Taxonomy of the order Bunyavirales : second update 2018

In October 2018, the order Bunyavirales was amended by inclusion of the family Arenaviridae, abolishment of three families, creation of three new families, 19 new genera, and 14 new species, and renaming of three genera and 22 species. This article presents the updated taxonomy of the order Bunyavirales as now accepted by the International Committee on Taxonomy of Viruses (ICTV).Non peer reviewe

Taxonomy of the order Bunyavirales : update 2019

In February 2019, following the annual taxon ratification vote, the order Bunyavirales was amended by creation of two new families, four new subfamilies, 11 new genera and 77 new species, merging of two species, and deletion of one species. This article presents the updated taxonomy of the order Bunyavirales now accepted by the International Committee on Taxonomy of Viruses (ICTV).Peer reviewe

Taxonomy of the order Bunyavirales: update 2019

In February 2019, following the annual taxon ratification vote, the order Bunyavirales was amended by creation of two new families, four new subfamilies, 11 new genera and 77 new species, merging of two species, and deletion of one species. This article presents the updated taxonomy of the order Bunyavirales now accepted by the International Committee on Taxonomy of Viruses (ICTV)

Taxonomy of the order Bunyavirales : update 2019

In February 2019, following the annual taxon ratification vote, the order Bunyavirales was amended by creation of two new families, four new subfamilies, 11 new genera and 77 new species, merging of two species, and deletion of one species. This article presents the updated taxonomy of the order Bunyavirales now accepted by the International Committee on Taxonomy of Viruses (ICTV).This work was supported in part through Battelle Memorial Institute’s prime contract with the US National Institute of Allergy and Infectious Diseases (NIAID) under Contract no. HHSN272200700016I (J. H. K.). This work was also funded in part by Grant 109520 by the UK Department of Health, Public Health England (R. H.). W. M. S. is supported by Fundação de Amparo à Pesquisa do Estado de São Paulo, Brazil (17/13981-0). This work was supported by the Intergovernmental Special Program of State Key Research and Development Plan from the Ministry of Science and Technology of China (2016YFE0113500) and European Union’s Horizon 2020 EVAg project (no. 653316).http://link.springer.com/journal/7052020-07-01hj2019Medical Virolog

DUNE Offline Computing Conceptual Design Report

This document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype