Admixture mapping based on recently admixed populations is a powerful method to detect disease variants with substantial allele frequency differences in ancestral populations. We performed admixture mapping analysis for systolic blood pressure (SBP) and diastolic blood pressure (DBP), followed by trait-marker association analysis, in 6303 unrelated African-American participants of the Candidate Gene Association Resource (CARe) consortium. We identified five genomic regions (P< 0.001) harboring genetic variants contributing to inter-individual BP variation. In follow-up association analyses, correcting for all tests performed in this study, three loci were significantly associated with SBP and one significantly associated with DBP (P< 10(-5)). Further analyses suggested that six independent single-nucleotide polymorphisms (SNPs) contributed to the phenotypic variation observed in the admixture mapping analysis. These six SNPs were examined for replication in multiple, large, independent studies of African-Americans [Women's Health Initiative (WHI), Maywood, Genetic Epidemiology Network of Arteriopathy (GENOA) and Howard University Family Study (HUFS)] as well as one native African sample (Nigerian study), with a total replication sample size of 11 882. Meta-analysis of the replication set identified a novel variant (rs7726475) on chromosome 5 between the SUB1 and NPR3 genes, as being associated with SBP and DBP (P< 0.0015 for both); in meta-analyses combining the CARe samples with the replication data, we observed P-values of 4.45 × 10(-7) for SBP and 7.52 × 10(-7) for DBP for rs7726475 that were significant after accounting for all the tests performed. Our study highlights that admixture mapping analysis can help identify genetic variants missed by genome-wide association studies because of drastically reduced number of tests in the whole genome.

Recently, simulations based on the Monte Carlo code have been increasingly applied for physics phenomena, patient dose and quality assurance of radiation systems. The objective of this study was to use Monte Carlo simulation and measurement to verify dose and dose reduction in cephalography. The collimator was constructed with 3-mm thick lead plate, and attached to the tube head to remove regions of disinterest in the radiation field. A digital phantom patient was constructed to evaluate patient dose. In addition, detectors of pixel size 1×1 cm² and 0.1×0.1 cm² were constructed to check collimator location. The effective dose according to International Commission on Radiological Protection 103 was calculated with and without collimation. The effective doses for simulation with and without collimation were 5.09 and 11.32 µSv, respectively. The results of the calculated effective dose show 61.7 % reduction of field area and 55 % of effective dose. The Monte Carlo simulation is a good evaluation tool for patient dose.

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We address the joint problem of learning and scheduling in multi-hop wireless network without a prior knowledge on link rates. Previous scheduling algorithms need the link rate information, and learning algorithms often require a centralized entity and polynomial complexity. These become a major obstacle to develop an efficient learning-based distributed scheme for resource allocation in large-scale multi-hop networks. In this work, by incorporating with learning algorithm, we develop provably efficient scheduling scheme under packet arrival dynamics without a priori link rate information. We extend the results to distributed implementation and evaluation their performance through simulations.