The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Diabetes mellitus: pathophysiological changes and therap

Building Footprint Simplification Techniques and Their Effects on Radio Propagation Predictions

Building footprint simplification is of critical importance to radio propagation predictions in wireless communication systems as the prediction time is closely related to the number of both buildings and vertices involved. Intuitively, if the complexity of footprints (i.e. the number of vertices in the footprints) is reduced, predictions can be generated more quickly. However, such reductions often affect the accuracy of results as the simplification error constrains the efficiency that can be achieved. To achieve a good vertex reduction rate for the footprints involved and at the same time preserve the shapes of footprints in terms of their areas, orientations and centroids, we propose a number of efficient single-pass methods to simplify building footprints. To satisfy constraints on edges, areas and centroids of simplified footprints, multi-pass methods are suggested. Hybrid methods take advantage of complementary properties exhibited by different footprint simplification methods. We assess the baseline effectiveness of our proposed techniques, and carry out an extensive comparative evaluation with real geographic information system data from different municipalities. Through experimentation, we find that hybrid methods deliver the best performance in both vertex reduction rate and simplification error. We examine the effects that these footprint simplification methods have on the ray-tracing based radio propagation prediction systems in terms of processing time and prediction accuracy. Our experiments show that footprint simplification methods indeed reduce prediction time up to three-fold, and maintain prediction accuracy with high confidence as well. We also investigate the relationship between footprint simplification error and the prediction accuracy. We find that the prediction accuracy is sensitive to the distortion (i.e. change of shape) of building footprints. This helps us to better understand the trade-off between precision of the building database and the accuracy of predictions generated by ray-tracing based radio propagation prediction systems

Radio-wave propagation prediction using ray-tracing techniques on a network of workstations (NOW)

Ray-tracing based radio wave propagation prediction models play an important role in the design of contemporary wireless networks as they may now take into account diverse physical phenomena including reflections, diffractions, and diffuse scattering. However, such models are computationally expensive even for moderately complex geographic environments. In this paper, we propose a computational framework that functions on a network of workstations (NOW) and helps speed up the lengthy prediction process. In ray-tracing based radio propagation prediction models, orders of diffractions are usually processed in a stage-by-stage fashion. In addition, various source points (transmitters, diffraction corners, or diffuse scattering points) and different ray-paths require different processing times. To address these widely varying needs, we propose a combination of the phase-parallel and manager/workers paradigms as the underpinning framework. The phase-parallel component is used to coordinate different computation stages, while the manager/workers paradigm is used to balance workloads among nodes within each stage. The original computation is partitioned into multiple small tasks based on either raypath-level or source-point-level granularity. Dynamic load-balancing scheduling schemes are employed to allocate the resulting tasks to the workers.We also address issues regarding main memory consumption, intermediate data assembly, and final prediction generation. We implement our proposed computational model on a NOW configuration by using the message passing interface (MPI) standard. Our experiments with real and synthetic building and terrain databases show that, when no constraint is imposed on the main memory consumption, the proposed prediction model performs very well and achieves nearly linear speedups under various workload. When main memory consumption is a concern, our model still delivers very promising performance rates provided that the complexity of the involved computation is high, so that the extra computation and communication overhead introduced by the proposed model do not dominate the original computation. The accuracy of prediction results and the achievable speedup rates can be significantly improved when 3D building and terrain databases are used and/or diffuse scattering effect is taken into account. © 2004 Elsevier Inc. All rights reserved

Efficient RF Coverage Prediction through a fully Discrete, GPU-Parallelized Ray-Launching model

A fully discrete Ray Launching field prediction algorithm that takes advantage of environment visibility preprocessing for both diffuse and specular interactions is presented and used to perform efficient RF coverage prediction in large environments. The algorithm, being discrete, has been parallelized in a straightforward way on NVIDIA-compatible Graphical Processing Units. These innovative features combined allow to achieve a computation time reduction of about three orders of magnitude compared to conventional algorithms, while retaining the same accuracy level