Quantifying numerical dispersion in non-orthogonal FDTD meshes

Numerical electromagnetic models such as FDTD are widely used for the design and analysis of structures, including antennas. Numerical dispersion is one of the main sources of error that degrade the accuracy of the results-for each structure of interest, the users of the model must attempt to generate a mesh that will avoid introducing high levels of dispersion. This is, however, especially difficult for non-orthogonal meshes since little information is available on the dispersion properties of the non-orthogonal FDTD algorithm on complex meshes. For the first time, the dispersion in realistic non-orthogonal FDTD models of microstrip structures is quantified directly through numerical simulations. A test structure is considered, discretised using a number of nonorthogonal mesh configurations, including single and multiple skew angles. A numerical analysis of reflections generated at the transition between two mesh regions with different skew angles is also presented. These results give a practical guide to mesh generation for users of the algorith

Modelling metallic discontinuities with the non-orthogonal finite difference time domain method

Numerical electromagnetic models, such as the finite difference time domain (FDTD) method, have many applications. The authors focus on the non-orthogonal FDTD method, which offers an improved geometric flexibility compared to other standard techniques. Results from numerical electromagnetic analysis methods, such as the FDTD method, are often degraded by an error known as numerical dispersion. For metallic structures this dispersion error is often higher than expected from theoretical considerations. The source of this additional error is due to the reciprocal field interpolation scheme used in the non-orthogonal FDTD algorithm. The error is illustrated by means of a microstrip waveguide and a microstrip antenna. Techniques for reducing this error are evaluated; careful construction of the mesh at the metallic boundary being the most reliable solution

Microwave detection of buried mines using non-contact, synthetic near-field focusing

Existing ground penetrating radars (GPR) are limited in their 3-D resolution. For the detection of buried land-mines, their performance is also seriously restricted by `clutter'. Previous work by the authors has concentrated on removing these limitations by employing multi-static synthetic focusing from a 2-D real aperture. This contribution presents this novel concept, describes the proposed implementation, examines the influence of clutter and of various ground features on the system's performance, and discusses such practicalities as digitisation and time-sharing of a single transmitter and receiver. Experimental results from a variety of scenarios are presented

Identification of common genetic risk variants for autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.Peer reviewe

Genome-wide by Environment Interaction Studies of Depressive Symptoms and Psychosocial Stress in UK Biobank and Generation Scotland

Stress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population-based cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank (DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77 x 10(-6)). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145 (53-kb downstream PIWIL4; p = 4.95 x 10(-9); total SLE) and rs17070072 (intronic to ZCCHC2; p = 1.46 x 10(-8); dependent SLE). A third locus upstream CYLC2 (rs12000047 and rs12005200, p < 2.00 x 10(-8); dependent SLE) when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77 x 10(-6)). Polygenic risk scores (PRSs) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91 x 10(-3)). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions

Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike’s information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk