PASE synthesis of 1-azolyl-1H-1,2,4-triazoles by the reaction of diazoazoles with ethyl isocyanoacetate

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Modeling of the glycine tripeptide cyclization in the Ser65Gly/Tyr66Gly mutant of green fluorescent protein

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Multistrain edge‐based compartmental model on networks

Multistrain diseases, which are infected through individual contacts, pose severe public health threat nowadays. In this paper, we build competitive and mutative two‐strain edge‐based compartmental models using probability generation function (PGF) and pair approximation (PA). Both of them are ordinary differential equations. Their basic reproduction numbers and final size formulas are explicitly derived. We show that the formula gives a unique positive final epidemic size when the reproduction number is larger than unity. We further consider competitive and mutative multistrain diseases spreading models and compute their basic reproduction numbers. We perform numerical simulations that show some dynamical properties of the competitive and mutative two‐strain models.     

Unconditionally Superclose Analysis of a New Mixed Finite Element Method for Nonlinear Parabolic Equation

This paper develops a framework to deal with the unconditional superclose analysis of nonlinear parabolic equation. Taking the finite element pair $Q_{11}/Q_{01} × Q_{10}$ as an example, a new mixed finite element method (FEM) is established and the $τ$ -independent superclose results of the original variable $u$ in $H^1$-norm and the flux variable $\mathop{q} \limits ^{\rightarrow}= −a(u)∇u$ in $L^2$-norm are deduced ($τ$ is the temporal partition parameter). A key to our analysis is an error splitting technique, with which the time-discrete and the spatial-discrete systems are constructed, respectively. For the first system, the boundedness of the temporal errors is obtained. For the second system, the spatial superclose results are presented unconditionally, while the previous literature always only obtain the convergent estimates or require certain time step conditions. Finally, some numerical results are provided to confirm the theoretical analysis, and show the efficiency of the proposed method.     

Existence of strong minimizers for the Griffith static fracture model in dimension two

We consider the Griffith fracture model in two spatial dimensions, and prove existence of strong minimizers, with closed jump set and continuously differentiable deformation fields. One key ingredient, which is the object of the present paper, is a generalization to the vectorial situation of the decay estimate by De Giorgi, Carriero, and Leaci. This is based on replacing the coarea formula by a method to approximate $SB{D}^p$ functions with small jump set by Sobolev functions, and is restricted to two dimensions. The other two ingredients will appear in companion papers and consist respectively in regularity results for vectorial elliptic problems of the elasticity type and in a method to approximate in energy $GSB{D}^p$ functions by $SB{V}^p$ ones.     

Stevia rebaudiana targeting α-amylase: An in-vitro and in-silico mechanistic study

Diabetes mellitus (DM) is the fastest growing metabolic disorder in the world. Recently, more attention is paid to the study of natural products due to side effects of synthetic drugs. Stevia rebaudiana (Bertoni) is considered an encouraging starting point for the antidiabetic lead development. In the present study, the in vitro α-amylase inhibitory activity of the extracts of S. rebaudiana is investigated. In order to understand the molecular mechanism and future pharmacophore development, in silico study of secondary metabolites isolated from S. rebaudiana was carried out. Results indicated that water extract shows highest α-amylase inhibitory activity as compared to other extracts. Moreover, compound 20 (rebaudioside A) which has been previously reported and isolated from water extract showed the impressive binding profile with α-amylase. Therefore, our study suggests that S. rebaudiana could be used in the development of therapeutic drugs for the treatment of diabetes.