Taishan, China
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PubMed | Chengdu Military General Hospital, Shanghai University, Tai Shan University and The Eighty eighth Military Hospital
Type: Journal Article | Journal: Osteoarthritis and cartilage | Year: 2016

The International Hip Outcome Tool (iHOT-33) is a questionnaire designed for young, active patients with hip disorders. It has proven to be a highly reliable and valid questionnaire. The main purpose of our study was to adapt the iHOT-33 questionnaire into simplified Chinese and to assess its psychometric properties in Chinese patients.The iHOT-33 was cross culturally adapted into Chinese and 138 patients completed the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the EuroQol-5D (EQ-5D), and the Chinese version of the iHOT-33(SC-iHOT-33) pre- or postoperatively within 6 months follow-up. The Cronbachs alpha, intraclass correlation coefficient (ICC), Pearsons correlation coefficient (r), effect size (ES), and standardized response mean (SRM) were calculated to assess the reliability, validity, and responsiveness of the SC-iHOT-33, respectively.Total Cronbachs alpha was 0.965, which represented excellent internal consistency of the SC-iHOT-33. The ICC ranges from 0.866 to 0.929, which shows excellent test-retest reliability. The subscales of SC-iHOT-33 had the highest correlation coefficient (r=0.812) with the physical function subscales of the WOMAC, as well as good correlation between the social/emotional subscale of the SC-iHOT-33 and the EQ-5D (r=0.740, r=0.743). No floor or ceiling effects were found. The ES and SRM values indicated good responsiveness of 2.44 and 2.67, respectively.The SC-iHOT-33 questionnaire is reliable, valid, and responsive for the evaluation of young, Chinese, active patients with hip disorders.

Miao Q.,Fujian Agriculture and forestry University | Miao Q.,University of New Brunswick | Zhong G.,Fujian Agriculture and forestry University | Qin M.,Tai Shan University | And 2 more authors.
Industrial and Engineering Chemistry Research | Year: 2014

Dissolved and colloidal substances (DCS) will be released during mechanical pulping and following bleaching operations. The accumulation of DCS in paper machine systems can bring various negative impacts on papermaking operations. In present study, the influence of alkaline treatment and alkaline peroxide bleaching of aspen chemithermomechanical pulp on released DCS was evaluated based on general DCS properties, lignin content, and carbohydrate and organic extractives compositions. Results showed that both treatments could promote the DCS release by increasing the concentration and cationic demand of DCS samples. However, alkaline peroxide bleaching caused the decrease in turbidity and average particle size. The total amounts of dissolved lignin, carbohydrates, and organic extractives were also increased. The dissolved lignin-related substances and carbohydrates were the predominant components of DCS. In the extractives, alkaline peroxide bleaching mainly resulted in the release of some lignin-degraded substances and a slight degradation of unsaturated fatty acids and their esters. © 2014 American Chemical Society.

Yang J.,Tai Shan University | Yang J.,CAS Institute of Zoology | Liu R.,Nankai University | Song W.,CAS Institute of Zoology | And 3 more authors.
Applied Biochemistry and Biotechnology | Year: 2012

Field contamination with pesticide mixtures of organophosphates (OPs) and organochlorines (OCs) is becoming global issues to be solved urgently. The strategy of utilizing engineered microorganisms that have an ability to simultaneously degrade OPs and OCs has increasingly received great interest. In this work, an OP degradation gene (mpd) and an OC degradation gene (linA) were simultaneously introduced into Escherichia coli by using two compatible plasmids, resulting in strains with both OP degradation and OC degradation capabilities. To overcome the potential substrate uptake limitation, MPH was displayed on the cell surface of Escherichia coli using the N- and C-terminal domains of ice nucleation protein (INPNC) as an anchoring motif. The surface localization of INPNC-MPH was verified by cell fractionation, Western blot, proteinase accessibility, and immunofluorescence microscopy. Furthermore, both LinA and green fluorescent protein (GFP) were functionally co-expressed in the MPH-displaying Escherichia coli. The engineered Escherichia coli degraded OPs as well as OCs rapidly, and it can be easily monitored by GFP fluorescence. © Springer Science+Business Media, LLC 2011.

Ren J.,Yanshan University | Wang Q.,Yanshan University | Wang M.,Tai Shan University | He H.,Yanshan University
Journal of Computational Information Systems | Year: 2014

Sequential pattern mining based on a bitmap can always perform faster calculations, but a large amount of candidate sequences are generated and tested, which leads to a large calculated amount. A new algorithm for effectively mining frequent sequential patterns, called CB-PMFS, is proposed in this paper. It scans the database once to form a compressed bitmap which records all the item positions in each sequence. Frequent items are mined first and candidate k-sequences are generated in pairs from frequent (kC1)-sequences while the bitmap is updated. Frequent sequences in the lists of each level are sorted to avoid unnecessary candidate sequence connections, and the mining process is converted from sequence matching to comparison between position values. CB-PMFS has been evaluated by experiments on both real and synthetic datasets, and the experimental result shows its high efficiency and good scalability. © 2014 by Binary Information Press

Song X.,Yanshan University | Wang M.,Tai Shan University | Zhu X.,University of Science and Technology of China
Journal of Computational Information Systems | Year: 2014

CMAC is a neural network with strong memory capacity and input-output generalization ability. In the study of nonlinear functions, it is faster than other neural networks. And it is widely used in applications. In order to solve the problem of its dramatic increase of weights storage space when input is high-dimensional. This paper presents a CMAC neural network based on non-uniform quantitative structure. It takes clustering algorithm to cluster data samples. The clustering results make the data with similar output assign to a same cluster. These samples are quantized to a same interval. The data with greatly different output is quantized to a single interval This new structure changes the uniform quantitative structure of CMAC. In the premise of ensuring approximation accuracy, the new structure reduces the quantizing levels. The simulation results show that to get the same target error, the new structure is more efficient than the original structure at the use of weights storage space and uses less running time than the original structure. © 2014 by Binary Information Press

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