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OBJECTIVE An improved QuEChERS method was established and verified for simultaneous determination of isoxaflutole and its metabolites diketonitrile and benzoic acid analogue residues in corn and plants. This method was mainly used to study the digestion rule and final residue level of isoxaflutole and its metabolites in corn and plants. It was hoped that the safe use of isoxaflutole in corn can be achieved eventually. METHOD: The method was completed by means of ultra‐performance liquid chromatography with triple quadrupole mass spectrometry. The extraction of the target substance was through acetonitrile solution containing 1% acetic acid, and the purification was through primary secondary amine, octadecylsilane and graphitized carbon black sorbent. RESULTS: In the method, the quantitative limits and detection limits of the three analytes were 0.005–0.01 and 0.001–0.003 mg kg−1 respectively. The half‐life of isoxaflutole in the plants in Shandong and Anhui was 36.4 and 42.1 days respectively, and the digestion dynamics all conformed to the first‐order kinetics. The final residues of isoxaflutole in green corn and mature corn were less than 0.02 mg kg−1 of the maximum residue limit set by the Codex Alimentarius Commission. CONCLUSIONS: The residual amount of isoxaflutole in corn and plants at harvest time was acceptable when isoxaflutole was applied once at a dose of 121.5 g a.i. ha−1. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Linear discriminant analysis (LDA) is one of commonly used supervised subspace learning methods. However, LDA will be powerless faced with the no-label situation. In this paper, the unsupervised LDA (Un-LDA) is proposed and first formulated as a seamlessly unified objective optimization which guarantees convergence during the iteratively alternative solving process. The objective optimization is in both the ratio trace and the trace ratio forms, forming a complete framework of a new approach to jointly clustering and unsupervised subspace learning. The extension of LDA into Un-LDA enables to not only complete unsupervised subspace learning via the explicitly presented subspace projection matrix but also simultaneously finish clustering and even clustering out-of-sample data via the explicitly presented transformation matrix. To overcome the difficulty in solving the non-convex objective optimization, we mathematically prove that the Un-LDA optimization in both forms can be transformed into the simple K-means clustering optimization when the subspace is determined. The Un-LDA optimization is eventually completed by alternatively optimizing the clusters using K-means and the subspace using the supervised LDA methods and iterating this whole process until convergence or stopping criterion. The experiments demonstrate that our proposed Un-LDA algorithms are comparable or even much superior to the counterparts. [ABSTRACT FROM AUTHOR]

During the outbreak of the novel coronavirus disease (COVID‐19) in Wuhan, the gastroenterology department of our hospital performed gastrointestinal endoscopy procedures using strict infection control measures. Thorough screening of incoming patients, separation of diagnostic and treatment areas, regional management, hierarchical protection, disinfection protocols, and other measures were enforced to prevent virus transmission during endoscopic treatments. During the COVID‐19 pandemic, between February and March 2020, 159 endoscopic examinations and treatments were performed, including emergency endoscopy for 17 patients. Among these, seven patients were either previously infected with or were suspected carriers of the virus. Using the aforementioned control measures, we did not encounter a single case of cross‐infection or infection among the patients or staff. The presented protocols may provide valuable insight regarding how to protect gastroenterology endoscopy units during the novel coronavirus disease pandemic. [ABSTRACT FROM AUTHOR]

High-mobility group box chromosomal protein (HMGB-1) contributes to osteoarthritis (OA) by modulating various oxidative, inflammatory and apoptotic signaling pathways. The effect of chrysin (CH), a natural plant flavonoid, and its functional interaction with HMGB-1, was investigated in a chondrocyte model of OA. Human chondrocytes were pre-treated with CH, and then subsequently treated with IL-1β to induce the formation of chondrocytes similar to those found in OA joints. Next, the expression level of HMGB-1 was determined by immunofluorescence and western blot analysis. Additionally, inflammatory factor expression was measured by ELISA, and cell apoptosis was analyzed with flow cytometry. To further explore the effects of CH, HMGB-1 expression was silenced following CH treatment with small interfering (si)RNA. The results demonstrated that CH inhibited cell apoptosis, dose-dependently reduced matrix metalloproteinase (MMP) 13, collagenase and IL-6 expression, and increased collagen α-1 (II) chain (COL2A1) expression in human osteoarthritis chondrocytes. These effects of CH were accompanied by decreased HMGB-1 expression. Additionally, the expression of MMP13, collagenase, IL-6 and COL2A1, as well as apoptosis, was significantly reduced by HMGB-1 siRNA. These results demonstrated that HMGB-1 is critical for the protective effect of CH on human osteoarthritis chondrocytes, including cell apoptosis and inflammatory factor inhibition, which suggests that CH may have potential therapeutic effect in treating OA by protecting human osteoarthritis chondrocytes via HMGB1 suppression. [ABSTRACT FROM AUTHOR]

Graph-based unsupervised feature selection has been proven to be effective in dealing with unlabeled and high-dimensional data. However, most existing methods face a number of challenges primarily due to their high computational complexity. In light of the ever-increasing size of data, these approaches tend to be inefficient in dealing with large-scale data sets. We propose a novel approach, called Fast Unsupervised Feature Selection (FUFS), to efficiently tackle this problem. Firstly, an anchor graph is constructed by means of a parameter-free adaptive neighbor assignment strategy. Meanwhile, an approximate nearest neighbor search technique is introduced to speed up the anchor graph construction. The ℓ2,1-norm regularization is then performed to select more valuable features. Experiments on several large-scale data sets demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]