The Population Genetic Signature of Polygenic Local Adaptation

Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We first describe a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of $Q_{ST}/F_{ST}$ comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.Comment: 42 pages including 8 figures and 3 tables; supplementary figures and tables not included on this upload, but are mostly unchanged from v

Spatial Accuracy of Climate Networks in Nebraska

Climate data has become increasingly scrutinized for its accuracy because of the need for reliable predictions about climate change. The U.S. has taken great strides to keep up with the demand for accurate climate data. Over the last thirty years, vast improvements to instrumentation, data collection, and station siting have created more accurate data records. This study is to explore the accuracy of existing networks. This study analyzes three climate networks used in Nebraska: the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the newest network, the U.S. Climate Reference Network (CRN). Each of these networks has its own instrumentation, collection methods and station sites. Maximum and minimum surface temperature from the three networks and the spatial structure of temperature variations at the surface are compared. Two different timeframes, 2005-2009 and 1985-2005, were used to include the newest network, CRN, in the analysis. Daily data were collected from each of these networks within the specified timeframe. Root mean square error (RMSE) between each candidate station and the surrounding stations within 500 kilometers were calculated and evaluated to determine spatial accuracy of the network. This study found that in the 5 year analysis, CRN versus AWDN, the two networks were not significantly different enough to denote the network with high spatial accuracy. For the 21 year analysis, HCN versus AWDN, AWDN stations showed higher spatial accuracy (smaller error) than HCN stations for the variable of maximum temperature. The error for the two networks were not significantly different enough to decipher the network with the higher spatial accuracy. Advisors: Kenneth G. Hubbard & David B. Mar

Quantitative Description of Grain Contacts in a Locked Sand

Quantifying the fabric of intact soil is of great importance in both geomechanics and geology. A unique and interesting example of fabric can be found in “locked sands”. These geologically old sands are characterized by significant grain interlocking and a low cement content. They can be sampled with minimal fabric disturbance. This study analyzes images acquired by x-ray microtomography of resin impregnated samples of a natural sand, Reigate Silver Sand part of the Folkestone Bed formation from southeast England. 2D and 3D image analyses were carried out to identify the grain-grain contacts and quantify individual contact areas. In contrast to earlier studies that have focused on the coordination number, this work demonstrates that for non-punctual contacts a measure of fabric that considers the contact area may be more appropriate

A miniature triaxial apparatus for investigating the micromechanics of granular soils with in situ X-ray micro-tomography scanning

The development of a miniature triaxial apparatus is presented. In conjunction with an X-ray microtomography (termed as X-ray μCT hereafter) facility and advanced image processing techniques, this apparatus can be used for in situ investigation of the micro-scale mechanical behavior of granular soils under shear. The apparatus allows for triaxial testing of a miniature dry sample with a size of 8 mm × 16 mm (diameter × height). In situ triaxial testing of a 0.4–0.8 mm Leighton Buzzard sand (LBS) under a constant confining pressure of 500 kPa is presented. The evolutions of local porosities (i.e., the porosities of regions associated with individual particles), particle kinematics (i.e., particle translation and particle rotation) of the sample during the shear are quantitatively studied using image processing and analysis techniques. Meanwhile, a novel method is presented to quantify the volumetric strain distribution of the sample based on the results of local porosities and particle tracking. It is found that the sample, with nearly homogenous initial local porosities, starts to exhibit obvious inhomogeneity of local porosities and localization of particle kinematics and volumetric strain around the peak of deviatoric stress. In the post-peak shear stage, large local porosities and volumetric dilation mainly occur in a localized band. The developed triaxial apparatus, in its combined use of X-ray μCT imaging techniques, is a powerful tool to investigate the micro-scale mechanical behavior of granular soils

Micromechanical Behaviour in Shearing of Reproduced Flat LBS Grains with Strong and Weak Artificial Bonds

The shearing behaviour of reproduced flat LBS grains artificially bonded with ordinary Portland cement (OPC) and plaster of Paris (PP) was examined using micromechanical experiments. Monotonic shearing tests showed a distinct variation in the load–displacement relationship at low, medium and high normal loads, and a nonlinear shear strength envelope was proposed. For OPC-bonded sand grains, a brittle–ductile transition at 20–30 N normal load was observed and three breakage mechanisms in shearing (chipping, shear cracks and crushing) were distinguished in accordance with the changes in the load–displacement curves. OPC-bonded sands showed a predominant dilation at lower normal loads, whereas PP-bonded sands were highly compressive. Based on previously published works using element-scale tests, a new mechanism for dilation under micromechanical testing was proposed in the study. Cyclic shearing tests were conducted on OPC-bonded sands, and the effects of increased displacement amplitude and normal load were highlighted

Transitional Behaviors in Well-Graded Coarse Granular Soils

Drained triaxial compression tests were carried out for a well-graded coarse granular soil (CGS) to investigate the effect of the initial specific volume on the location of the critical state line (CSL). A family of parallel CSLs in the v∼logp′ plane was observed for the well-graded CGS, indicating that it exhibited transitional behavior. The degree of transitional behavior was quantified from the relationship between the intercepts of the CSLs and the initial specific volumes, giving a value of 0.59, which indicated a substantially transitional behavior. The observations of the CSL pattern in the CGS illustrated that transitional behavior could be extended to large-sized granular soils, beyond the usual transitional soils that have been observed so far, which generally have gradings between those of clean sand and plastic clay

Experimental investigation into the primary fabric of stress transmitting particles

Understanding the stress distribution amongst the constituent grains is fundamental to predict the response of soil and advance science-based, rather than purely empirical, constitutive models. Photoelastic experiments and discrete element method simulations have provided evidence that, upon loading, discrete force chains form in granular materials. These force chains are made up of particles transmitting relatively large stresses and they are aligned in the direction of the major principal stress. A few qualitative studies have identified the presence of these force chains in sands but direct measurements of force chains have not been previously documented and tracking stress transmission in assemblies of real soil grains remains a challenging task. The present study makes use of three dimensional micro CT images to investigate the evolution of the internal topology of a sand subjected to triaxial compression loading. The analysis of the contact normal and branch vector orientations has shown the realignment of the contact normals in the direction of the major principal stress as a clear indication of the formation of force chains in the post-peak regime. Here the extent of the non-colinearity of the branch and contact normal vectors is explored. Using the micro CT data contact force networks within and outside of shear bands are compared

Image segmentation techniques for granular materials

To improve understanding of the mechanical behavior of granular materials it is important to be able to quantify the relative arrangement of the grains, i.e. the fabric. This can be done, for example, by measuring the orientations of the particles (e.g. the long axis orientation) or by considering the orientations of the vectors normal to each grain‐grain contact. In two dimensional (2D) analyses this information can be obtained by digital image analysis of images of thin sections obtained from an optical microscope. While such data is useful, granular materials of engineering interest are three dimensional (3D) materials and quantification of the 3D fabric is necessary. Micro Computed‐Tomography (μCT) together with 3D image analysis has emerged as a promising technique for obtaining the 3D data required. This paper aims to highlight the challenges associated with using image analysis to provide quantitative information on fabric. While automated image segmentation has proved to produce reasonable results in some cases, it is sometimes less successful when dealing with highly irregular and angular soil grains. This paper evaluates the effectiveness of 2D and 3D segmentation techniques that rely on the watershed segmentation algorithm. The primary material considered is Reigate Silver Sand, a natural quartzitic sand with grain diameters in the range of 150–300 μm. While the sand considered is primarily of interest to geotechnical engineers, the results of this study will be of interest to anyone seeking to quantify granular material fabric using either 2D microscopy data or μCT 3D data sets

RPCs Considered Harmful

Scholars agree that amphibious configurations are an interesting new topic in the field of cryp- toanalysis, and mathematicians concur. After years of structured research into gigabit switches, we argue the improvement of forward-error correction, which embodies the private principles of cryptography. In our research, we use authenticated modalities to demonstrate that digital-to-analog converters and evolutionary programming can collude to address this question. Such a hypothesis at first glance seems unexpected but is derived from known results

Spatial Accuracy of Climate Networks: A Case Study in Nebraska

Climate data are increasingly scrutinized for accuracy because of the need for reliable input for climaterelated decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005–09 for CRN and AWDN and 1985–2005 for AWDN and HCN. Individual root-mean-square errors between candidate station and surrounding stations were calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station