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Now showing 1 - 4 of 4
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    Magnetofluidic platform for multidimensional magnetic and optical barcoding of droplets
    (Cambridge : RSC, 2014) Lin, Gungun; Makarov, Denys; Medina-Sánchez, Mariana; Guix, Maria; Baraban, Larysa; Cuniberti, Gianaurelio; Schmidt, Oliver G.
    We present a concept of multidimensional magnetic and optical barcoding of droplets based on a magnetofluidic platform. The platform comprises multiple functional areas, such as an encoding area, an encoded droplet pool and a magnetic decoding area with integrated giant magnetoresistive (GMR) sensors. To prove this concept, penicillin functionalized with fluorescent dyes is coencapsulated with magnetic nanoparticles into droplets. While fluorescent dyes are used as conventional optical barcodes which are decoded with an optical decoding setup, an additional dimensionality of barcodes is created by using magnetic nanoparticles as magnetic barcodes for individual droplets and integrated micro-patterned GMR sensors as the corresponding magnetic decoding devices. The strategy of incorporating a magnetic encoding scheme provides a dynamic range of ~40 dB in addition to that of the optical method. When combined with magnetic barcodes, the encoding capacity can be increased by more than 1 order of magnitude compared with using only optical barcodes, that is, the magnetic platform provides more than 10 unique magnetic codes in addition to each optical barcode. Besides being a unique magnetic functional element for droplet microfluidics, the platform is capable of on-demand facile magnetic encoding and real-time decoding of droplets which paves the way for the development of novel non-optical encoding schemes for highly multiplexed droplet-based biological assays.
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    The quest for research information
    (Amsterdam : Elsevier, 2014) Blümel, Ina; Dietze, Stefan; Heller, Lambert; Jäschke, Robert; Mehlberg, Martin
    Research information, i.e., data about research projects, organisations, researchers or research outputs such as publications or patents, is spread across the web, usually residing in institutional and personal web pages or in semi-open databases and information systems. While there exists a wealth of unstructured information, structured data is limited and often exposed following proprietary or less-established schemas and interfaces. Therefore, a holistic and consistent view on research information across organisational and national boundaries is not feasible. On the other hand, web crawling and information extraction techniques have matured throughout the last decade, allowing for automated approaches of harvesting, extracting and consolidating research information into a more coherent knowledge graph. In this work, we give an overview of the current state of the art in research information sharing on the web and present initial ideas towards a more holistic approach for boot-strapping research information from available web sources.
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    Web technologies for environmental Big Data
    (Amsterdam [u.a.] : Elsevier Science, 2014) Vitolo, Claudia; Elkhatib, Yehia; Reusser, Dominik; Macleod, Christopher J.A.; Buytaert, Wouter
    Recent evolutions in computing science and web technology provide the environmental community with continuously expanding resources for data collection and analysis that pose unprecedented challenges to the design of analysis methods, workflows, and interaction with data sets. In the light of the recent UK Research Council funded Environmental Virtual Observatory pilot project, this paper gives an overview of currently available implementations related to web-based technologies for processing large and heterogeneous datasets and discuss their relevance within the context of environmental data processing, simulation and prediction. We found that, the processing of the simple datasets used in the pilot proved to be relatively straightforward using a combination of R, RPy2, PyWPS and PostgreSQL. However, the use of NoSQL databases and more versatile frameworks such as OGC standard based implementations may provide a wider and more flexible set of features that particularly facilitate working with larger volumes and more heterogeneous data sources.
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    SCSlib: Transparently Accessing Protected Sensor Data in the Cloud
    (Amsterdam [u.a.] : Elsevier, 2014) Henze, Martin; Bereda, Sebastian; Hummen, René; Wehrle, Klaus
    As sensor networks get increasingly deployed in real-world scenarios such as home and industrial automation, there is a similarly growing demand in analyzing, consolidating, and storing the data collected by these networks. The dynamic, on-demand resources offered by today’s cloud computing environments promise to satisfy this demand. However, prevalent security concerns still hinder the integration of sensor networks and cloud computing. In this paper, we show how recent progress in standardization can provide the basis for protecting data from diverse sensor devices when outsourcing data processing and storage to the cloud. To this end, we present our Sensor Cloud Security Library (SCSlib) that enables cloud service developers to transparently access cryptographically protected sensor data in the cloud. SCSlib specifically allows domain specialists who are not security experts to build secure cloud services. Our evaluation proves the feasibility and applicability of SCSlib for commodity cloud computing environments.