Modeling of retention of adrenoreceptor agonists and antagonists on polar stationary phases in hydrophilic interaction chromatography: a review

Retention prediction models for reversed-phase liquid chromatography (RPLC) have been extensively studied owing to the fact that RPLC remains the most widely used chromatographic technique especially in the field of pharmaceutical and biomedical analyses. However, RPLC is not always the method of choice for the analysis of some compounds that have high polarity. Hydrophilic interaction chromatography (HILIC) has been gaining interest in the last few years as an alternative option to RPLC for the analysis of polar and hydrophilic analytes. HILIC is a variant of normal-phase liquid chromatography, but utilizes water in a water-miscible organic solvent as the eluent in conjunction with a hydrophilic stationary phase. The present review aims to summarize recent contributions on the development of retention prediction models for a group of basic analytes, namely, the adrenoreceptor agonists and antagonists, on different polar stationary phases. The use of multiple linear regression and artificial neural networks in model building is highlighted.     

Estimation of Average Comonomer Content of Ethylene/1-Olefin Copolymers Using Crystallization Analysis Fractionation (Crystaf) and Artificial Neural Network (ANN)

Summary: An artificial neural network (ANN) with a 4-3-3-1 architecture was developed to estimate average comonomer content of ethylene/1-olefin copolymers from crystallization analysis fractionation (Crystaf) results. The ANN was trained with a back propagation algorithm. It was found that average comonomer contents predicted from ANN agree well with experimental results for both training and testing data sets. The developed ANN was also used to systematically investigate the effects of chain microstructures and Crystaf operating conditions on Crystaf calibration curves.     

扑热息痛片剂药品的近红外光谱法非破坏定量分析

现代近红外光谱分析技术将近红外光谱 (NIR)法同计算机科学和化学计量学结合 ,实现了对样品进行无损非破坏性定量分析 .该法具有速度快、操作简单及所需样品少等特点 ,能够实现样品分析的时间同步、地点同步及无损非破坏分析 .为实现生产过程中即时、在线的质量控制提供了新的手段[1 ] .本文应用人工神经网络 [2 ]与近红外漫反射光谱相结合对扑热息痛片剂药品进行了非破坏快速定量分析 .用扑热息痛片剂药品的近红外漫反射光谱数据、一阶导数光谱数据及二阶导数光谱数据分别建立了 ANN模型 ,预测未知样品 ,讨论了影响网络的因素 ,使用了新…     

Mass deacidification and reinforcement of papers and books VI - Study of aminopropylmethyldiethoxysilane treated papers

In libraries and archives some of the items which, upon ageing, have acidified considerably since their production are so brittle that they cannot be handled without risking loss of material. In contrast to current deacidification processes, aminoalkylakoxysilanes (AAAS) improve the mechanical properties of paper. A simple AAAS, aminopropylmethyldiethoxysilane (AMDES) was used as a model to better understand previous observations made on these systems (hydrolysis, condensation and possible reactions with the organic substrate). The evaluation of the mechanical properties of papers that were treated with AMDES showed that there was no formation of a polymer network on the fibres’ surface. However, treated papers not only exhibited a high alkaline reserve, but also a significant increase in both their tensile breaking length and their folding endurance. Treatment of hygrothermally aged papers with AMDES provided improved folding endurance as well as a good resistance to ageing. Various oxidation treatments of the model papers using sodium hypochlorite led to the conclusion that the nature of the oxidised groups formed on cellulose did not seem to play a significant role in the strengthening mechanism but rather that this effect of AMDES mainly arose from hydrogen bonding between the two molecules.     

Extending the dynamic range of the determination of copper by adsorption differential pulse stripping method using a principal component artificial neural network

A principal component artificial neural network (PC-ANN) has been applied for the analysis of the voltammogram data of Cu(II) and Cu(II)–PAN complex for extending the dynamic range of the determination of Cu(II) (5–550 μg/l). A three layer back-propagation network (6:5:1) was used with learning rate (r=0.09) and momentum (m=0.9), with sigmoidal transfer function in the hidden layer and one bias node in the input layer. Overall, the application of PC-ANN enables the extension of the dynamic range of the determination of Cu(II) from its narrow linear range (5–50 μg/l) to the high dynamic range (5–550 μg/l).     

应用反向传播神经网络预测化合物脑血分配系数

选用27种三维结构性质描述符对脑血分配系数预测建立神经网络模型.网络模型选用典型的适合函数逼近的两层结构神经网络对脑血分配系数(lgBB,BB为脑血浓度比)进行预测,计算中采用的模型具有一个双曲正切型激活函数的隐含层和一个线性激活函数的输出层.计算表明,使用小心选择的反向传播神经网络模型对化合物脑血分配系数具有较好的预测能力.     

Madaline网络用于分光光度法同时测定铜钴镍的研究

采用4－（2－吡啶偶氮）－间苯二酚（PAR）－Cu,Co,Ni显色体系，应用Madline神经网络原理，对于吸收光谱严重重叠的三组分金属配合物体系同时进行含量测定，Cu^2 ,Co^2 ,Ni^2 的平均回收率分别为94％，101％，102％，实验表明，该法具有训练速度快，预测结果准确度良好等特点，有望成为多组分分析的有效方法之一。     

辨识药物定量构效关系的模糊神经网络方法研究

提出一种基于遗传算法的新型模糊神经网络方法,用于计算Benzodiazepines(BZs)类药物的定量构效关系.这类模糊神经网络综合了神经网络、遗传算法与模糊逻辑的各自优势,具有优良的定量构效关系辨识能力,其学习速度较快,不易陷入局部最小区域;网络知识以模糊语言变量的形式加以表达,不仅易于理解,而且能有效地利用已有的专家经验.一旦通过学习获得规律后,不仅能很好地预测化合物的活性,还能对后续的药物分子设计提供有益的理论指导.