3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum

A web portal for Portuguese brain imaging network

Mestrado em Engenharia de Computadores e TelemáticaA Imagiologia Cerebral (IC) está na fronteira entre a neurologia, engenharia e física. écnicas de imagens médicas multimodais, tais como a Ressonância Magnética (MRI e fMRI) e Espectroscopia (MRS), Tomografia Computadorizada por Emissão de Fotões/Positrões (SPECT/PET), entre outros, são emergentes ferramentas de pesquisa médica que pode fornecer informações valiosas para o diagnóstico de doenças do cérebro. Eletroencefalograma de alta resolução (HR-EEG), técnicas para sincronizar e fundir seus resultados de análise e várias técnicas de imagem são também parte de IC. Em Portugal, dado o facto que a maioria das áreas relacionadas com IC (por exemplo, medicina, engenharia ou física) são assuntos de investigação em muitos grupos de P&D, um consórcio de universidades de Aveiro, Coimbra, Minho e Porto criou a Rede Nacional de Imagiologia Funcional Cerebral (RNIFC). A RNIFC é uma associação sem fins lucrativos que foi formalizada e assinada em fevereiro de 2009. Actualmente, com o suporte de sistemas digitais para armazenar imagens médicas, é possível partilhar dados entre essas instituições para melhorar o diagnóstico, e permitir investigações entre a comunidade médica de diferentes instituições. O principal objectivo desta dissertação é descrever a implementação dos serviços de sistemas de informação essenciais para a Brain Imaging Network (BIN) que suportam actualmente o RNIFC acessível através do Portal BIN, o principal ponto de entrada para a BING. O Portal BIN permite aos pesquisadores na comunidade BING espalhadas pelo país e no estrangeiro, quer para solicitar o acesso a instrumentos científicos ou para recuperar os seus casos e executar as suas análises. ABSTRACT: Brain Imaging is in the frontier between neurology, engineering and physics. Multimodal medical imaging techniques, such as Magnetic Resonance Imaging (MRI and fMRI) and Spectroscopy (MRS), Single Photon/Positron Emitting Tomography (SPECT/PET) among others, are emergent medical research tools that can provide valuable information for diagnosis of brain diseases. High-resolution electroencephalogram (HR-EEG), techniques for synchronizing and fuse its analysis results and several imaging techniques are also part of BI. In Portugal, given fact that most of the BI related areas (e.g. medical, engineering or physics) are subjects of research in many R&D groups, a consortium of the universities of Aveiro, Coimbra, Minho and Porto created the National Functional Brain Imaging Network (RNIFC). The RNIFC is a non-profitable association that was formalized and signed in February 2009. Currently, with the support of digital systems to store medical images, it is possible to share data among these institutions to improve diagnosis, and allow investigations by the medical community among different institutions. The main objective of this thesis is to describe the implementation of the essential Brain Imaging Network (BIN) information systems services that currently support the RNIFC accessible through the BIN Portal, the main entry point for the BING. BIN Portal enables researchers in the BING community scattered along the country and abroad either to apply for access to the scientific instruments or to retrieve their cases and run their analysis

LHCb distributed data analysis on the computing grid

LHCb is one of the four Large Hadron Collider (LHC) experiments based at CERN, the European Organisation for Nuclear Research. The LHC experiments will start taking an unprecedented amount of data when they come online in 2007. Since no single institute has the compute resources to handle this data, resources must be pooled to form the Grid. Where the Internet has made it possible to share information stored on computers across the world, Grid computing aims to provide access to computing power and storage capacity on geographically distributed systems. LHCb software applications must work seamlessly on the Grid allowing users to efficiently access distributed compute resources. It is essential to the success of the LHCb experiment that physicists can access data from the detector, stored in many heterogeneous systems, to perform distributed data analysis. This thesis describes the work performed to enable distributed data analysis for the LHCb experiment on the LHC Computing Grid

From "Low Hanging" to "User Ready": Initial Steps into a HealthGrid

Grids offer powerful infrastructures and promising concepts for the development and deployment of advanced applications in medical research and healthcare. The construction of HealthGrids in practice, however, is challenging due to reasons of scientific, technical, and cultural nature, among them the large gap between communities that develop and use the technology. Whereas grid developments focus mostly on functionality, usability issues are also very important to enable the potential of grids to be fully exploited by those who could mostly benefit from it, the end-users. In this paper we make a retrospective of our efforts to develop the Virtual Lab for functional Magnetic Resonance Imaging (fMRI). This project aims at providing for the end-users a grid-based system to facilitate research and clinical usage of fMRI data for study of brain activation. We present the evolution of this project in three phases coined "low hanging fruit", "trying out" and "end-user ready", and the lessons learnt in each one. The evolution of the software architecture, which had a large impact on the user front-end, is discussed in more detail. The current architecture facilitates the construction of front-ends that enable users to access the grid infrastructure from a single user-friendly GUI. All (local and grid) resources are accessed directly by the users from a virtual desktop implemented by the Virtual Resource Browser (VBrowser

On-demand distributed image processing over an adaptive Campus-Grid

This thesis explores how scientific applications, which are based upon short jobs (seconds and minutes) can capitalize upon the idle workstations of a Campus-Grid. These resources are donated on a voluntary basis, and consequently, the Campus-Grid is constantly adapting and the availability of workstations changes. Typically, to utilize these resources a Condor system or equivalent would be used. However, such systems are designed with different trade-offs and incentives in mind and therefore do not provide intrinsic support for short jobs. The motivation for creating a provisioning scenario for short jobs is that Image Processing, as well as other areas of scientific analysis, are typically composed of short running jobs, but still require parallel solutions. Much of the literature in this area comments on the challenges of performing such analysis efficiently and effectively even when dedicated resources are in use. The main challenges are: latency and scheduling penalties, granularity and the potential for very short jobs. A volunteer Grid retains these challenges but also adds further challenges. These can be summarized as: unpredictable re source availability and longevity, multiple machine owners and administrators who directly affect the operating environment. Ultimately, this creates the requirement for well conceived and effective fault management strategies. However, these are typically not in place to enable transparent fault-free job administration for the user. This research demonstrates that these challenges are answerable, and that in doing so opportunistically sourced Campus-Grid resources can host disparate applications constituted of short running jobs, of as little as one second in length. This is demonstrated by the significant improvements in performance when the system presented here was compared to a well established Condor system. Here, improvements are increased job efficiency from 60–70% to 95%–100%, up to a 99% reduction in application makespan and up to a 13000% increase in the efficiency of resource utilization. The Condor pool in use is approximately 1,600 workstations distributed across 27 administrative domains of Cardiff University. The application domain of this research is Matlab-based image processing, and the application area used to demonstrate the approach is the analysis of Magnetic Resonance Imagery (MRI). However, the presented approach is generalizable to any application domain with similar characteristics

Creating and utilising the Wales Asthma Observatory to support health policy, health service planning and clinical research

Asthma is a public health challenge in Wales. In order and improve its outcomes and reduce its burden, reliable evidence on disease epidemiology is needed. In this thesis, I describe the development of a platform for asthma surveillance and research in Wales using routinely collected electronic health record (EHR) data in the Secure Anonymised Information Linkage (SAIL) Databank.To inform the development of operational definitions for asthma and its outcomes, I examine the contemporary approaches to defining asthma and assessing its out-comes using EHR data, and describe significant variations and suboptimal report-ing on these approaches. I highlight the need for valid, standardised methods to study asthma, and emphasise the increasing demand for improved reporting to support research transparency and reproducibility.Acknowledging the infeasibility of reference standards to define asthma in SAIL, I describe the development of latent class model to identify asthma patients in this databank. I assess the performance of this model in relation to other objective and self-reported measures of asthma.I also describe other methodological aspects of the development of the Wales Asthma Observatory, including asthma data profiling and identification of impor-tant data gaps.To demonstrate the Observatory’s utility for health policy and service planning, I highlight the variations in asthma epidemiology in Wales across age groups, gen-der, and socioeconomic deprivation levels. I found that asthma patients living in the most deprived areas had higher healthcare utilisation for asthma, indicating worse disease control, than those in the least deprived areas.Finally, I reflect on the experience of developing the Wales Asthma Observatory, recognising its strengths and limitations, and identify opportunities and challenges of maximising the use of routine data towards a learning health system for asthma in Wales