © 2023, CC BY.

关键词： Supernovae

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Detection of B Cell Lymphomas By a Simple Epigenetic Blood Test

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Blood 2024年 144卷 1582-1582页

作者： Irit Mazza Avivi Miriam Neaman Noa Lowenton-Spier Chava Perry Erel Joffe Roy Vitkon Vlada Bogol Yael Michaeli Noa Gilat Lynne Itelson Shahar Zirkin Jasline Deek Efstathios Kastritis Karolis Sablauskas Skirmante Cernauskiene Vilmantė Vaitekėnaitė Andrius Zucenka Tomas Papajik Eva Kriegova Petr Gajdos Yuval Ebenstein Department of Hematology Tel Aviv Sourasky Medical Center Tel Aviv Israel Faculty of Medicine Tel Aviv University Tel Aviv Israel Department of Chemistry Raymond and Beverly Sackler Faculty of Exact Sciences Tel Aviv University Tel Aviv Israel JaxBio Technologies Ltd. Netanya Israel Department of Clinical Therapeutics National and Kapodistrian University of Athens School of Medicine Athens Greece Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania Hematology Oncology and Transfusion Medicine Center Vilnius University Hospital Santaros Klinikos Vilnius Lithuania Vilnius University Hospital Santaros Klinikos Vilnius Lithuania Hematology and Oncology Department Faculty of Medicine Vilnius University Vilnius Lithuania Department of Hemato-Oncology Faculty of Medicine and Dentistry Palacky University and University Hospital Olomouc Czech Republic Department of Immunology Faculty of Medicine and Dentistry Palacky University and University Hospital Olomouc Czech Republic Department of Computer Science Faculty of Electrical Engineering and Computer Science Technical University of Ostrava Ostrava Czech Republic Department of Biomedical Engineering Faculty of Engineering Tel Aviv Univesity Tel Aviv Israel

Background The SANGUINE project is an EU-funded initiative aimed to address the need for minimally invasive, affordable, and accurate molecular testing for the early detection, pathological characterization, and disease monitoring of hematological malignancies. The project utilizes a novel technology developed by JaxBio, which evaluates methylation profiles of whole blood samples and cell-free DNA (cfDNA) with an immunofluorescence assay that is rapid and uncostly. Here we present an interim result of the SANGUINE trial, for the detection of diffuse large cell B cell lymphoma (DLBCL) and follicular lymphoma (FL) at diagnosis, assuming that future employment of this test in the clinic, would enable to diagnose FL and DLBCL at early disease stages and promote an early detection of relapse. Methods Patients were enrolled in a prospective, multi-center (4 sites), open-label controlled trial (NCT05735704). Inclusion criteria included: age ≥18 years old and diagnosis of FL or DLBCL. Exclusion criteria were prior treatment for anti -NHL, diagnosis of cancer within the last 5 years, HIV diagnosis and active autoimmune disease. Age matched volunteers, with no prior history of HIV, autoimmune disease or cancer served as control. Peripheral blood samples were collected in Streck tubes and processed within 7 to 10 days. Genomic DNA was extracted from the buffy coat, and cell-free DNA was extracted from the plasma of each sample. Epigenetic markers were then fluorescently labeled using a proprietary chemoenzymatic reaction, followed by hybridization to a high-content microarray chip. A deep neural network (DNN) machine learning algorithm was subsequently employed to select 50-100 epigenetic biomarkers, for distinguishing between DLBCL /FL samples and control samples. Results Patient characteristics: Seventy newly diagnosed, previously untreated B cell Non Hodgkin Lymphoma patients: 28 with FL and 42 with DLBCL were included in the study cohort. Sixty-six age matched healthy volu

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