Efficient data management in a large-scale epidemiology research project

This article describes the concept of a "Central Data Management" (CDM) and its implementation within the large-scale population-based medical research project "Personalized Medicine". The CDM can be summarized as a conjunction of data capturing, data integration, data storage, data refinement, and data transfer. A wide spectrum of reliable "Extract Transform Load" (ETL) software for automatic integration of data as well as "electronic Case Report Forms" (eCRFs) was developed, in order to integrate decentralized and heterogeneously captured data. Due to the high sensitivity of the captured data, high system resource availability, data privacy, data security and quality assurance are of utmost importance. A complex data model was developed and implemented using an Oracle database in high availability cluster mode in order to integrate different types of participant-related data. Intelligent data capturing and storage mechanisms are improving the quality of data. Data privacy is ensured by a multi-layered role/right system for access control and de-identification of identifying data. A well defined backup process prevents data loss. Over the period of one and a half year, the CDM has captured a wide variety of data in the magnitude of approximately 5terabytes without experiencing any critical incidents of system breakdown or loss of data. The aim of this article is to demonstrate one possible way of establishing a Central Data Management in large-scale medical and epidemiological studies.     

Science in Brazil. Part 1: A macro-level comparative study

Summary In the present paper, the evolution of publication activity and citation impact in Brazil is studied for the period 1991-2003. Besides the analysis of trends in publication and citation patterns and of national publication profiles, an attempt is made to find statistical evidences of the relation between international co-authorship and both research profile and citation impact in the Latin American region. Despite similarities and strong co-publication links with the other countries in the region, Brazil has nonetheless a specific research profile, and forms the largest potential in the region.     

Material and bonding characteristics for dimensioning and modelling of textile reinforced concrete (TRC) elements

The collaborative research center “Textile Reinforced Concrete (TRC) – Development of a New Technology” (SFB 532) established at Aachen University (RWTH Aachen) is investigating the basic mechanisms of this new composite material. The use of technical textiles as reinforcement material in cementitious binder systems allows the production of thin-structured elements as will be dimensioned, modelled, and produced within the research project. For this reason the material properties of the single components have to be known and will be integrated in analytical and numerical simulations of textile reinforced structures. Thus key parameters on the meso-level are introduced. These are on the one hand the tensile strength and elastic modulus of filaments and rovings, on the other hand mechanical and fracture mechanical parameters of the matrix, and finally the bonding characteristics of filaments as well as rovings embedded in the cement based matrix.     

Predicting change for individual psychotherapy clients on the basis of their nearest neighbors.

This study extended client-focused research by using the nearest neighbor (NN) approach, a client-specific sampling and prediction strategy derived from research on alpine avalanches. Psychotherapy clients (N = 203) seen in routine practice settings in the United Kingdom completed a battery of intake measures and then completed symptom intensity ratings before each session. Forecasts of each client's rate of change and session-by-session variability were computed on the basis of that client's NNs (n = 10-50 in different comparisons). Alternative forecasts used linear or log-linear slopes and were compared with an alternative prediction strategy. Results showed that the NN approach was superior to the alternative model in predicting rate of change, though the advantage was less clear for predicting variability. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Science and technology in the region: The output of regional science and technology, its strengths and its leading institutions

Summary We operationalize scientific output in a region by means of the number of articles (as in the SciSearch database) per year and technology output by means of the number of patent applications (as in the database of the European Patent Office) per priority year. All informetric analyses were done using the DIALOG online-system. The main research questions are the following: Which scientific and technological fields or topics are most influent within a region and which institutions or companies are mainly publishing articles or holding patents? Do the distributions of regional science and technology fields and of publishing institutions follow the well-known informetric function? Are there - as it is expected - only few fields and few institutions which dominate the region? Is there a connection between the economic power of a region and the regional publication and patent output? Examples studied in detail are seven German regions: Aachen, Düsseldorf, Hamburg, Köln (Cologne), Leipzig - Halle - Dessau, München (Munich), and Stuttgart. Three different indicators were used, science and technology attraction of a region (number of scientific articles and patents), science and technology intensity (articles and patents per 1,000 inhabitants), and science and technology density (articles and patents per 1 billion EURO gross value added). Top region concerning both attraction and intensity is Munich, concerning density it is Aachen.     

SUSANS With Polarized Neutrons

Super Ultra-Small Angle Neutron Scattering (SUSANS) studies over wave vector transfers of 10^–4 nm^–1 to 10^–3 nm^–1 afford information on micrometer-size agglomerates in samples. Using a right-angled magnetic air prism, we have achieved a separation of ≈10 arcsec between ≈2 arcsec wide up- and down-spin peaks of 0.54 nm neutrons. The SUSANS instrument has thus been equipped with the polarized neutron option. The samples are placed in a uniform vertical field of 8.8 × 10⁴ A/m (1.1 kOe). Several magnetic alloy ribbon samples broaden the up-spin neutron peak significantly over the ±1.3 × 10^–3 nm^–1 range, while leaving the down-spin peak essentially unaltered. Fourier transforms of these SUSANS spectra corrected for the instrument resolution, yield micrometer-range pair distribution functions for up- and down-spin neutrons as well as the nuclear and magnetic scattering length density distributions in the samples.     

Model Driven Performance Analysis of Enterprise Information Systems

This paper describes the particular motivation for performance analysis in the domain of Enterprise Information Systems (EISs) and argues that the Model Driven Architecture (MDA) is a suitable framework for integrating formal analysis techniques with engineering methods appropriate to the domain. The MDA permits natural and economical modelling of design and analysis domains and the relationships between them, supporting both manual and automatic analysis. It incorporates the Unified Modelling Language (UML), which is extensively used to capture system designs. We present our general modelling approach and outline its use in relating models of Enterprise Java Bean (EJB) applications, annotated using standard profiles, to analysable formal models.     

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km² in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m³/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.