A pathway analysis of genome-wide association study highlights novel type 2 diabetes risk pathways.

Genome-wide association studies (GWAS) have been widely used to identify common type 2 diabetes (T2D) variants. However, the known variants just explain less than 20% of the overall estimated genetic contribution to T2D. Pathway-based methods have been applied into T2D GWAS datasets to investigate the biological mechanisms and reported some novel T2D risk pathways. However, few pathways were shared in these studies. Here, we performed a pathway analysis using the summary results from a large-scale meta-analysis of T2D GWAS to investigate more genetic signals in T2D. Here, we selected PLNK and VEGAS to perform the gene-based test and WebGestalt to perform the pathway-based test. We identified 8 shared KEGG pathways after correction for multiple tests in both methods. We confirm previous findings, and highlight some new T2D risk pathways. We believe that our results may be helpful to study the genetic mechanisms of T2D

Discovery and Identification of W' and Z' in SU(2) x SU(2) x U(1) Models at the LHC

We explore the discovery potential of W' and Z' boson searches for various SU(2) x SU(2) x U(1) models at the Large Hadron Collider (LHC), after taking into account the constraints from low energy precision measurements and direct searches at both the Tevatron (1.96 TeV) and the LHC (7 TeV). In such models, the W' and Z' bosons emerge after the electroweak symmetry is spontaneously broken. Two patterns of the symmetry breaking are considered in this work: one is SU(2)_L x SU(2)_2 x U(1)_X to SU(2)_L x U(1)_Y (BP-I), another is SU(2)_1 x SU(2)_2 x U(1)_Y to SU(2)_L x U(1)_Y (BP-II). Examining the single production channel of W' and Z' with their subsequent leptonic decays, we find that the probability of detecting W' and Z' bosons in the considered models at the LHC (with 14 TeV) is highly limited by the low energy precision data constraints. We show that observing Z' alone, without seeing a W', does not rule out new physics models with non-Abelian gauge extension, such as the phobic models in BP-I. Models in BP-II would predict the discovery of degenerate W' and Z' bosons at the LHC.Comment: 29 pages, including 11 figures, 3 tables, added references for introductio

Spin superconductor in ferromagnetic graphene

We show a spin superconductor (SSC) in ferromagnetic graphene as the counterpart to the charge superconductor, in which a spin-polarized electron-hole pair plays the role of the spin $2 (\hbar/2)$ `Cooper pair' with a neutral charge. We present a BCS-type theory for the SSC. With the `London-type equations' of the super-spin-current density, we show the existence of an electric `Meissner effect' against a spatial varying electric field. We further study a SSC/normal conductor/SSC junction and predict a spin-current Josephson effect.Comment: 6 pages, 4 figure

University-Industry Technology Transfer: Empirical Findings from Chinese Industrial Firms

The knowledge and innovation generated by researchers at universities is transferred to industries through patent licensing, leading to the commercialization of academic output. In order to investigate the development of Chinese university-industry technology transfer and whether this kind of collaboration may affect a firm's innovation output, we collected approximately 6400 license contracts made between more than 4000 Chinese firms and 300 Chinese universities for the period between 2009 and 2014. This is the first study on Chinese university-industry knowledge transfer using a bipartite social network analysis (SNA) method, which emphasizes centrality estimates. We are able to investigate empirically how patent license transfer behavior may affect each firm's innovative output by allocating a centrality score to each firm in the university-firm technology transfer network. We elucidate the academic-industry knowledge by visualizing flow patterns for different regions with the SNA tool, Gephi. We find that innovation capabilities, R&D resources, and technology transfer performance all vary across China, and that patent licensing networks present clear small-world phenomena. We also highlight the Bipartite Graph Reinforcement Model (BGRM) and BiRank centrality in the bipartite network. Our empirical results reveal that firms with high BGRM and BiRank centrality scores, long history, and fewer employees have greater innovative output