Publications by authors named "Yong-Hua Wu"

We analyzed, for the first time, the major components and biological properties of the venom of , a wasp from South China. Using HPLC and SDS-PAGE, combined with LC-MS/MS, MALDI-TOF-MS, and NMR data to analyze venom (VBV), we found that VBV contains three proteins (hyaluronidase A, phospholipase A1 (two isoforms), and antigen 5 protein) with allergenic activity, two unreported proteins (proteins 5 and 6), and two active substances with large quantities (mastoparan-like peptide 12a (Vb-MLP 12a), and 5-hydroxytryptamine (5-HT)). In addition, the antimicrobial activity of VBV was determined, and results showed that it had a significant effect against anaerobic bacteria.

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BACKGROUND This study aimed to investigate the factors associated with sarcopenia in elderly residents in three nursing homes in Suzhou City, East China including the association with nutrition and physical exercise. MATERIAL AND METHODS Elderly residents (n=316) from three nursing homes included 112 men and 204 women. The appendicular skeletal muscle index (ASMI), grip strength, and movements were measured to diagnose sarcopenia.

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The side population (SP) phenotype might represent a common molecular feature for a wide variety of stem cells. The aim of this study was to investigate whether monoclonal SP progenitor cells were established from human fetal pancreas. Islet-like cell clusters (ICCs) were isolated from human fetal pancreas.

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Aim: To isolate nestin-positive progenitor cells from human fetal pancreas and to detect their surface markers and their capability of proliferation and differentiation into pancreatic islet endocrine cells in vitro.

Methods: Islet-like cell clusters (ICCs) were isolated from human fetal pancreas by using collagenase digestion. The free-floating ICCs were handpicked and cultured in a new dish.

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We propose a new, to our knowledge, denoising method for lidar signals based on a regression model and a wavelet neural network (WNN) that permits the regression model not only to have a good wavelet approximation property but also to make a neural network that has a self-learning and adaptive capability for increasing the quality of lidar signals. Specifically, we investigate the performance of the WNN for antinoise approximation of lidar signals by simultaneously addressing simulated and real lidar signals. To clarify the antinoise approximation capability of the WNN for lidar signals, we calculate the atmosphere temperature profile with the real signal processed by the WNN.

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