Abstract

xtabstractProlonged prednisolone treatment for the initial episode of childhood nephrotic syndrome may reduce relapse rate, butwhether this results fromthe increased duration of treatment or a higher cumulative dose remains unclear.We conducted a randomized, double-blind, placebo-controlled trial in 69 hospitals in The Netherlands. We randomly assigned 150 children (9 months to 17 years) presenting with nephrotic syndrome to either 3 months of prednisolone followed by 3 months of placebo ( n=74) or 6 months of prednisolone (n=76), and median follow-up was 47 months. Both groups received equal cumulative doses of prednisolone (approximately 3360 mg/m2). Among the 126 children who started trial medication, relapses occurred in 48 (77%) of 62 patients who received 3 months of prednisolone and 51 (80%) of 64 patients who received 6 months of prednisolone. Frequent relapses, according to international criteria, occurred with similar frequency between groups as well (45% versus 50%). In addition, there were no statistically significant differences between groups with respect to the eventual initiation of prednisolone maintenance and/or other immunosuppressive therapy (50% versus 59%), steroid dependence, or adverse effects. In conclusion, in this trial, extending initial prednisolone treatment from 3 to 6 months without increasing cumulative dose did not bene fit clinical outcome in children with nephrotic syndrome. Previous findings indicating that prolonged treatment regimens reduce relapses most likely resulted from increased cumulative dose rather than the treatment duration. Copyrigh

Abstract

Comparisons of measured and model-predicted atmospheric copper concentrations show a severe underestimation of the observed concentrations by the models. This underestimation may be (partly) due to underestimated emissions of copper to air. Since the phase out of asbestos brake lining material, the composition of brake lining material has changed and may contain up to ∼15% copper. This makes brake wear from vehicles potentially an important source of atmospheric (particulate) copper concentrations. In this paper, we reassess the copper emissions due to exhaust emissions and brake wear from road transport. Overall, our reassessments result in an estimate of total copper emission to air in UNECE-Europe of 4.0-5.5 ktonnes yr-1, which is substantially higher than the previous estimate of 2.8 ktonnes yr-1. Copper concentrations over Europe are calculated with the LOTOS-EUROS model using the revised emission data as model input. The results show that the revised emission estimates are a major step towards gap closure of predicted versus observed copper concentrations in ambient air. Brake wear emissions may be responsible for 50-75% of the total copper emissions to air for most of Western Europe. The hypothesis that road transport is an important source of copper emissions is tested and confirmed by (1) reviewing available literature data of chemically speciated PM data from road tunnel studies and (2) the gradient observed in copper concentrations from ambient PM monitoring going from rural sites to street stations. The literature review and observational data suggest that the majority of the emitted PM10 brake wear particles is in the PM2.5-10 size range. The results of this study indicate that modification of brake lining composition is an important mitigation option to reduce copper exposure of the population in Western Europe. © 2007 Elsevier Ltd. All rights reserved.

Abstract

The recent innovation of collecting X-ray scattering from solutions containing purified macromolecules in high-throughput has yet to be truly exploited by the biological community. Yet, this capability is becoming critical given that the growth of sequence and genomics data is significantly outpacing structural biology results. Given the huge mismatch in information growth rates between sequence and structural methods, their combined high-throughput and high success rate make high-throughput small angle X-ray scattering (HT-SAXS) analyses increasingly valuable. HT-SAXS connects sequence as well as NMR and crystallographic results to biological outcomes by defining the flexible and dynamic complexes controlling cell biology. Commonly falling under the umbrella of bio-SAXS, HT-SAXS data collection pipelines have or are being developed at most synchrotrons. How investigators practically get their biomolecules of interest into these pipelines, balance sample requirements and manage HT-SAXS data output format varies from facility to facility. While these features are unlikely to be standardized across synchrotron beamlines, a detailed description of HT-SAXS issues for one pipeline provides investigators with a practical guide to the general procedures they will encounter. One of the longest running and generally accessible HT-SAXS endstations is the SIBYLS beamline at the Advanced Light Source in Berkeley CA. Here we describe the current state of the SIBYLS HT-SAXS pipeline, what is necessary for investigators to integrate into it, the output format and a summary of results from 2 years of operation. Assessment of accumulated data informs issues of concentration, background, buffers, sample handling, sample shipping, homogeneity requirements, error sources, aggregation, radiation sensitivity, interpretation, and flags for concern. By quantitatively examining success and failures as a function of sample and data characteristics, we define practical concerns, considerations, and concepts for optimally applying HT-SAXS techniques to biological samples.

Abstract

Digital enterprises and their networks increasingly rely on advanced decision-making capabilities, however, development of decision-making models requires significant effort and is often performed independently of other digitalization activities. Additionally, dynamic nature of many decision-making problems requires rapid ramp-up of decision-making capabilities. To addresses these challenges, this position paper proposes to elaborate a method for integrated on-demand decision modeling. The method combines mathematical programming and data analytics models to create case specific models on the basis of generic decision-making models. The integrated model and its data supply pipelines are configured using enterprise models allowing for consistent and rapid model deployment. The integrated model is intended for the trusted ICT supply chain configuration problem though it can be used for solving various types of decision-making problems. The main expected results are formulation of the new type decision-making model and the method for on-demand configuration of such models.

Abstract

There are many factors that affect urban traffic flow. In the case of severe traffic congestion, the vehicle speed is very slow, which results in the GPS positioning system’s estimation of the vehicle speed being very inaccurate, which in turn leads to poor reliability of the estimated congestion time of the road segment. The main contents of this study are: in the case of urban traffic congestion, the prediction and analysis of the degree of traffic congestion and the length of congestion. Taking the dynamic traffic data of Shenzhen on June 9, 2014 as an example, the road section of Binhe Avenue is selected, and the data of traffic flow, average speed of traffic volume and traffic volume density in the current time period are calculated after data preprocessing, as a measure of traffic. The main impact indicators of congestion status. Then we use the fuzzy comprehensive evaluation method to divide TSI as a traffic congestion evaluation index and divide the road congestion into four levels. In this way, we can get the congestion of the road in each time period of the day and the time required to pass. Then we use the random forest, adaboost, GBDT, Lasso CV and BP neural networks in the machine learning algorithm to build models to measure traffic congestion for training and testing. Finally, the BP neural network has the best effect on this problem, and mean square error is 0.0190. Finally, we used BP neural network to predict and congest the road in the next three hours. From the experimental simulation results, this method can effectively analyze and predict the real-time traffic congestion.

Abstract

Rail transport is an environmentally responsible approach and traffic is expected to increase in the coming decades. Little is known about the implications for quality of life of populations living close to railways. This study explores the way in which vibrations from rail are perceived and described by these populations. The study took place in the Vastra Gotaland and Varmland regions of Sweden. A qualitative study approach was undertaken using semi-structured interviews within a framework of predetermined questions in participants’ homes. A 26.3% response rate was achieved and 17 participants were interviewed. The experience of vibrations was described in tangible terms through different senses. Important emerging themes included habituation to and acceptance of vibrations, worry about property damage, worry about family members and general safety. Participants did not reflect on health effects, however, chronic exposure to vibrations through multimodal senses in individual living environments may reduce the possibility for restoration in the home. Lack of empowerment to reduce exposure to vibrations was important. This may alter individual coping strategies, as taking actions to avoid the stressor is not possible. The adoption of other strategies, such as avoidance, may negatively affect an individual’s ability to cope with the stressor and their health. Document type: Article

Abstract

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts 1) archival and dissemination services for raw and curated data, together with their provenance graph, 2) modelling services and virtual machines, 3) tools for data analytics, and pre-/post-processing, and 4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of the FAIR principles that extends to computational workflows. Materials Cloud leverages the AiiDA framework to record the provenance of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow to retrace and reproduce any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain. Comment: 19 pages, 8 figure Document type: Article

Abstract

Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets. Document type: Article

Abstract

Large scale image processing demands a standardized way of not only storage but also a method for job distribution and scheduling. The eXtensible Neuroimaging Archive Toolkit (XNAT) is one of several platforms that seeks to solve the storage issues. Distributed Automation for XNAT (DAX) is a job control and distribution manager. Recent massive data projects have revealed several bottlenecks for projects with "100,000 assessors (i.e., data processing pipelines in XNAT). In order to address these concerns, we have developed a new API, which exposes a direct connection to the database rather than REST API calls to accomplish the generation of assessors. This method, consistent with XNAT, keeps a full history for auditing purposes. Additionally, we have optimized DAX to keep track of processing status on disk (called DISKQ) rather than on XNAT, which greatly reduces load on XNAT by vastly dropping the number of API calls. Finally, we have integrated DAX into a Docker container with the idea of using it as a Docker controller to launch Docker containers of image processing pipelines. Using our new API, we reduced the time to create 1,000 assessors (a sub-cohort of our case project) from 65040 seconds to 229 seconds (a decrease of over 270 fold). DISKQ, using pyXnat, allows launching of 400 jobs in under 10 seconds which previously took 2,000 seconds. Together these updates position DAX to support projects with hundreds of thousands of scans and to run them in a time-efficient manner.

Abstract

High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on Global Navigation Satellite System (GNSS), which, as stand-alone positioning, does not meet the necessary requirements in terms of accuracy. Furthermore, the rise of connected driving offers various possibilities to enhance GNSS positioning by applying cooperative positioning (CP) methods. Utilizing only low-cost sensors, especially in urban environments, GNSS CP faces several demanding challenges. Therefore, this contribution presents an empirical study on how Vehicle-to-Everything (V2X) technologies can aid GNSS position estimation in urban environments, with the focus being solely on positioning performance instead of multi-sensor data fusion. The performance of CP utilizing common positioning approaches as well as CP integration in state-of-the-art Vehicular Ad-hoc Networks (VANET) is displayed and discussed. Additionally, a measurement campaign, providing a representational foundation for validating multiple CP methods using only consumer level and low-cost GNSS receivers, as well as commercially available IEEE 802.11p V2X communication modules in a typical urban environment is presented. Evaluating the algorithm&rsquo s performance, it is shown that CP approaches are less accurate compared to single positioning in the given environment. In order to investigate error influences, a skyview modelling seeking to identify non-line-of-sight (NLoS) effects using a 3D building model was performed. We found the position estimates to be less accurate in areas which are affected by NLoS effects such as multipath reception. Due to covariance propagation, the accuracy of CP approaches is decreased, calling for strategies for multipath detection and mitigation. In summary, this contribution will provide insights on integration, implementation strategies and accuracy performances, as well as drawbacks for local area, low-cost GNSS CP in urban environments. Document type: Article