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O-Perfluoropyridin-4-yl group was first installed onto cycloketone oximes as a new electrophore, which was proven to be efficient iminyl radical precursors under photocatalytic and thermal conditions. A range of O-perfluoropyridin-4-yl oximes were successfully utilized in C­(sp2)–C­(sp3) bond formations of quinoxalin-2­(1H)-ones and alkenes, providing facile accesses to a range of functionalized alkylnitriles.

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In this paper, we present a computationally simple yet effective image recoloring method based on color harmonization. Our method permits the user to obtain recolored results interactively by rotating a harmonious template after completing color harmonization. Two main improvements are made in this paper. Firstly, we give a new strategy for finding the most harmonious scheme, in terms of finding the template which best matches the hue distribution of the input image. Secondly, in order to achieve spatially coherent harmonization, geodesic distances are used to move hues lying outside the harmonious sectors to inside them. Experiments show that our approach can produce higher-quality visually pleasing recolored images than existing methods. Moreover, our method is simple and easy to implement, and has good runtime performance.

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It is of great clinical significance to establish an accurate intelligent model to diagnose the somatization disorder of community correctional personnel. In this study, a novel machine learning framework is proposed to predict the severity of somatization disorder in community correction personnel. The core of this framework is to adopt the improved bacterial foraging optimization (IBFO) to optimize two key parameters (penalty coefficient and the kernel width) of a kernel extreme learning machine (KELM) and build an IBFO-based KELM (IBFO-KELM) for the diagnosis of somatization disorder patients. The main innovation point of the IBFO-KELM model is the introduction of opposition-based learning strategies in traditional bacteria foraging optimization, which increases the diversity of bacterial species, keeps a uniform distribution of individuals of initial population, and improves the convergence rate of the BFO optimization process as well as the probability of escaping from the local optimal solution. In order to verify the effectiveness of the method proposed in this study, a 10-fold cross-validation method based on data from a symptom self-assessment scale (SCL-90) is used to make comparison among IBFO-KELM, BFO-KELM (model based on the original bacterial foraging optimization model), GA-KELM (model based on genetic algorithm), PSO-KELM (model based on particle swarm optimization algorithm) and Grid-KELM (model based on grid search method). The experimental results show that the proposed IBFO-KELM prediction model has better performance than other methods in terms of classification accuracy, Matthews correlation coefficient (MCC), sensitivity and specificity. It can distinguish very well between severe somatization disorder and mild somatization and assist the psychological doctor with clinical diagnosis.

Introduction Potentilla kleiniana Wight et Arn is widely used as a herbal medicine to treat type 2 diabetes. However, detailed information about its active compounds is lacking. Objective To develop an efficient method for the rapid screening and separation of α‐glucosidase inhibitors from Potentilla kleiniana Wight et Arn. Methodology Potential α‐glucosidase inhibitors from Potentilla kleiniana Wight et Arn were rapidly screened out through ultrafiltration high‐performance liquid chromatography mass spectrometry (HPLC‐MS), and then followed by a target‐guided high‐speed counter‐current chromatography (HSCCC) separation using two‐phase solvent systems composed of n‐hexane/ethyl acetate/methanol/water (1:10:1:10, v/v/v/v and 1:10:5:6, v/v/v/v), and adopting increasing flow‐rate from 1.5 to 3.0 mL/min after 200 min. Their structures were identified by ultraviolet (UV), MS, proton nuclear magnetic resonance (1H‐NMR) and carbon‐13 (13C)‐NMR, and their α‐glucosidase inhibitory activities were assessed by in vitro assay. Results Five α‐glucosidase inhibitors including gallic acid (25.7 mg, 98.2%, 1), brevifolincarboxylic acid (9.86 mg, 95.3%, 2), ethyl evifolincarboxylate (13.26 mg, 97.6%, 3), 3,3′‐di‐O‐methylellagic acid‐4′‐O‐β‐d‐glucopyranoside (16.26 mg, 95.1%, 4), and 3,3′‐di‐O‐methylellagic acid (10.54 mg, 96.8%, 5) were successfully purified from 250 mg n‐butanol extract in a single run. Compounds 1, 2, 4 and 5 exhibited stronger α‐glucosidase inhibitory activities[half maximal inhibition concentration (IC50) values at 173.41 ± 6.35, 323.46 ± 8.08, 44.63 ± 2.50, and 20.73 ± 2.56 μM, respectively] than acarbose (IC50 value at 332.12 ± 5.52 μM, reference compound). Conclusions Notably, compounds 2–5 were reported in the Potentilla kleiniana Wight et Arn for the first time. The results indicated that the proposed method could be applied for the rapid screening and preparative separation of α‐glucosidase inhibitors from a complex matrix.