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The matrix solid-phase dispersion (MSPD) was applied for extracting seven sulfonamides (SAs) in liver samples. The separation and determination were carried out by high-performance liquid chromatography. The analytes were derivated with fluorescamine and detected with fluorescence detector. The types of dispersion adsorbents for MSPD were examined and the highest recovery was obtained when the diatomaceous earth was used as the dispersion adsorbent and the mass ratio of dispersion adsorbent to sample was 3:1. The acetone was used as the elution solvent. Under the optimal conditions, the linear range for determining the SAs in liver samples was 5.0-1000.0 ng/g. The porcine, chicken and cattle liver samples were analyzed and the average recoveries of seven SAs were higher than 84.6%.

Introduction: Major depressive disorder (MDD) is one of the most common mental disorders worldwide. The aim of this study was to identify potential pathological genes in MDD. Methods: We searched and downloaded gene expression data from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) in MDD. Then, Kyoto Encyclopedia of Genes and Genomes pathway, Gene Ontology analysis, and protein-protein interaction (PPI) network were applied to investigate the biological function of identified DEGs. The quantitative real-time polymerase chain reaction and a published dataset were used to validate the result of bioinformatics analysis. Results: A total of 514 DEGs were identified in MDD. In the PPI network, some hub genes with high degrees were identified, such as EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, and AP2B1. The result of in vitro validation of RPL26L1, RSRC1, TOMM20L, RPLPO, PRPF8, AP2B1, STIP1, and C5orf45 was consistent with the bioinformatics analysis. Electronic validation of C5orf45, STIP1, PRPF8, AP2B1, and SLC35E1 was consistent with the bioinformatics analysis. Discussion: The deregulated genes could be used as potential pathological factors of MDD. In addition, EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, and AP2B1 might be therapeutic targets for MDD. K E Y W O R D S drug target, gene expression, in vitro validation, major depressive disorder, protein-protein interaction network

This paper addresses the problem of ground moving target relocation (GMTR) for airborne dual-channel wide-area radar systems. The monopulse technique can be utilized to perform GMTR. However, in real conditions, the GMTR performance degrades greatly due to the effect of channel mismatch. To tackle this problem, prior knowledge of the antenna pattern information is fully utilized to improve the GMTR performance, and a knowledge-aided GMTR algorithm (KA-GMTR) for airborne dual-channel wide-area radar is proposed in this paper. First, the GMTR model for the two receiving channels is analyzed. The channel mismatch model is constructed, and its expression is derived. Then, the channel mismatch phase error is well estimated by exploiting the prior antenna pattern information based on the least squares (LS) method. Meanwhile, the knowledge-aided monopulse curve (KA-MPC) is derived to perform the direction of arrival (DOA) estimation for potential targets. Finally, KA-GMTR, based on the KA-MPC, is performed to estimate the azimuth offsets and relocate the geometry positions of the potential targets when channel mismatch occurs. Moreover, the target relocation performance is analyzed, and the intrinsic reason that degrades the target relocation accuracy is figured out. The performance assessment based on airborne real-data, also in comparison to the conventional GMTR method, has demonstrated that our proposed KA-GMTR algorithm offers preferable target relocation results under channel mismatch scenarios.

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