Genetic addiction risk severity assessment identifies polymorphic reward genes as antecedents to reward deficiency syndrome (RDS) hypodopaminergia\u27s effect on addictive and non-addictive behaviors in a nuclear family

This case series presents the novel genetic addiction risk score (GARS), which shows a high prevalence of polymorphic risk alleles of reward genes in a nuclear family with multiple reward deficiency syndrome (RDS) behavioral issues expressing a hypodopaminergic antecedent. The family consists of a mother, father, son, and daughter. The mother experienced issues with focus, memory, anger, and amotivational syndrome. The father experienced weight issues and depression. The son experienced heavy drinking, along with some drug abuse and anxiety. The daughter experienced depression, lethargy, brain fog, focus issues, and anxiety, among others. A major clinical outcome of the results presented to the family members helped reduce personal guilt and augment potential hope for future healing. Our laboratory\u27s prior research established that carriers of four or more alleles measured by GARS

Performance studies of the Belle II Silicon Vertex Detector with data taken at the DESY test beam in April 2016

Belle II is a multipurpose detector currently under construction which will be operated at the next generation B-factory SuberKEKB in Japan. Its main devices for the vertex reconstruction are the Silicon Vertex Detector (SVD) and the Pixel Detector (PXD). In April 2016 a sector of the Belle II SVD and PXD have been tested in a beam of high energetic electrons at the test beam facility at DESY Hamburg (Germany). We report here the results for the hit efficiency estimation and the measurement of the resolution for the Belle II silicon vertex etector. We find that the hit efficiencies are on average above 99.5% and that the measured resolution is within the expectations

Performance studies of the Belle II Silicon Vertex Detector with data taken at the DESY test beam in April 2016

Belle II is a multipurpose detector currently under construction which will be operated at the next generation B-factory SuberKEKB in Japan. Its main devices for the vertex reconstruction are the Silicon Vertex Detector (SVD) and the Pixel Detector (PXD). In April 2016 a sector of the Belle II SVD and PXD have been tested in a beam of high energetic electrons at the test beam facility at DESY Hamburg (Germany). We report here the results for the hit efficiency estimation and the measurement of the resolution for the Belle II silicon vertex etector. We find that the hit efficiencies are on average above 99.5% and that the measured resolution is within the expectations

The Belle II SVD detector

The Silicon Vertex Detector (SVD) is one of the main detectors in the Belle II experiment at KEK, Japan. In combination with a pixel detector, the SVD determines precise decay vertex and low-momentum track reconstruction. The SVD ladders are being developed at several institutes. For the development of the tracking algorithm as well as the performance estimation of the ladders, beam tests for the ladders were performed. We report an overview of the SVD development, its performance measured in the beam test, and the prospect of its assembly and commissioning until installation

Fermi LAT observations of cosmic-ray electrons from 7 GeV to 1 TeV

We present the results of our analysis of cosmic-ray electrons using about 8 million electron candidates detected in the first 12 months on-orbit by the Fermi Large Area Telescope. This work extends our previously-published cosmic-ray electron spectrum down to 7 GeV, giving a spectral range of approximately 2.5 decades up to 1 TeV. We describe in detail the analysis and its validation using beam-test and on-orbit data. In addition, we describe the spectrum measured via a subset of events selected for the best energy resolution as a cross-check on the measurement using the full event sample. Our electron spectrum can be described with a power law $\propto {\rm E}^{-3.08 \pm 0.05}$ with no prominent spectral features within systematic uncertainties. Within the limits of our uncertainties, we can accommodate a slight spectral hardening at around 100 GeV and a slight softening above 500 GeV.Comment: 20 pages, 23 figures, 2 tables, published in Physical Review D 82, 092004 (2010) - contact authors: C. Sgro', A. Moisee

Preparedness and response to the covid-19 emergency: Experience from the teaching hospital of Pisa, Italy

In Italy, the coronavirus disease 2019 (COVID-19) emergency took hold in Lombardy and Veneto at the end of February 2020 and spread unevenly among the other regions in the following weeks. In Tuscany, the progressive increase of hospitalized COVID-19 patients required the set-up of a regional task force to prepare for and effectively respond to the emergency. In this case report, we aim to describe the key elements that have been identified and implemented in our center, a 1082-bed hospital located in the Pisa district, to rapidly respond to the COVID-19 outbreak in order to guarantee safety of patients and healthcare workers

INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures

[EN] This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications.INDIGO-Datacloud has been funded by the European Commision H2020 research and innovation program under grant agreement RIA 653549.Salomoni, D.; Campos, I.; Gaido, L.; Marco, J.; Solagna, P.; Gomes, J.; Matyska, L.... (2018). INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures. Journal of Grid Computing. 16(3):381-408. https://doi.org/10.1007/s10723-018-9453-3S381408163García, A.L., Castillo, E.F.-d., Puel, M.: Identity federation with VOMS in cloud infrastructures. In: 2013 IEEE 5Th International Conference on Cloud Computing Technology and Science, pp 42–48 (2013)Chadwick, D.W., Siu, K., Lee, C., Fouillat, Y., Germonville, D.: Adding federated identity management to OpenStack. Journal of Grid Computing 12(1), 3–27 (2014)Craig, A.L.: A design space review for general federation management using keystone. In: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp 720–725. IEEE Computer Society (2014)Pustchi, N., Krishnan, R., Sandhu, R.: Authorization federation in iaas multi cloud. In: Proceedings of the 3rd International Workshop on Security in Cloud Computing, pp 63–71. ACM (2015)Lee, C.A., Desai, N., Brethorst, A.: A Keystone-Based Virtual Organization Management System. In: 2014 IEEE 6Th International Conference On Cloud Computing Technology and Science (Cloudcom), pp 727–730. IEEE (2014)Castillo, E.F.-d., Scardaci, D., García, A.L.: The EGI Federated Cloud e-Infrastructure. Procedia Computer Science 68, 196–205 (2015)AARC project: AARC Blueprint Architecture, see https://aarc-project.eu/architecture . Technical report (2016)Oesterle, F., Ostermann, S., Prodan, R., Mayr, G.J.: Experiences with distributed computing for meteorological applications: grid computing and cloud computing. Geosci. Model Dev. 8(7), 2067–2078 (2015)Plasencia, I.C., Castillo, E.F.-d., Heinemeyer, S., García, A.L., Pahlen, F., Borges, G.: Phenomenology tools on cloud infrastructures using OpenStack. The European Physical Journal C 73(4), 2375 (2013)Boettiger, C.: An introduction to docker for reproducible research. ACM SIGOPS Operating Systems Review 49(1), 71–79 (2015)Docker: http://www.docker.com (2013)Gomes, J., Campos, I., Bagnaschi, E., David, M., Alves, L., Martins, J., Pina, J., Alvaro, L.-G., Orviz, P.: Enabling rootless linux containers in multi-user environments: the udocker tool. Computing Physics Communications. https://doi.org/10.1016/j.cpc.2018.05.021 (2018)Zhang, Z., Chuan, W., Cheung, D.W.L.: A survey on cloud interoperability taxonomies, standards, and practice. SIGMETRICS perform. Eval. Rev. 40(4), 13–22 (2013)Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments. Journal of Grid Computing 12(4), 559–592 (2014)Nyrén, R., Metsch, T., Edmonds, A., Papaspyrou, A.: Open Cloud Computing Interface–Core. Technical report, Open Grid Forum (2010)Metsch, T., Edmonds, A.: Open Cloud Computing Interface-Infrastructure. Technical report, Open Grid Forum (2010)Metsch, T., Edmonds, A.: Open Cloud Computing Interface-RESTful HTTP Rendering. Technical report, Open Grid Forum (2011)(Ca Technologies) Lipton, P., (Ibm) Moser, S., (Vnomic) Palma, D., (Ibm) Spatzier, T.: Topology and Orchestration Specification for Cloud Applications. Technical report, OASIS Standard (2013)Teckelmann, R., Reich, C., Sulistio, A.: Mapping of cloud standards to the taxonomy of interoperability in IaaS. In: Proceedings - 2011 3rd IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2011, pp 522–526 (2011)García, A.L., Castillo, E.F.-d., Fernández, P.O.: Standards for enabling heterogeneous IaaS cloud federations. Computer Standards & Interfaces 47, 19–23 (2016)Caballer, M., Zala, S., García, A.L., Montó, G., Fernández, P.O., Velten, M.: Orchestrating complex application architectures in heterogeneous clouds. Journal of Grid Computing 16 (1), 3–18 (2018)Hardt, M., Jejkal, T., Plasencia, I.C., Castillo, E.F.-d., Jackson, A., Weiland, M., Palak, B., Plociennik, M., Nielsson, D.: Transparent Access to Scientific and Commercial Clouds from the Kepler Workflow Engine. Computing and Informatics 31(1), 119 (2012)Fakhfakh, F., Kacem, H.H., Kacem, A.H.: Workflow Scheduling in Cloud Computing a Survey. In: IEEE 18Th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations (EDOCW), 2014, Vol. 71, pp. 372–378. Springer, New York (2014)Stockton, D.B., Santamaria, F.: Automating NEURON simulation deployment in cloud resources. Neuroinformatics 15(1), 51–70 (2017)Plóciennik, M., Fiore, S., Donvito, G., Owsiak, M., Fargetta, M., Barbera, R., Bruno, R., Giorgio, E., Williams, D.N., Aloisio, G.: Two-level Dynamic Workflow Orchestration in the INDIGO DataCloud for Large-scale, Climate Change Data Analytics Experiments. Procedia Computer Science 80, 722–733 (2016)Moreno-Vozmediano, R., Montero, R.S., Llorente, I.M.: Multicloud deployment of computing clusters for loosely coupled mtc applications. IEEE transactions on parallel and distributed systems 22(6), 924–930 (2011)Katsaros, G., Menzel, M., Lenk, A.: Cloud Service Orchestration with TOSCA, Chef and Openstack. In: Ic2e (2014)Garcia, A.L., Zangrando, L., Sgaravatto, M., Llorens, V., Vallero, S., Zaccolo, V., Bagnasco, S., Taneja, S., Dal Pra, S., Salomoni, D., Donvito, G.: Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers. J. Phys. Conf. Ser. 898(9), 92010 (2017)Singh, S., Chana, I.: A survey on resource scheduling in cloud computing issues and challenges. Journal of Grid Computing, pp. 1–48 (2016)García, A.L., Castillo, E.F.-d., Fernández, P.O., Plasencia, I.C., de Lucas, J.M.: Resource provisioning in Science Clouds: Requirements and challenges. Software: Practice and Experience 48(3), 486–498 (2018)Chauhan, M.A., Babar, M.A., Benatallah, B.: Architecting cloud-enabled systems: a systematic survey of challenges and solutions. Software - Practice and Experience 47(4), 599–644 (2017)Somasundaram, T.S., Govindarajan, K.: CLOUDRB A Framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud. Futur. Gener. Comput. Syst. 34, 47–65 (2014)Sotomayor, B., Keahey, K., Foster, I.: Overhead Matters: A Model for Virtual Resource Management. In: Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing SE - VTDC ’06, p 5. IEEE Computer Society, Washington (2006)SS, S.S., Shyam, G.K., Shyam, G.K.: Resource management for Infrastructure as a Service (IaaS) in cloud computing SS Manvi A survey. J. Netw. Comput. Appl. 41, 424–440 (2014)INDIGO-DataCloud consortium: Initial requirements from research communities - d2.1, see https://www.indigo-datacloud.eu/documents/initial-requirements-research-communities-d21 https://www.indigo-datacloud.eu/documents/initial-requirements-research-communities-d21 https://www.indigo-datacloud.eu/documents/initial-requirements-research-communities-d21 . Technical report (2015)Europen open science cloud: https://ec.europa.eu/research/openscience (2015)Proot: https://proot-me.github.io/ (2014)Runc: https://github.com/opencontainers/runc (2016)Fakechroot: https://github.com/dex4er/fakechroot (2015)Pérez, A., Moltó, G., Caballer, M., Calatrava, A.: Serverless computing for container-based architectures Future Generation Computer Systems (2018)de Vries, K.J.: Global fits of supersymmetric models after LHC run 1. Phd thesis Imperial College London (2015)Openstack: https://www.openstack.org/ (2015)See http://argus-documentation.readthedocs.io/en/stable/argus_introduction.html (2017)See https://en.wikipedia.org/wiki/xacml (2013)See http://www.simplecloud.info (2014)Opennebula: http://opennebula.org/ (2018)Redhat openshift: http://www.opencityplatform.eu (2011)The cloud foundry foundation: https://www.cloudfoundry.org/ (2015)Caballer, M., Blanquer, I., Moltó, G., de Alfonso, C.: Dynamic management of virtual infrastructures. Journal of Grid Computing 13(1), 53–70 (2015)See http://www.infoq.com/articles/scaling-docker-with-kubernetes http://www.infoq.com/articles/scaling-docker-with-kubernetes (2014)Prisma project: http://www.ponsmartcities-prisma.it/ (2010)Opencitiy platform: http://www.opencityplatform.eu (2014)Onedata: https://onedata.org/ (2018)Dynafed: http://lcgdm.web.cern.ch/dynafed-dynamic-federation-project http://lcgdm.web.cern.ch/dynafed-dynamic-federation-project (2011)Fts3: https://svnweb.cern.ch/trac/fts3 (2011)Fernández, P.O., García, A.L., Duma, D.C., Donvito, G., David, M., Gomes, J.: A set of common software quality assurance baseline criteria for research projects, see http://hdl.handle.net/10261/160086 . Technical reportHttermann, M.: Devops for developers Apress (2012)EOSC-Hub: ”Integrating and managing services for the European Open Science Cloud” Funded by H2020 research and innovation pr ogramme under grant agreement No. 777536. See http://eosc-hub.eu (2018)Apache License: author = https://www.apache.org/licenses/LICENSE-2.0 (2004)INDIGO Package Repo: http://repo.indigo-datacloud.eu/ (2017)INDIGO DockerHub: https://hub.docker.com/u/indigodatacloud/ https://hub.docker.com/u/indigodatacloud/ (2015)Indigo gitbook: https://indigo-dc.gitbooks.io/indigo-datacloud-releases https://indigo-dc.gitbooks.io/indigo-datacloud-releases (2017)Van Zundert, G.C., Bonvin, A.M.: Disvis: quantifying and visualizing the accessible interaction space of distance restrained biomolecular complexes. Bioinformatics 31(19), 3222–3224 (2015)Van Zundert, G.C., Bonvin, A.M.: Fast and sensitive rigid–body fitting into cryo–em density maps with powerfit. AIMS Biophys. 2(0273), 73–87 (2015