Two new automated, compared with two enzyme-linked immunosorbent, antimüllerian hormone assays

Objective: To compare new automated antimüllerian hormone (AMH) assay performance characteristics from the new automated Elecsys AMH (Roche; Elecsys) and Access AMH (Beckman Coulter; Access) assays with the existing AMH Gen II ELISA (enzyme-linked immunosorbent assay; Gen II; Beckman Coulter) and AMH ELISA (Ansh Labs) assays. Design: Prospective assay evaluation. Setting: University-affiliated clinical chemistry laboratory. Patient(s): Patients referred for serum AMH measurement (n = 83) before start of in vitro fertilization cycle between September 2014 and October 2014. Intervention(s): None. Main Outcome Measure(s): Serum AMH concentration.: Result(s): Intra-assay coefficients of variation were low; Ansh ≤ 9.0%; Gen II ≤ 5.8%; Access ≤ 10.7%; and Elecsys ≤ 2.8%. The Passing-Bablok regression equations (pmol/L) were y (Access) = 0.128 + (0.781 × Gen II); and y (Access) = 0.302 + (0.742 x Ansh). For y (Elecys) = 0.087 + (0.729 x Gen II) and y (Elecys) = 0.253 + (0.688 x Ansh Labs). For y (Elecys) = 0.943 − (0.037 × Access). For all the assays, AMH exhibited a moderate positive correlation with AFC (r = 0.62–0.64); number of cumulus oocyte complexes (r = 0.60–0.64); and metaphase II oocytes (r = 0.48–0.50). Accuracy of pregnancy prediction, as determined by area under the receiver operating characteristic curve, was uniformly low for all assays (0.62–0.63). Conclusion(s): The novel automated assays exhibit strong concordance in calibration, but derived values are substantially lower than those obtained from pre-existing assays, with assay-specific interpretation required for routine clinical use. These results highlight the need for an international standard of measurement of AMH

A FAIR based approach to data sharing in Europe

The European fusion research activities have, over recent decades, generated a vast and varied set of data. The volume and diversity of the data that need to be catalogued and annotated make the task of organising and making the data available within a broader environment very challenging. Nevertheless, there are strong scientific drivers as well as incentives and mandates from national research agencies suggesting that a more coherent approach to data referencing, dissemination and sharing would provide strong benefits to the fusion research community and beyond. Here, we discuss the technical requirements and developments needed to transition the current, and future, range of fusion research data to an open and Findable, Accessible, Interoperable, and Reusable data sharing structure guided by the principle \u27as open as possible, as closed as necessary\u27. Here we propose a set of recommendations and technical implementations needed to form a European data sharing environment for the fusion research programmes. Consistency with the emerging IMAS (ITER Integrated Modelling and Analysis Suite) infrastructure is considered to facilitate future deployments

A Cloud-Based Framework for Machine Learning Workloads and Applications

[EN] In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.This work was supported by the project DEEP-Hybrid-DataCloud ``Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud'' that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant 777435Lopez Garcia, A.; Marco De Lucas, J.; Antonacci, M.; Zu Castell, W.; David, M.; Hardt, M.; Lloret Iglesias, L.... (2020). A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access. 8:18681-18692. https://doi.org/10.1109/ACCESS.2020.2964386S1868118692

High-sensitivity study of levels in Al-30 following beta decay of Mg-30

gamma-ray and fast-timing spectroscopy were used to study levels in Al-30 populated following the beta(-) decay of Mg-30. Five new transitions and three new levels were located in Al-30. A search was made to identify the third 1(+) state expected at an excitation energy of similar to 2.5 MeV. Two new levels were found, at 3163.9 and 3362.5 keV, that are firm candidates for this state. Using the advanced time-delayed (ATD) beta gamma gamma (t) method we have measured the lifetime of the 243.8-keV state to be T-1/2 = 15(4) ps, which implies that the 243.8-keV transition is mainly of M1 character. Its fast B(M1; 2(+) -> 3(+)) value of 0.10(3) W.u. is in very good agreement with the USD shell-model prediction of 0.090 W.u. The 1801.5-keV level is the only level observed in this study that could be a candidate for the second excited 2(+) state.Peer reviewe

Recent EUROfusion Achievements in Support of Computationally Demanding Multiscale Fusion Physics Simulations and Integrated Modeling

Integrated modeling (IM) of present experiments and future tokamak reactors requires the provision of computational resources and numerical tools capable of simulating multiscale spatial phenomena as well as fast transient events and relatively slow plasma evolution within a reasonably short computational time. Recent progress in the implementation of the new computational resources for fusion applications in Europe based on modern supercomputer technologies (supercomputer MARCONI-FUSION), in the optimization and speedup of the EU fusion-related first-principle codes, and in the development of a basis for physics codes/modules integration into a centrally maintained suite of IM tools achieved within the EUROfusion Consortium is presented. Physics phenomena that can now be reasonably modelled in various areas (core turbulence and magnetic reconnection, edge and scrape-off layer physics, radio-frequency heating and current drive, magnetohydrodynamic model, reflectometry simulations) following successful code optimizations and parallelization are briefly described. Development activities in support to IM are summarized. They include support to (1) the local deployment of the IM infrastructure and access to experimental data at various host sites, (2) the management of releases for sophisticated IM workflows involving a large number of components, and (3) the performance optimization of complex IM workflows.This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014 to 2018 under grant agreement 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission or ITER.Peer ReviewedPostprint (published version

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

INDIGO-DataCloud: A data and computing platform to facilitate seamless access to e-infrastructures

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