多种天敌控制多种害虫的模糊数学模型

从生态经济学的原理出发，利用模糊集理论，探讨如何评价天敌控制多种害虫的为害作用，提出了多种天敌控制多种害虫的模糊数学模型。同时又得到了天敌对害虫的控制能力C，影响率G，影响强度H，及权数ω(x)的计算公式，以及在天敌作用下安全度的讨论与分析。     

Cardiac Fibroblasts Contribute to Myocardial Dysfunction in Mice with Sepsis: The Role of NLRP3 Inflammasome Activation

Myocardial contractile dysfunction in sepsis is associated with the increased morbidity and mortality. Although the underlying mechanisms of the cardiac depression have not been fully elucidated, an exaggerated inflammatory response is believed to be responsible. Nucleotide-binding oligomerization domain-like receptor containing pyrin domain 3 (NLRP3) inflammasome is an intracellular platform that is involved in the maturation and release of interleukin (IL)-1β. The aim of the present study is to evaluate whether sepsis activates NLRP3 inflammasome/caspase-1/IL-1β pathway in cardiac fibroblasts (CFs) and whether this cytokine can subsequently impact the function of cardiomyocytes (cardiac fibroblast-myocyte cross-talk). We show that treatment of CFs with lipopolysaccharide (LPS) induces upregulation of NLRP3, activation of caspase-1, as well as the maturation (activation) and release of IL-1β. In addition, the genetic (small interfering ribonucleic acid [siRNA]) and pharmacological (glyburide) inhibition of the NLRP3 inflammasome in CFs can block this signaling pathway. Furthermore, the inhibition of the NLRP3 inflammasome in cardiac fibroblasts ameliorated the ability of LPS-chalenged CFs to impact cardiomyocyte function as assessed by intracellular cyclic adenosine monophosphate (cAMP) responses in cardiomyocytes. Salient features of this the NLP3 inflammasome/ caspase-1 pathway were confirmed in in vivo models of endotoxemia/sepsis. We found that inhibition of the NLRP3 inflammasome attenuated myocardial dysfunction in mice with LPS and increased the survival rate in mice with feces-induced peritonitis. Our results indicate that the activation of the NLRP3 inflammasome in cardiac fibroblasts is pivotal in the induction of myocardial dysfunction in sepsis.     

Identification of up-regulated genes in human uterine leiomyoma by suppression subtractive hybridization

In searching for differentially expressed genes in human uterine leiomyomas (ULs), suppression sub-tractive hybridization was used to construct an UL up-regulated library, which turned out to represent 88genes. After two rounds of screening by reverse Northern analysis, twenty genes were proved to be up-regulated, including seventeen known genes and three genes with unknown function. All these genes werefirstly associated with UL. Three genes with notable difference were selected for Northern confirmationOur results proved the authenticity of the twenty genes. One gene named Phospholipase A2 (PLA2) showedup-regulation in 4/6 of the patients and investigation of tissue distribution indicated that it had obviousexpression in prostate, testis, liver, heart and skeletal muscle.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.