Topological susceptibility in finite temperature QCD with physical (u/d,s,c)(u/d, s, c) domain-wall quarks

We perform hybrid Monte-Carlo (HMC) simulation of lattice QCD with $N_f=2+1+1$ domain-wall quarks at the physical point, on the $64^3 \times (64,20,16,12,10,8,6)$ lattices, each with three lattice spacings. The lattice spacings and the bare quark masses are determined on the $64^4$ lattices. The resulting gauge ensembles provide a basis for studying finite temperature QCD with $N_f=2+1+1$ domain-wall quarks at the physical point. In this paper, we determine the topological susceptibility of the QCD vacuum for $T > T_c \sim 150$ MeV. The topological charge of each gauge configuration is measured by the clover charge in the Wilson flow at the same flow time in physical units, and the topological susceptibility $\chi_t(a,T)$ is determined for each ensemble with lattice spacing $a$ and temperature $T$. Using the topological susceptibility $\chi_t(a,T)$ of 15 gauge ensembles with three lattice spacings and different temperatures in the range $T \sim 155-516$ MeV, we extract the topological susceptibility $\chi_t(T)$ in the continuum limit. Moreover, a detailed discussion on the reweighting method for domain-wall fermion is presented.Comment: 36 pages, 5 figure

Decay Constants of Pseudoscalar DD-mesons in Lattice QCD with Domain-Wall Fermion

We present the first study of the masses and decay constants of the pseudoscalar $D$ mesons in two flavors lattice QCD with domain-wall fermion. The gauge ensembles are generated on the $24^3 \times 48$ lattice with the extent $N_s = 16$ in the fifth dimension, and the plaquette gauge action at $\beta = 6.10$, for three sea-quark masses with corresponding pion masses in the range $260-475$ MeV. We compute the point-to-point quark propagators, and measure the time-correlation functions of the pseudoscalar and vector mesons. The inverse lattice spacing is determined by the Wilson flow, while the strange and the charm quark masses by the masses of the vector mesons $\phi(1020)$ and $J/\psi(3097)$ respectively. Using heavy meson chiral perturbation theory (HMChPT) to extrapolate to the physical pion mass, we obtain $f_D = 202.3(2.2)(2.6)$ MeV and $f_{D_s} = 258.7(1.1)(2.9)$ MeV.Comment: 15 pages, 3 figures. v2: the statistics of ensemble (A) with m_sea = 0.005 has been increased, more details on the systematic error, to appear in Phys. Lett.

Paeonol Protects Memory after Ischemic Stroke via Inhibiting β-Secretase and Apoptosis

Poststroke dementia commonly occurs following stroke, with its pathogenesis related to β-amyloid production and apoptosis. The present study evaluate the effects of paeonol, one of the phenolic phytochemicals isolated from the Chinese herb Paeonia suffruticosa Andrews (MC), on protection from memory loss after ischemic stroke in the subacute stage. Rats were subjected to transient middle cerebral artery occlusion (tMCAo) with 10 min of ischemia. The data revealed that paeonol recovered the step-through latency in the retrieval test seven days after tMCAo, but did not improve the neurological deficit induced by tMCAo. Levels of Amyloid precursor protein (APP)- and beta-site APP cleaving enzyme (BACE; β-secretase)-immunoreactive cells, and terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL)-positive cells decreased in the paeonol-administered group. Western blotting revealed decreased levels of Bax protein in mitochondria and apoptosis-inducing factor (AIF) in cytosol following paeonol treatment. In conclusion, we speculate that paeonol protected memory after ischemic stroke via reducing APP, BACE, and apoptosis. Supression the level of Bax and blocking the release of AIF into cytosol might participate in the anti-apoptosis provided by paeonol

GenEpi: gene-based epistasis discovery using machine learning.

BackgroundGenome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD).ResultsIn this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.ConclusionsThe results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future