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In this study, a dissipative particle dynamics mehtod was used to study the solubilization enhancing effect of platycodin towards 5 different drug components. Two factors were mainly considered, including the XlogP value of drug components and the sugar chain length of platycodin. As a result, it was found that there was an optimal drug XlogP value for the solubilization enhancing effect of platycodin, and it was different between the drug’s own XlogP value and the optimal drug XlogP value of platycodin that determines the solubilization enhancing effect.

Convolutional neural networks (CNN) and recurrent neural networks (RNN) are widely used in natural language processing. However, the natural language has a certain dependence on the structure. A single CNN model leads to ignore the semantic and grammatical information of words. Traditional RNN has gradient disappearance or gradient dispersion problem. For this reason, the paper designs the CNN-BLSTM network in the way of network level combination, introduces Attention mechanism, and proposes a CNN-BLSTM +Attention model. The fusion model effectively deals with the position invariance of local features and extracted efficient local feature information. Furthermore, Attention mechanism is introduced to automatically weigh the output sequence information at each time to reduce the loss of key features when RNNs are used to model the sequence features. The feature extraction in time and space is completed. The experimental results show that the accuracy of the proposed model is 3 to 4 percentage points higher than that of other models. When dealing with alarm text, the model not only guarantees the local correlation of data, but also strengthens the effective combination ability of sequence features.

With the rapid development of mobile Internet, mobile Internet has come into the era of big data. The demand for data analysis of mobile applications has become more and more obvious, which puts forward higher requirements for the standard of mobile application information collection. Due to the large number of applications, almost all third-party app stores display only a small number of applications, and most of the information is hidden in the Deep Web database behind the query form. The existing crawler strategy cannot meet the demand. In order to solve the above problems, this paper proposes a collection method based on category keywords query to improve the crawl rate and integrity of the mobile app stores information collection. Firstly, get the information of application interfaces that include various kinds of applications by using the vertical crawler. Then extract the keywords that represent each category of applications by TF-IDF algorithm from the application name and description information. Finally, incremental crawling is performed by using keyword query-based acquisition method. Results show that this collection method effectively promoted information integrity and acquisition efficiency.

Alzheimer’s disease (AD) is classified as a neurodegenerative disease, impacting on brain integrity and functioning, resulting in a progressive deterioration of cognitive capabilities. Epigenetic changes can be acquired over the life span and mediate environmental effects on gene expression. DNA-methyltransferase 3A (DNMT3A) plays an important role in the development of embryogenesis and the generation of aberrant methylation late-onset AD (LOAD). In this study, the rs1550117 polymorphism of DNMT3A was determined by polymerase chain reaction/restriction fragment length polymorphism and confirmed by sequencing. The results showed that AA genotype carriers had a 2.08-fold risk of developing LOAD in comparison with GG genotype carriers (odds ratio [OR] = 2.08, 95% confidence interval [CI]: 1.03-4.21, P = .038) and had a 2.05-fold risk for LOAD compared with GG+GA genotype carriers (OR = 2.05, 95% CI: 1.03-4.11, P = .038), indicating that the DNMT3A polymorphism supports a major role in the pathogenesis of LOAD and can be used as a stratification marker to predict an individual’s susceptibility to LOAD.

The catalytic cracking characteristics were investigated in a home-made two-stage fixed bed reactor with the special coke, calcined dolomite, and porous high-alumina clinker as catalysts. The purpose of the present work was to get high gas energy yield by catalytic cracking reaction of biomass. Experiments were conducted at pyrolysis temperature 700 °C and catalytic cracking temperature ranging from 700 to 900 °C using sawdust as biomass raw material. The results show that the special coke is the most effective among three kinds of catalysts on tar conversion rate and gas energy yield, etc. The microstructures of catalysts were observed by scanning electron microscopy (SEM), and influence of catalyst's microstructure on gas energy yield of biomass was studied.