Rapid classification and identification of chemical constituents in Leonurus japonicus Houtt based on UPLC-Q-Orbitrap-MS combined with data post-processing techniques.

Leonurus japonicus Houtt (LJH) is a bulk medicinal material commonly used in clinical practice, but its complex constituents have not been completely understood, posing challenges to pharmacology, pharmacokinetic research, and scientific and rational drug use. As a result, it is critical to develop an efficient and accurate method for classifying and identifying the chemical composition of LJH. In this study, ultra-performance liquid chromatography-quadrupole electrostatic field-orbital trap high resolution mass spectrometry (UPLC-Q-Orbitrap-MS) was successfully established, along with two data post-processing techniques, characteristic fragmentations (CFs) and neutral losses (NLs), to quickly classify and identify the chemical constituents in LJH. As a result, 44 constituents of LJH were identified, including four alkaloids, 20 flavonoids, two phenylpropanoids, 17 organic acids, and one amino acid. The method in this paper enables classification and identification of chemical compositions rapidly, providing a scientific foundation for further research on the effective and toxic substances of LJH.

Research on User Behavior Based on Higher-Order Dependency Network.

In the era of the popularization of the Internet of Things (IOT), analyzing people's daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people's daily behaviors, while the mainstream first-order network (FON) method ignores the high-order dependencies between daily behaviors. A higher-order dependency network (HON) can more accurately mine the requirements by considering higher-order dependencies. Firstly, our work adopts indoor daily behavior sequences obtained by video behavior detection, extracts higher-order dependency rules from behavior sequences, and rewires an HON. Secondly, an HON is used for the RandomWalk algorithm. On this basis, research on vital node identification and community detection is carried out. Finally, results on behavioral datasets show that, compared with FONs, HONs can significantly improve the accuracy of random walk, improve the identification of vital nodes, and we find that a node can belong to multiple communities. Our work improves the performance of user behavior analysis and thus benefits the mining of user requirements, which can be used to personalized recommendations and product improvements, and eventually achieve higher commercial profits.