A novel prestack sparse azimuthal AVO inversion

In this paper we demonstrate a new algorithm for sparse prestack azimuthal AVO inversion. A novel Euclidean prior model is developed to at once respect sparseness in the layered earth and smoothness in the model of reflectivity. Recognizing that methods of artificial intelligence and Bayesian computation are finding an every increasing role in augmenting the process of interpretation and analysis of geophysical data, we derive a generalized matrix-variate model of reflectivity in terms of orthogonal basis functions, subject to sparse constraints. This supports a direct application of machine learning methods, in a way that can be mapped back onto the physical principles known to govern reflection seismology. As a demonstration we present an application of these methods to the Marcellus shale. Attributes extracted using the azimuthal inversion are clustered using an unsupervised learning algorithm. Interpretation of the clusters is performed in the context of the Ruger model of azimuthal AVO

Nucleotide diversity in each of the 20 herring populations

Nucleotide diversity in each populations was obtained using popoolation, and parameters used could be found in the readme.txt

Genetic and geographic distance metrices used for IBD

Genetic and geographic distance metrices used for IB

outlier detection

Outlier detection with both empirical and BayeScan tests in both all of the 20 and just 17 Baltic Sea herring populations

Genetic differentiation associated with environmental parameters

Detecting association between Genetic differentiation and environmental parameters with Bayenv

R script and code for cluster-based association and QTL mapping

R script and code for cluster-based association and QTL mappin

Data_Sheet_1_Inflammatory bowel disease is causally related to irritable bowel syndrome: a bidirectional two-sample Mendelian randomization study.docx

IntroductionInflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) are lifelong digestive diseases that severely impact patients’ quality of life. The existence of a causal association between IBS and IBD remains unclear. This study aimed to determine the direction of causality between IBD and IBS by quantifying their genome-wide genetic associations and performing bidirectional two-sample Mendelian randomization (MR) analyses.MethodsGenome-wide association studies (GWAS) among a predominantly European patient cohort identified independent genetic variants associated with IBS and IBD. Two separate databases (a large GWAS meta-analysis and the FinnGen cohort) for both IBS and IBD were consulted to retrieve statistics on instrument-outcome associations. MR analyses included inverse-variance-weighted, weighted-median, MR-Egger regression, MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) methods, and sensitivity analyses were performed. The MR analyses were carried out for each outcome data, followed by a fixed-effect meta-analysis.ResultsGenetically predicted IBD was associated with an increased risk of IBS. Odds ratios (95% confidence intervals) for samples of 211,551 (17,302 individuals with IBD), 192,789 (7,476 Crohn’s disease cases), and 201,143 (10,293 ulcerative colitis cases) individuals were 1.20 (1.00, 1.04), 1.02 (1.01, 1.03), and 1.01 (0.99, 1.03), respectively. After outlier correction using MR-PRESSO, the odds ratio for ulcerative colitis was 1.03 (1.02, 1.05) (p = 0.001). However, an association between genetically influenced IBS and IBD was not identified.DiscussionThis study confirms that IBD is causally related to IBS, which may interfere with the diagnosis and treatment of both diseases.</p

Table_1_Inflammatory bowel disease is causally related to irritable bowel syndrome: a bidirectional two-sample Mendelian randomization study.xlsx

IntroductionInflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) are lifelong digestive diseases that severely impact patients’ quality of life. The existence of a causal association between IBS and IBD remains unclear. This study aimed to determine the direction of causality between IBD and IBS by quantifying their genome-wide genetic associations and performing bidirectional two-sample Mendelian randomization (MR) analyses.MethodsGenome-wide association studies (GWAS) among a predominantly European patient cohort identified independent genetic variants associated with IBS and IBD. Two separate databases (a large GWAS meta-analysis and the FinnGen cohort) for both IBS and IBD were consulted to retrieve statistics on instrument-outcome associations. MR analyses included inverse-variance-weighted, weighted-median, MR-Egger regression, MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) methods, and sensitivity analyses were performed. The MR analyses were carried out for each outcome data, followed by a fixed-effect meta-analysis.ResultsGenetically predicted IBD was associated with an increased risk of IBS. Odds ratios (95% confidence intervals) for samples of 211,551 (17,302 individuals with IBD), 192,789 (7,476 Crohn’s disease cases), and 201,143 (10,293 ulcerative colitis cases) individuals were 1.20 (1.00, 1.04), 1.02 (1.01, 1.03), and 1.01 (0.99, 1.03), respectively. After outlier correction using MR-PRESSO, the odds ratio for ulcerative colitis was 1.03 (1.02, 1.05) (p = 0.001). However, an association between genetically influenced IBS and IBD was not identified.DiscussionThis study confirms that IBD is causally related to IBS, which may interfere with the diagnosis and treatment of both diseases.</p

Spatiotemporal Transmission Model to Simulate an Interregional Epidemic Spreading

Infectious disease spread is a spatiotemporal process with significant regional differences that can be affected by multiple factors, such as human mobility and manner of contact. From a geographical perspective, the simulation and analysis of an epidemic can promote an understanding of the contagion mechanism and lead to an accurate prediction of its future trends. The existing methods fail to consider the mutual feedback mechanism of heterogeneities between the interregional population interaction and the regional transmission conditions (e.g., contact probability and the effective reproduction number). This disadvantage oversimplifies the transmission process and reduces the accuracy of the simulation results. To fill this gap, a general model considering the spatiotemporal characteristics is proposed, which includes compartment modeling of population categories, flow interaction modeling of population movements, and spatial spread modeling of an infectious disease. Furthermore, the correctness of a theoretical hypothesis for modeling and prediction accuracy of this model was tested with a synthetic data set and a real-world COVID-19 data set in China, respectively. The theoretical contribution of this article was to verify that the interplay of multiple types of geospatial heterogeneities dramatically influences the spatial spread of infectious disease. This model provides an effective method for solving infectious disease simulation problems involving dynamic, complex spatiotemporal processes of geographical elements, such as optimization of lockdown strategies, analyses of the medical resource carrying capacity, and risk assessment of herd immunity from the perspective of geography. Key Words: geospatial heterogeneities, health geography, interregional population interaction, spatiotemporal analysis, transmission modeling.</p

Data for QTL mapping on brain size in sticklebacks

The data set is used in a quantitative trait locus mapping study on six brain volume traits including bulbus olfactorious, telecephalon, optic tectum, hypothalamus, cerebellum and total brain size of a F2 nine stickleback population. The data consists of 239 individuals, and 15198 non-identical SNPs. A linkage map has been constructed, and divide the SNPs into 21 linkage groups. The data are distributed as following: File1: brain_phenotype.txt -The phenotype data of six brain traits, which have been corrected by the sex and bodysize effects. File2: genotype.txt -The SNP data, each row represents the individuals which is one-to-one match to the phenotype data file, and each column represents the SNPs which is one-to-one match to the linkage map file. The missing genotype data have been imputed, and coded as 1,0,-1 for the genotypes AA, AB and BB, respectively. File3: linkage_map.csv -The linkage map information, column1 is the marker ID, column2 is the linkage group info, and column3 is the linkage position of each SN