Magnetoresistance of a 2-dimensional electron gas in a random magnetic field

We report magnetoresistance measurements on a two-dimensional electron gas (2DEG) made from a high mobility GaAs/AlGaAs heterostructure, where the externally applied magnetic field was expelled from regions of the semiconductor by means of superconducting lead grains randomly distributed on the surface of the sample. A theoretical explanation in excellent agreement with the experiment is given within the framework of the semiclassical Boltzmann equation.Comment: REVTEX 3.0, 11 pages, 3 Postscript figures appended. The manuscript can also be obtained from our World Wide Web server: http://roemer.fys.ku.dk/randmag.ht

Association of genetic variants previously implicated in coronary artery disease with age at onset of coronary artery disease requiring revascularizations

BACKGROUND:The relation between burden of risk factors, familial coronary artery disease (CAD), and known genetic variants underlying CAD and low-density lipoprotein cholesterol (LDL-C) levels is not well-explored in clinical samples. We aimed to investigate the association of these measures with age at onset of CAD requiring revascularizations in a clinical sample of patients undergoing first-time coronary angiography. METHODS:1599 individuals (mean age 64 years [min-max 29-96 years], 28% women) were genotyped (from blood drawn as part of usual clinical care) in the Copenhagen area (2010-2014). The burden of common genetic variants was measured as aggregated genetic risk scores (GRS) of single nucleotide polymorphisms (SNPs) discovered in genome-wide association studies. RESULTS:Self-reported familial CAD (prevalent in 41% of the sample) was associated with -3.2 years (95% confidence interval -4.5, -2.2, p<0.0001) earlier need of revascularization in sex-adjusted models. Patients with and without familial CAD had similar mean values of CAD-GRS (unweighted scores 68.4 vs. 68.0, p = 0.10, weighted scores 67.7 vs. 67.5, p = 0.49) and LDL-C-GRS (unweighted scores 58.5 vs. 58.3, p = 0.34, weighted scores 63.3 vs. 61.1, p = 0.41). The correlation between the CAD-GRS and LDL-C-GRS was low (r = 0.14, p<0.001). In multivariable adjusted regression models, each 1 standard deviation higher values of LDL-C-GRS and CAD-GRS were associated with -0.70 years (95% confidence interval -1.25, -0.14, p = 0.014) and -0.51 years (-1.07, 0.04, p = 0.07) earlier need for revascularization, respectively. CONCLUSIONS:Young individuals presenting with CAD requiring surgical interventions had a higher genetic burden of SNPs relating to LDL-C and CAD (although the latter was statistically non-significant), compared with older individuals. However, the absolute difference was modest, suggesting that genetic screening can currently not be used as an effective prediction tool of when in life a person will develop CAD. Whether undiscovered genetic variants can still explain a "missing heritability" in early-onset CAD warrants more research

Atomic force microscopy analysis of nanoparticles in non-ideal conditions

Nanoparticles are often measured using atomic force microscopy or other scanning probe microscopy methods. For isolated nanoparticles on flat substrates, this is a relatively easy task. However, in real situations, we often need to analyze nanoparticles on rough substrates or nanoparticles that are not isolated. In this article, we present a simple model for realistic simulations of nanoparticle deposition and we employ this model for modeling nanoparticles on rough substrates. Different modeling conditions (coverage, relaxation after deposition) and convolution with different tip shapes are used to obtain a wide spectrum of virtual AFM nanoparticle images similar to those known from practice. Statistical parameters of nanoparticles are then analyzed using different data processing algorithms in order to show their systematic errors and to estimate uncertainties for atomic force microscopy analysis of nanoparticles under non-ideal conditions. It is shown that the elimination of user influence on the data processing algorithm is a key step for obtaining accurate results while analyzing nanoparticles measured in non-ideal conditions

Generalized Canonical Regression

This paper introduces a generalized approach to canonical regression, in which a set of jointly dependent variables enters the left-hand side of the equation as a linear combination, formally like the linear combination of regressors in the right-hand side of the equation. Natural applications occur when the dependent variable is the sum of components that may optimally receive unequal weights or in time series models in which the appropriate timing of the dependent variable is not known a priori. The paper derives a quasi-maximum likelihood estimator as well as its asymptotic distribution and provides illustrative applications

Sequencing and de novo assembly of 150 genomes from Denmark as a population reference

Hundreds of thousands of human genomes are now being sequenced to characterize genetic variation and use this information to augment association mapping studies of complex disorders and other phenotypic traits. Genetic variation is identified mainly by mapping short reads to the reference genome or by performing local assembly. However, these approaches are biased against discovery of structural variants and variation in the more complex parts of the genome. Hence, large-scale de novo assembly is needed. Here we show that it is possible to construct excellent de novo assemblies from high-coverage sequencing with mate-pair libraries extending up to 20 kilobases. We report de novo assemblies of 150 individuals (50 trios) from the GenomeDenmark project. The quality of these assemblies is similar to those obtained using the more expensive long-read technology. We use the assemblies to identify a rich set of structural variants including many novel insertions and demonstrate how this variant catalogue enables further deciphering of known association mapping signals. We leverage the assemblies to provide 100 completely resolved major histocompatibility complex haplotypes and to resolve major parts of the Y chromosome. Our study provides a regional reference genome that we expect will improve the power of future association mapping studies and hence pave the way for precision medicine initiatives, which now are being launched in many countries including Denmark

Combining Geoprocessing and Interregional Input-Output Systems: An Application to the State of São Paulo in Brazil

This work develops a method for the construction of input-output systems capable of estimating the flows of goods and services among cities, having in view that the creation of accurate strategies depends on the regional peculiarities incorporated in the scope of the economic planning researches. The study innovates by combining geoprocessing with inputoutput theory elements, facilitating the interpretation of the information available on the extensive data set of interregional input-output systems. The analytical potential is showed through a panoramic evaluation of the São Paulo State supply and demand relations, and by the application of the estimated input-output system to a study of the regional impacts of the “Bolsa Familia” Program, an income transfer program from the Federal government. The results show that this program must be understood not only as a form of income transference, but also as a catalytic agent for decreasing the regional inequality inside the state