Mechanistic studies of reactions catalysed by diamine oxidase using isotope effects

Diamine oxidase (DAO), the enzyme that is responsible for amine biodegradation in animals, plants and humans, catalyses the biotransformation of amines such as histamine (HA), putrescine, 1-phenylethylamine, tyrosine, tryptamine, serotonine and spermine. The kinetic and solvent isotope effects (SIEs) were applied to study the mechanism of the biotransformation using HA and its methylderivatives. The SIE for the biotransformation of HA, N^τ-methylhistamine and N^π-methylhistamine was found to be 3.58, 2.22 and 5.70 on V_max, and 1.58, 1.06 and 1.14 on V_max/K_M, respectively. On the other hand, the kinetic isotope effect for oxidation of stereospecifically deuterium-labelled [(α R)-²H]-N^τ-methylhistamine and [(α R)-²H]-N^π-methylhistamine was 0.69 and 0.62 on V_max, and 15.06 and 7.50 on V_max/K_M, respectively. These results demonstrate that DAO catalyses amine biotransformation by stereospecifically cleaving the αC\bond H bond in the pro-S position. Moreover, the oxidation of amine to aldehyde involves several transition states, including hybridisation change from sp³ (Schiff base) to sp² (imine), then back again to sp³ to give a final product with hybridisation sp² (aldehyde).     

Portfolio selection with a minimax measure in safety constraint

In this paper, we attempt to design a portfolio optimization model for investors who desire to minimize the variation around the mean return and at the same time wish to achieve better return than the worst possible return realization at every time point in a single period portfolio investment. The portfolio is to be selected from the risky assets in the equity market. Since the minimax portfolio optimization model provides us with the portfolio that maximizes (minimizes) the worst return (worst loss) realization in the investment horizon period, in order to safeguard the interest of investors, the optimal value of the minimax optimization model is used to design a constraint in the mean-absolute semideviation model. This constraint can be viewed as a safety strategy adopted by an investor. Thus, our proposed bi-objective linear programming model involves mean return as a reward and mean-absolute semideviation as a risk in the objective function and minimax as a safety constraint, which enables a trade off between return and risk with a fixed safety value. The efficient frontier of the model is generated using the augmented -constraint method on the GAMS software. We simultaneously solve the ratio optimization problem which maximizes the ratio of mean return over mean-absolute semideviation with same minimax value in the safety constraint. Subsequently, we choose two portfolios on the above generated efficient frontier such that the risk from one of them is less and the mean return from other portfolio is more than the respective quantities of the optimal portfolio from the ratio optimization model. Extensive computational results and in-sample and out-of-sample analysis are provided to compare the financial performance of the optimal portfolios selected by our proposed model with that of the optimal portfolios from the existing minimax and mean-absolute semideviation portfolio optimization models on real data from S&P CNX Nifty index.     

MIL-101(Fe)高效催化 β-蒎烯与甲醛的Prins缩合制备诺卜醇

通过水热晶化法制备了MIL-101(Fe)金属有机骨架材料, 利用X射线衍射(XRD)、 傅里叶变换红外光谱(FTIR)、 热重分析(TG)、 扫描电子显微镜(SEM)、 透射电子显微镜(TEM)和X射线光电子能谱(XPS)对催化剂的结构和形貌进行了表征. 结果表明, 该材料用于催化β-蒎烯与甲醛的Prins缩合制备诺卜醇反应的效果优异; 催化剂合成温度、 合成时间、 催化剂用量、 反应溶剂、 反应温度和反应时间对β-蒎烯的反应结果均有一定影响. 在相似的反应条件下, 合成的MIL-101(Fe)催化β-蒎烯制备诺卜醇反应的最佳条件为使用150 ℃下反应15 h合成的催化剂MIL-101(Fe), 在90 ℃下反应8 h得到的β-蒎烯转化率高达97.3%, 诺卜醇选择性达到96.7%.