Loss of function,missense, and intronic variants in NOTCH1 confer different risks for left ventricular outflow tract obstructive heart defects in two European cohorts

Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO). However, the risk conferred by rare and noncoding variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare or low-frequency missense variants were found in 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and noncoding variants in NOTCH1 in 1,085 cases and 332,788 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO amongst European-ancestry individuals. This result was replicated in an independent analysis of 210 cases and 68,762 controls of non-European and mixed ancestry. In conclusion, carrying rare predicted loss of function variants in NOTCH1 confer significant risk for LVOTO. In addition, the two intronic variants seem to be associated with an increased risk for these defects. Our approach demonstrates the utility of population-based data sets in quantifying the specific risk of individual variants for disease-related phenotypes.     

Host MyD88 signaling protects against acute graft-versus-host disease after allogeneic bone marrow transplantation

Recent experimental strategies to reduce graft-versus-host disease (GVHD) have focused largely on modifying innate immunity. Toll-like receptor (TLR)-driven myeloid differentiation primary response 88 (MyD88)-dependent signalling pathways that initiate adaptive immune function are also critical for the pathogenesis of GVHD. This study aimed to delineate the role of host MyD88 in the development of acute GVHD following fully major histocompatibility complex-mismatched allogeneic bone marrow transplantation (BMT). When myeloablated BALB/c MyD88 knock-out recipients were transplanted with C57BL/6 (B6) donor cells, they developed significantly more severe GVHD than wild-type (WT) BALB/c hosts. The increased morbidity and mortality in MyD88^–/– mice correlated with increased serum levels of lipopolysaccharide and elevated inflammatory cytokines in GVHD target organs. Additionally, MyD88 deficiency in BMT recipients led to increased donor T cell expansion and more donor CD11c⁺ cell intestinal infiltration with apoptotic cells but reduced proliferation of intestinal epithelial cells compared with that in WT BMT recipients. Decreased expression of tight junction mRNA in epithelial cells of MyD88^–/– mice suggested that MyD88 contributes to intestinal integrity. Cox-2 expression in the GVHD-targeted organs of WT mice is increased upon GVHD induction, but this enhanced expression was obviously inhibited by MyD88 deficiency. The present findings demonstrate an unexpected role for host MyD88 in preventing GVHD after allogeneic BMT.     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

铜绿假单胞菌噬菌体PaP3生物学特性的研究

目的测定铜绿假单胞菌噬菌体PaP3的最佳感染复数、一步生长曲线和吸附K值及交叉吸附K值等基本生物学特性。方法按照感染复数（MOI）分别为0．0001、0．001、0．01、0．1、1和10加入噬菌体纯培养液和宿主菌，充分裂解细菌后，测定噬菌体滴度；以MOI=10的比例加入噬菌体及宿主菌，进行一步生长实验；纯化PaP3颗粒，免疫家兔，获得抗血清，通过中和反应实验测定PaP3和其抗血清之间的吸附反应常数K值。同时，利用抗血清交叉中和试验确定本室分离的三株噬菌体之间的血清学关系。结果当MOI=0．001时PaP3感染其宿主菌产生的子代噬菌体滴度最高；根据一步生长实验结果绘制一步生长曲线；通过血清交叉中和反应得出不同的吸附常数。结论PaP3最佳感染复数为0．001，感染宿主菌的潜伏期是20min，爆发期是60min，平均爆发量约为31，其抗血清反应的吸附常数K值为262。