Free fatty acids regulate insulin secretion from pancreatic beta cells through GPR40

Diabetes, a disease in which carbohydrate and lipid metabolism are regulated improperly by insulin, is a serious worldwide health issue. Insulin is secreted from pancreatic beta cells in response to elevated plasma glucose, with various factors modifying its secretion. Free fatty acids (FFAs) provide an important energy source as nutrients, and they also act as signalling molecules in various cellular processes, including insulin secretion. Although FFAs are thought to promote insulin secretion in an acute phase, this mechanism is not clearly understood. Here we show that a G-protein-coupled receptor, GPR40, which is abundantly expressed in the pancreas, functions as a receptor for long-chain FFAs. Furthermore, we show that long-chain FFAs amplify glucose-stimulated insulin secretion from pancreatic beta cells by activating GPR40. Our results indicate that GPR40 agonists and/or antagonists show potential for the development of new anti-diabetic drugs.     

Extracting multiple layers from data having graph structures

Much data such as geometric image data and drawings have graph structures. Such data are called graph structured data. In order to manage efficiently such graph structured data, we need to analyze and abstract graph structures of such data. The purpose of this paper is to find knowledge representations which indicate plural abstractions of graph structured data. Firstly, we introduce a term graph as a graph pattern having structural variables,and a substitution over term graphs which is graph rewriting system. Next,for a graph G,we define a multiple layer (g,（θ1, …,θk)) of G as a pair of a term graph g and a list of 4 substitutionsθ1, …,θk such that G can be obtained from g by applying substitutions θ1, …,θk to g. In the same way,for a set S of graphs, we also define a multiple layer for Sas a pair (D,@) of a set 5 of term graphs and a list 6 of substitutions. Secondly,for a graph G and a set S of graphs,we present effective algorithms for extracting minimal multiple layers of G and S which give us stratifying abstractions of G and S,respectively. Finally,we report experimental results obtained by applying our algorithms to both artificial data and drawings of power plants which are real world data.     

微重力条件下的InxGa1-xSb晶体生长研究

InxGa1-xSb晶体是吸收波长可以在1.7–6.8μm范围内调控的三元半导体晶体,在红外探测、热光伏电池领域具有重要的应用前景,但由于其固液相线相距很远,容易形成成分偏析和结晶缺陷,且重力对流会增加晶体生长界面处物质输送的不均匀性,使得地面环境下难于生长高质量的InxGa1-xSb晶体.微重力环境由于抑制了自然对流,为晶体生长提供了良好条件,本文综述了微重力环境对InxGa1-xSb半导体晶体成分偏析和晶体缺陷的影响,并介绍了中国返回式微重力科学卫星实践十号上的InxGa1-xSb三元晶体的空间生长实验成果.     

Haemagglutinin mutations responsible for the binding of H5N1 influenza A viruses to human-type receptors

H5N1 influenza A viruses have spread to numerous countries in Asia, Europe and Africa, infecting not only large numbers of poultry, but also an increasing number of humans, often with lethal effects. Human and avian influenza A viruses differ in their recognition of host cell receptors: the former preferentially recognize receptors with saccharides terminating in sialic acid-alpha2,6-galactose (SAalpha2,6Gal), whereas the latter prefer those ending in SAalpha2,3Gal (refs 3-6). A conversion from SAalpha2,3Gal to SAalpha2,6Gal recognition is thought to be one of the changes that must occur before avian influenza viruses can replicate efficiently in humans and acquire the potential to cause a pandemic. By identifying mutations in the receptor-binding haemagglutinin (HA) molecule that would enable avian H5N1 viruses to recognize human-type host cell receptors, it may be possible to predict (and thus to increase preparedness for) the emergence of pandemic viruses. Here we show that some H5N1 viruses isolated from humans can bind to both human and avian receptors, in contrast to those isolated from chickens and ducks, which recognize the avian receptors exclusively. Mutations at positions 182 and 192 independently convert the HAs of H5N1 viruses known to recognize the avian receptor to ones that recognize the human receptor. Analysis of the crystal structure of the HA from an H5N1 virus used in our genetic experiments shows that the locations of these amino acids in the HA molecule are compatible with an effect on receptor binding. The amino acid changes that we identify might serve as molecular markers for assessing the pandemic potential of H5N1 field isolates.     

Essential role for the peroxiredoxin Prdx1 in erythrocyte antioxidant defence and tumour suppression

Reactive oxygen species are involved in many cellular metabolic and signalling processes and are thought to have a role in disease, particularly in carcinogenesis and ageing. We have generated mice with targeted inactivation of Prdx1, a member of the peroxiredoxin family of antioxidant enzymes. Here we show that mice lacking Prdx1 are viable and fertile but have a shortened lifespan owing to the development beginning at about 9 months of severe haemolytic anaemia and several malignant cancers, both of which are also observed at increased frequency in heterozygotes. The haemolytic anaemia is characterized by an increase in erythrocyte reactive oxygen species, leading to protein oxidation, haemoglobin instability, Heinz body formation and decreased erythrocyte lifespan. The malignancies include lymphomas, sarcomas and carcinomas, and are frequently associated with loss of Prdx1 expression in heterozygotes, which suggests that this protein functions as a tumour suppressor. Prdx1-deficient fibroblasts show decreased proliferation and increased sensitivity to oxidative DNA damage, whereas Prdx1-null mice have abnormalities in numbers, phenotype and function of natural killer cells. Our results implicate Prdx1 as an important defence against oxidants in ageing mice.     

Extracting multiple layers from data having graph structures

Much data such as geometric image data and drawings have graph structures.Such data are called graph structured data. In order to manage efficiently such graph structured data, we need to analyze and abstract graph structures of such data. The purpose of this paper is to find knowledge representations which indicate plural abstractions of graph structured data. Firstly, we introduce a term graph as a graph pattern having structural variables, and a substitution over term graphs which is graph we also define a multiple layer for S as a pair (D,O) of a set D of term graphs and a list of substitutions. Secondly, for a graph G and a set S of graphs, we present effective algorithms for extracting minimal multiple layers of G and S which give us stratifying abstractions of G and S, respectively. Finally, we report experimental results obtained by applying our algorithms to both artificial data and drawings of power plants which are real world data.