We performed a genome-wide association study of esophageal squamous cell carcinoma (ESCC) by genotyping 1,077 individuals with ESCC and 1,733 control subjects of Chinese Han descent. We selected 18 promising SNPs for replication in an additional 7,673 cases of ESCC and 11,013 control subjects of Chinese Han descent and 303 cases of ESCC and 537 control subjects of Chinese Uygur-Kazakh descent. We identified two previously unknown susceptibility loci for ESCC: PLCE1 at 10q23 (P(Han combined for ESCC) = 7.46 x 10(-56), odds ratio (OR) = 1.43; P(Uygur-Kazakh for ESCC) = 5.70 x 10(-4), OR = 1.53) and C20orf54 at 20p13 (P(Han combined for ESCC) = 1.21 x 10(-11), OR = 0.86; P(Uygur-Kazakh for ESCC) = 7.88 x 10(-3), OR = 0.66). We also confirmed association in 2,766 cases of gastric cardia adenocarcinoma cases and the same 11,013 control subjects (PLCE1, P(Han for GCA) = 1.74 x 10(-39), OR = 1.55 and C20orf54, P(Han for GCA) = 3.02 x 10(-3), OR = 0.91). PLCE1 and C20orf54 have important biological implications for both ESCC and GCA. PLCE1 might regulate cell growth, differentiation, apoptosis and angiogenesis. C20orf54 is responsible for transporting riboflavin, and deficiency of riboflavin has been documented as a risk factor for ESCC and GCA.
Although millimeter wave (mmWave) systems promise to offer larger bandwidth and unprecedented peak data rates, their practical implementation faces several hardware challenges compared to sub-6 GHz communication systems. These hardware constraints can seriously undermine the performance and deployment progress of mmWave systems and, thus, necessitate disruptive solutions in the cross-design of analog and digital modules. In this article, we discuss the importance of different hardware constraints and propose a novel system architecture, which is able to release these hardware constraints while achieving better performance for future millimeter wave communication systems. The characteristics of the proposed architecture are articulated in detail, and a representative example is provided to demonstrate its validity and efficacy.
Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. It incorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the power of nonparametric functional mapping is investigated through simulation studies and demonstrated by examples.
This paper considers the beamspace channel estimation problem in 3D lens antenna array under a millimeter-wave communication system. We analyze the focusing capability of the 3D lens antenna array and the sparsity of the beamspace channel response matrix. Considering the analysis, we observe that the channel matrix can be treated as a 2D natural image; that is, the channel is sparse, and the changes between adjacent elements are subtle. Thus, for the channel estimation, we incorporate an image reconstruction technique called sparse non-informative parameter estimator-based cosparse analysis Ā M P for īmaging (SCAMPI) algorithm. The SCAMPI algorithm is faster and more accurate than earlier algorithms such as orthogonal matching pursuit and support detection algorithms. To further improve the SCAMPI algorithm, we model the channel distribution as a generic Gaussian mixture (GM) probability and embed the expectation maximization learning algorithm into the SCAMPI algorithm to learn the parameters in the GM probability. We show that the GM probability outperforms the common uniform distribution used in image reconstruction. We also propose a phase-shifter-reduced selection network structure to decrease the power consumption of the system and prove that the SCAMPI algorithm is robust even if the number of phase shifters is reduced by 10%.
Index TermsMillimeter wave communication system, lens antenna array, SCAMPI algorithm, GM probability, EM learning.
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