Data Bridge: solving diverse Data Access in Scientific Applications

The nature of data for scientific computation is very diverse in the age of big data. First, it may be available at a number of locations, e.g. the scientist’s machine, some institutional filesystem, a remote service, or some sort of database. Second, the size of the data may vary from a few kilobytes to many terabytes. In order to be available for computation, data has to be transferred to the location where the computation takes place. This requires a diverse set of middleware tools that are compatible both with the data and the compute resources. However, using this tools requires additional knowledge and makes running the experiments an inconvenient task. In this paper we present the Data Bridge, a high-level service that can be used easily in scientific computations to perform data transfer to and from a diverse set of storage services. The Data Bridge not only unifies access to different types of storage services, but it can also be used at different levels (e.g., single jobs, parameter sweeps, scientific workflows) in scientific computations

A Secure Cloud-based Platform to Host Healthcare Applications

Digital technologies, such as Big Data analytics, artificial intelligence, cloud and high-performance computing are presenting new opportunities to transform healthcare systems, increase connectivity of hospitals and other providers, and therefore potentially and significantly improve patient care. However, such networked computing infrastructures also raise significant cybersecurity risks, especially in the healthcare domain, where protecting sensitive personal information is of paramount importance. Project ASCLEPIOS aims at strengthening the trust of users in cloud-based healthcare services by utilizing trusted execution environment and several modern cryptographic approaches such as attribute based encryption, searchable encryption, functional encryption to build a cloud-based e-health framework that protects users’ privacy, prevents both internal and external attacks, verifies the integrity of medical devices before application, and runs privacy-preserving data analytics on encrypted data. The project investigates modern encryption techniques and their combination in order to provide increased security of e-health applications that are then presented towards end-users utilizing a cloud-based platform. Although some topics such as security and privacy are already investigated through block-chain related technologies, it has been decided that the selected approaches would be more suitable for these particular challenges. In order to prototype its security services, ASCLEPIOS develops and deploys three large-scale healthcare demonstrators, provided by three leading hospitals from Europe. These demonstrators are rooted in the practice-based problems and applications provided by the project’s healthcare partners. The Amsterdam University Centers, University of Amsterdam, plans to improve stroke hyper-acute care through secure information sharing on a cloud computing platform to improve patient management. Additionally, they are also building prediction models to enable earlier discharge of patients from hospitals with lower risk factors. Charité Berlin plans to improve inpatient and outpatient sleep medication by remotely controlling the quality of the collected data and transferring it on-line for further analysis. Finally, the Norwegian Centre for e-health Research, University Hospital of North Norway is developing a system for privacy-preserving monitoring and benchmarking of antibiotics prescription of general practitioners. The common characteristics of these three scenarios are the increased demand for high levels of security in data transfer, storage and privacy preserving analytics on cloud infrastructures. In order deploy, operate and further develop these applications to increase their security with the ASCLEPIOS framework, a cloud computing testbed is being setup. The testbed uses state-of-the-art technologies for cloud application deployment and run-time orchestration in order to ensure the optimized deployment and execution of the demonstrator applications. As the data sources do not require the local execution (albeit in one case data may remain on the data source) of processing, there is no need for fog or edge computing, but the testbed is based on private OpenStack cloud computing infrastructures and utilizes the MiCADO framework which is compatible with different containers such as Docker and Kubernetes. The project started only recently, and currently it is in the early stages of systems design and specification. This presentation will provide a short introduction to the ASCLEPIOS project and its demonstrators and will present early results of the currently ongoing requirements specification and platform design processes

Accelerating regional atrophy rates in the progression from normal aging to Alzheimer’s disease

We investigated progression of atrophy in vivo, in Alzheimer’s disease (AD), and mild cognitive impairment (MCI). We included 64 patients with AD, 44 with MCI and 34 controls with serial MRI examinations (interval 1.8 ± 0.7 years). A nonlinear registration algorithm (fluid) was used to calculate atrophy rates in six regions: frontal, medial temporal, temporal (extramedial), parietal, occipital lobes and insular cortex. In MCI, the highest atrophy rate was observed in the medial temporal lobe, comparable with AD. AD patients showed even higher atrophy rates in the extramedial temporal lobe. Additionally, atrophy rates in frontal, parietal and occipital lobes were increased. Cox proportional hazard models showed that all regional atrophy rates predicted conversion to AD. Hazard ratios varied between 2.6 (95% confidence interval (CI) = 1.1–6.2) for occipital atrophy and 15.8 (95% CI = 3.5–71.8) for medial temporal lobe atrophy. In conclusion, atrophy spreads through the brain with development of AD. MCI is marked by temporal lobe atrophy. In AD, atrophy rate in the extramedial temporal lobe was even higher. Moreover, atrophy rates also accelerated in parietal, frontal, insular and occipital lobes. Finally, in nondemented elderly, medial temporal lobe atrophy was most predictive of progression to AD, demonstrating the involvement of this region in the development of AD

BioClimate: a Science Gateway for Climate Change and Biodiversity research in the EUBrazilCloudConnect project

[EN] Climate and biodiversity systems are closely linked across a wide range of scales. To better understand the mutual interaction between climate change and biodiversity there is a strong need for multidisciplinary skills, scientific tools, and access to a large variety of heterogeneous, often distributed, data sources. Related to that, the EUBrazilCloudConnect project provides a user-oriented research environment built on top of a federated cloud infrastructure across Europe and Brazil, to serve key needs in different scientific domains, which is validated through a set of use cases. Among them, the most data-centric one is focused on climate change and biodiversity research. As part of this use case, the BioClimate Science Gateway has been implemented to provide end-users transparent access to (i) a highly integrated user-friendly environment, (ii) a large variety of data sources, and (iii) different analytics & visualization tools to serve a large spectrum of users needs and requirements. This paper presents a complete overview of BioClimate and the related scientific environment, in particular its Science Gateway, delivered to the end-user community at the end of the project.This work was supported by the EU FP7 EUBrazilCloudConnect Project (Grant Agreement 614048), and CNPq/Brazil (Grant Agreement no 490115/2013-6).Fiore, S.; Elia, D.; Blanquer Espert, I.; Brasileiro, FV.; Nuzzo, A.; Nassisi, P.; Rufino, LAA.... (2019). BioClimate: a Science Gateway for Climate Change and Biodiversity research in the EUBrazilCloudConnect project. Future Generation Computer Systems. 94:895-909. https://doi.org/10.1016/j.future.2017.11.034S8959099

Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks

Background and purpose: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice. Objective: To assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke. Materials and methods: We included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentati

The Global Impact of Science Gateways, Virtual Research Environments and Virtual Laboratories

Science gateways, virtual laboratories and virtual research environments are all terms used to refer to community-developed digital environments that are designed to meet a set of needs for a research community. Specifically, they refer to integrated access to research community resources including software, data, collaboration tools, workflows, instrumentation and high-performance computing, usually via Web and mobile applications. Science gateways, virtual laboratories and virtual research environments are enabling significant contributions to many research domains, facilitating more efficient, open, reproducible research in bold new ways. This paper explores the global impact achieved by the sum effects of these programs in increasing research impact, demonstrates their value in the broader digital landscape and discusses future opportunities. This is evidenced through examination of national and international programs in this field