Determination of protoapigenone in rat plasma by high‐performance liquid chromatography with UV detection and its application in pharmacokinetic studies

A simple and sensitive HPLC method using UV detection was developed to determine the concentration of protoapigenone in rat plasma. Chromatographic separation was conducted on a C₁₈ column with a mobile phase consisting of an acetonitrile–methanol–aqueous phase (containing 0.2% acetic acid, pH 3.0) system at a flow rate of 1.0 mL/min. The UV detector was set at 248 nm. The calibration curve was linear over the range of 0.031–10.0 µg/mL. The lower limit of quantification was 31 ng/mL. The recoveries for plasma samples ranged from 70.3 to 82.5%. The intra‐ and inter‐day accuracy and precision fulfilled the international standards. This method was successfully applied to a pharmacokinetic study of protoapigenone in rats after oral administration of protoapigenone. It was shown that protoapigenone could be absorbed rapidly after oral administration and could reach the maximum concentration within 1 h. Copyright © 2013 John Wiley & Sons, Ltd.     

Normal Coordinate Analysis of Long Chain Mono-Unsaturated Alcohol Acetates

Long chain mono-unsaturated alcohol acetates are important in practice as insect sex pheromones of lepidoptera. We have studied the relationship between the vibrational spectra of this kind of compounds and their structures⁽¹⁾. The compounds are similar in vibrational spectra even though they have differerces in both the length of chain and the position of double bond. The normal coordinate analysis of these compounds have not been reported in the literature yet.     

Zero-valent iron particles embedded on the mesoporous silica–carbon for chromium (VI) removal from aqueous solution

Journal of Nanoparticle Research - The demand for metal nanoparticles is increasing strongly. Transferred arc synthesis is a promising process in this respect, as it shows high production rates,...     

Binding numbers for fractional ID-k-factor-critical graphs

LetG be a graph,and k≥2 be a positive integer.A graph G is fractional independentset-deletable k-factor-critical(in short,fractional ID-k-factor-critical),if G I has a fractional k-factor for every independent set I of G.The binding number bind(G)of a graph G is defined as bind(G)=min|NG(X)||X|:=X V(G),NG(X)=V(G).In this paper,it is proved that a graph G is fractional ID-k-factor-critical if n≥6k 9 and bind(G)(3k 1)(n 1)kn 2k+2.     

Network-based naive Bayes model for social network

Naive Bayes (NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes (NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational effciency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance. A real data example is also analyzed for illustration purpose.     

Evaluation of approximations for concentration-dependent micropore diffusion in sorbent with bidisperse pore structure

Average diffusivity linear driving force (AD-LDF) and concentration-dependent diffusivity linear driving force (CDD-LDF) approximations are introduced to simplify the precise model describing the concentration-dependent micropore diffusion in bidisperse sorbents, and are compared with the precise model in predicting the dynamics of a sorption process under two different perturbations (i.e., step change perturbations and sinusoidal wave perturbation) with different concentrations imposed at the exterior surface of the bidisperse sorbent. The performance of the two approximations is validated by the precise model and experiments. The AD-LDF performs better in step adsorption and CDD-LDF does better in step desorption. Under sinusoidal wave perturbation, the CDD-LDF performs better. The different levels of consistency of the two approximations with the precise model are attributed to the different definitions of the diffusivities.