马来酸酯类化合物结构与抑菌活性密度泛函理论研究

采用量子化学密度泛函理论(DFT)研究马来酸酯类化合物对油菜菌核病菌抗菌活性EM50的构效关系(QSAR)。在DFT-B3LYP方法及基组6-31G(d)水平下,对14种马来酸酯类化合物分子进行几何及电子结构全优化,选择最高占据轨道能EHOMO,次最高占据轨道能ENHOMO,最低空轨道能ELUMO,次最低空轨道能ENLUMO,分子中原子的净电荷QZ,偶极矩μ,零点振动能Ev,分子总能量E,热容Cv和熵Sm等作为量子化学描述符qL。通过最佳变量子集回归分析筛选出影响EM50的主要因素,并建立了QSAR模型。通过留一法交叉分析和方差膨胀因子法检测发现,模型具有良好的稳定性及预测能力。拟合的判定系数R2及交叉验证系数R2cv分别为0.910,0.818。分析结果表明,EHOMO和O7原子上净电荷QO7是影响油菜菌核病菌抗菌活性的主要因素。EHOMO越高,抑菌活性越强;QO7越大,抑菌活性越弱。该文QSAR模型可为设计具有高抑菌活性的马来酸酯类化合物提供理论参考。     

应用FQ-PCR检测孕妇外周血中胎儿细胞含量

合成一对染色体单拷贝基因GAPDH特异的引物和一对Y染色体单拷贝基因SRY特异的引物,并合成两条特异性TaqMan探针,加等量的DNA到含有上述两个引物探针的PCR反应体系中,行荧光定量PCR,获得两种基因的定量值,通过这两种基因含量的比值,计算孕妇外周血中胎儿细胞含量.在30 μL FQ-PCR反应体系中,加入最少约0.05 ng 正常男性基因组DNA能够稳定地获得扩增曲线;在60例临床孕妇外周血标本DNA 的DCFQPCR扩增中有35例出现SRY扩增阳性,阳性率为58.3%;在这些标本中,GAPDH 基因Ct值范围在20~22之间;SRY 基因Ct值范围在33~40;经计算,胎儿细胞含量范围在1/10.4~1/10.6.     

Back-propagation network improved by conjugate gradient based on genetic algorithm in QSAR study on endocrine disrupting chemicals

Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships （QSARs）-based fast screening approaches are urgently needed for the assessment of the potential risk of endocrine disrupting chemicals （EDCs）. The artificial neural networks （ANN） are capable of recognizing highly nonlinear relationships, so it will have a bright application prospect in building high-quality QSAR models. As a popular supervised training algorithm in ANN, back-propagation （BP） converges slowly and immerses in vibration frequently. In this paper, a research strategy that BP neural network was improved by conjugate gradient （CG） algorithm with a variable selection method based on genetic algorithm was applied to investigate the QSAR of EDCs. This resulted in a robust and highly predictive ANN model with R2 of 0.845 for the training set, q^2 pred of 0.81 and root-mean-square error （RMSE） of 0.688 for the test set. The result shows that our method can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.     

HP-β-CD降低胶束化作用对十二烷基苯磺酸钠与甜菜碱复配体系检测光谱的干扰

为克服十二烷基苯磺酸钠与甜菜碱之间的胶束化作用及相互协同作用对二者定量分析产生的干扰,按十二烷基苯磺酸钠的摩尔计量比加入1∶1的羟丙基-β-环糊精,分别采用同步荧光光谱法及"化学-光谱"分析法检测复配体系中二者含量.结果表明:十二烷基苯磺酸钠的临界胶束浓度值随甜菜碱浓度的增加而降低,当甜菜碱的浓度增加到1.0、2.0及3.0mmol/L时,十二烷基苯磺酸钠的临界胶束浓度由1.256mmol/L分别降低至1.121、0.989及0.684mmol/L;甜菜碱还能对低浓度十二烷基苯磺酸钠溶液(浓度低于临界胶束浓度)的同步荧光强度产生明显影响.另一方面,十二烷基苯磺酸钠对"化学-光谱"分析法检测甜菜碱也能产生明显干扰.加入羟丙基-β-环糊精后,能显著降低十二烷基苯磺酸钠和甜菜碱之间的相互干扰,复配体系中十二烷基苯磺酸钠和甜菜碱的回收率分别由79.7%~107.5%及119.7%~159.6%改变至101.5%~102.5%和101.5%~103.0%.     

The application of a new approach based on organic homo‐rank compounds and homologous compounds to the structure–property relationship study of mono‐substituted alkanes

The two conceptual systems of organic homologous compounds and homo‐rank compounds give insight into the influence of structures on the properties of mono‐substituted alkanes X_i–(CH₂)_j–H from the transverse (change of repeating unit number j of CH₂) and longitudinal (change of functional group X_i) perspectives, respectively. This paper aims to combine the organic homo‐rank compounds approach together with the homologous compounds approach to explore the property change rules of mono‐substituted alkanes involving various substituents. Firstly, based on the concept of organic homologous compounds, the properties of mono‐substituted straight‐chain alkane homologues were linearly correlated to the two‐thirds power of the number of carbon atoms (N^2/3) in alkyl, and regression equations such as Q = A + BN^2/3 were obtained. The regression coefficients A and B vary with different substituents X_i, so coefficients A and B were employed to characterize the structural information of substituent X_i. The structural features of alkyls (–(CH₂)_j–H, that is, –C_jH_2j+1) were described by the polarizability effect index (PEI_(R)) and vertex degree–distance index (VDI). Then based on four parameters A, B, PEI_(R), and VDI, quantitative structure–property relationship models were built for the boiling points (Bp) and refractive indexes (n_D) of each mono‐substituted alkane homo‐rank series, where j = 3–10 and the substituents X_i involve F, Cl, Br, I, NO₂, CN, NH₂, COOH, CHO, OH, SH, and NC. Good results indicate that the combination of an organic homo‐rank compounds method and a homologous compounds method has exhibited obvious advantages over traditional methods in the quantitative structure–property relationship study of mono‐substituted alkanes concerning various substituents. Copyright © 2015 John Wiley & Sons, Ltd.     

太赫兹时域光谱的α/β型HMX定量研究

α/β型HMX为同分异构体,物理化学特性有较大差异。为了对α/β型HMX混合物进行定量研究,利用太赫兹时域光谱技术,对α/β型HMX单质与一定比例混合物进行了检测与分析。获得单质α/β型HMX在0.2~2 THz频段标准太赫兹吸收谱,采用线性拟合对α/β型HMX混合的太赫兹吸收谱进行分析建立预测方程,并预测未知样品。结果表明:α/β型HMX混合物在0.82 THz处的吸收强度与α型HMX的含量有较好的线性关系。α/β型HMX混合物在0.82 THz处的吸收特性可以用来进行α/β型HMX的定量检测。     

Evaluation and comparison of mid-infrared,Raman and near-infrared spectroscopies for characterization and determination of the compositions in fully biodegradable poly(lactic acid)/poly(propylene carbonate)/poly(butylene adipate-co-terephthalate) blends combined with chemometrics

In this work, mid-infrared (MIR), Raman and near-infrared (NIR) spectroscopies were evaluated and compared for characterization and determination of the compositions in poly(lactic acid)/poly(propylene carbonate)/poly(butylene adipate-co-terephthalate) (PLA/PPC/PBAT) blends via chemometrics. Qualitative analysis of MIR, Raman, and NIR spectra of the three compositions was performed. Partial least squares (PLS) models were developed based on each spectroscopy for quantitative determination of the concentrations. The data suggested that MIR and Raman have an advantage over NIR in terms of qualitative recognition of the three compositions. The data also showed that Raman and NIR succeeded in determining the concentrations, while the concentration determined via MIR was inaccurate. Hence, Raman is the optimal analytical tool for qualitative characterization and quantitative determination of the compositions in fully biodegradable PLA/PPC/PBAT blends. The characteristic bands in the Raman spectra clearly identify PLA, PPC, and PBAT to be 392 cm^?1 (δ CCO), 948 cm^?1 (ν C?O?C) and 1600 cm^?1 (ν C ? C in benzene ring), respectively. The optimal calibration models based on Raman for PLA, PPC, and PBAT exhibited root mean square error of prediction (RMSEP) values of 3.140%, 3.576%, and 2.538%, respectively.