An Improved Synthesis of Retinoic Acid from β-Ionone

A convenient and large-scale preparation of retinoic acid 1 from β-ionone in five steps with 38% overall yield is described.The key steps are the epoxidization of 2 with a new methylated agent and the condensation 4 with tetraethyl methylenediphophonate in one-pot procedure to prepare 6.     

免疫珠技术快速检测腹泻性大肠杆菌

采用表面光滑玻璃珠作为载体材料，用1：40的腹泻性大肠杆菌诊断血清进行包被。可制成免疫珠。这种免疫珠能与腹泻性大肠杆菌发生特异性结合。经乳糖蛋白胨培养液培养，然后根据其产酸产气进行判断。能在18h内完成腹泻性大肠杆菌的检测。其灵敏度为样液中含有腹泻性大肠杆菌数必须大于10CFu／mL．该法简单、快速、易操作、灵敏度高。     

铁形态分析进展

本文对Fe（Ⅱ）、Fe（Ⅲ）形态分析在1990－2000年间的发展概况作了综述，内容包括分光光度法、色谱法、流动注射分析法等。引用文献53篇。     

X-ray investigation of the role of the mixed emulsifier in the structure formation in o/w creams

The aim of this research work was to clarify the role of the mixed emulsifier in the structure formation and water binding mode in the case of o/w creams prepared with different surfactants. The swelling behavior of mixed emulsifiers was examined by means of direct investigation methods such as transmission electron microscopy (TEM) and X-ray diffraction. The detailed structure image of the creams was created with the help of the latter. The influence of the structure of the hydrophilic gel phase, and the structural changes during storage were studied with rheological methods. On the basis of the results, it can be stated that the investigated creams had different structures from those mentioned in the literature: surfactant did not create a mixed bilayer with the structure to furnish fatty amphiphile; instead, micelles were formed. These results correlated well with the results of the rheological tests.     

Determination of Gossypol in Trace Level by Flow Injection Analysis with Chemiluminescence Detection

Gossypol, [1, 1′, 6, 6′, 7,7′-hexahydroxyl-5,5′-diisopropyl-3,3′-dimethyl-(2,2′-binaphtha- lene)-8,8′-dicarboxaldehyde] is a polyphenolic yellow compound naturally occurring in various parts of cotton plants1. The compound has been associated with     

Ab initio Valence Bond Study on AB-type Molecules,A Description for XH(X=L,Be,B,C,N,O,F) and XF(X=Li,Be,B)

Ab initio valence bond method is employed to quantitatively study the concepts of ionic resonance energy and ionicity of a chemical bond in the cases of hydrides XH(X=Li,Be,B,C,N,O,F) and fluorides XF(X=Li,Be,B),By establishing the relationship between resonance and stability,and comparing the calculated ionicities with Pauling‘s earlier estimations in the above diatomic molecules,the merits of Pauling‘s classical resonance theory were demonstrated at the ab initio level.     

纳米尺寸团簇NinZrn(n=3～5)的几何结构与成键规律研究

根据化学键理论与拓扑原理，设计了团簇NinZrn(n=3-5)的可能几何构型，并用从头算方法进行构型优化，结果表明，由NiZr组成的团簇原子间的Zr-Zr和Zr-Ni键明显较强，而Ni-Ni的成键较弱，并发与NinZrn(n=3-5)团簇电子性质与有机烯烃分子等瓣相似，原子之间的成键按照强弱相间的规则分布。     

DAD-Net: Classification of Alzheimer’s Disease Using ADASYN Oversampling Technique and Optimized Neural Network

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.     

铬的形态分析研究与进展

综述了Cr(Ⅲ)、Cr(Ⅵ)形态分析的发展。内容包括概述、样品分离富集、光谱分析、色谱分析等。引用文献43篇。