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Search Results: 1 - 10 of 10139 matches for " Jiahua Qian "
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Global Production and Sales Network Planning  [PDF]
Qian Huang, Jiahua Weng, Jiaxin Zhou, Hisashi Onari
Journal of Service Science and Management (JSSM) , 2016, DOI: 10.4236/jssm.2016.94037
Abstract: Due to the large changes in global market demands and production environment, redesigning of production and sales network planning is playing a key role to support the optimization of Global Production Networks (GPN). Making separate decisions regarding production capacity, and sales scale in GPN might decrease profits. Therefore, we propose an integrated method to simultaneously determine those two sub-problems. As the proposed model is a multi-product, multi-plant, multi-production-line, multi-market, multi-sales-price problem, in order to copy with the large search space of solutions, we apply the concept of a genetic algorithm and develop an efficient heuristic for better performance accuracy.
Program Slicing:Its Improved Algorithm and Application in Verification
Lu Qi,Zhang Fubo,Qian Jiahua
计算机科学技术学报 , 1988,
Abstract: Program slicing is a method for automatical program decomposition.This paper presents an improvedslicing algorithm on the basis of static analysis of the control structure of loop statements.The slice obtainedby the new algorithm is guaranteed to be no larger than that obtained by the previous slicing algorithmdeveloped by Mark Weiser.Moreover,the former will be much smaller than the latter for certain kinds ofprograms.In addition,a brief discussion of using slicing in program verification has been given for the sakeof extending the application area of program slicing.
Preliminary Study of Selenium (Se) Toxicity in Human Prostate Carcinoma (PC3) Cells with the Overexpression of Selenocysteine Synthase (SecS) Gene  [PDF]
Tomilowo Abijo, Jiahua Xie
Open Journal of Genetics (OJGen) , 2016, DOI: 10.4236/ojgen.2016.64009
Abstract: Selenium (Se) is a trace element required for normal body function. Its supplementation of human diet at standard optimum amount prevents oxidative damages in cells and could be a viable method in the prevention of diseases related to DNA damage, including cancer, neurodegenerative diseases and aging. While Se anticancer properties have been linked to its ability to remove excess Reactive Oxygen Species (ROS) in cells, the underlying molecular mechanism remains unknown. Recent studies have shown that the removal of ROS alone cannot account for Se anticancer properties. To really comprehend the molecular basis of Se anticancer properties, current researches now focus on the metabolism of Se in the cell, especially Se-containing amino acids. Selenocysteine (Sec) is a novel amino acid and one of the selenium-containing compounds in the cell. It is essential in the maintenance of the integrity of its parent proteins, some of which include enzymes such as Glutathione Peroxidases (GPXs) and Thioredoxin Reductases (TrXs). We propose in this study that the overproduction of Sec via the overexpression of Selenocysteine synthase (SecS) gene and Se supplementation induced cell death in Prostate Carcinoma (PC-3) cells. Although the mechanism underlying the cell death induction is unknown, we propose it could be due to the random incorporation of Sec into proteins at high concentration, causing premature protein degradation and cell death. The outcome of this study showed that increasing the concentration of intracellular Se-containing amino acids may provide important clinical implications for the treatment of cancer.
AN OBJECT MODEL FOR INCREMENTAL STATIC SEMANTIC ANALYSIS
增量静态语义分析的一个对象模型

Xu Zhichen,Qian Jiahua,
徐智晨
,钱家骅

软件学报 , 1994,
Abstract: An object model is provided to model the static semantic of PASCL-like programming language in this paper. Based on this model the authors discussed the process, actions, parallel machanism and implementation alternatives of incremental static semantic analysis. In the last part of this paper they further adapted the model to make it suitable for multiuser, distributed environment. The model can be easily tuned for other high-level programming language.
A Partial Order on Uncertainty and Information
Jiahua Chen
Mathematics , 2011,
Abstract: Information and uncertainty are closely related and extensively studied concepts in a number of scientific disciplines such as communication theory, probability theory, and statistics. Increasing the information arguably reduces the uncertainty on a given random subject. Consider the uncertainty measure as the variance of a random variable. Given the information that its outcome is in an interval, the uncertainty is expected to reduce when the interval shrinks. This proposition is not generally true. In this paper, we provide a necessary and sufficient condition for this proposition when the random variable is absolutely continuous or integer valued. We also give a similar result on Shannon information.
Glutamic Acid Decarboxylase-Derived Epitopes with Specific Domains Expand CD4+CD25+ Regulatory T Cells
Guojiang Chen, Gencheng Han, Jiannan Feng, Jianan Wang, Renxi Wang, Ruonan Xu, Beifen Shen, Jiahua Qian, Yan Li
PLOS ONE , 2009, DOI: 10.1371/journal.pone.0007034
Abstract: Background CD4+CD25+ regulatory T cell (Treg)-based immunotherapy is considered a promising regimen for controlling the progression of autoimmune diabetes. In this study, we tested the hypothesis that the therapeutic effects of Tregs in response to the antigenic epitope stimulation depend on the structural properties of the epitopes used. Methodology/Principal Findings Splenic lymphocytes from nonobese diabetic (NOD) mice were stimulated with different glutamic acid decarboxylase (GAD)-derived epitopes for 7–10 days and the frequency and function of Tregs was analyzed. We found that, although all expanded Tregs showed suppressive functions in vitro, only p524 (GAD524–538)-expanded CD4+CD25+ T cells inhibited diabetes development in the co-transfer models, while p509 (GAD509–528)- or p530 (GAD530–543)-expanded CD4+CD25+ T cells had no such effects. Using computer-guided molecular modeling and docking methods, the differences in structural characteristics of these epitopes and the interaction mode (including binding energy and identified domains in the epitopes) between the above-mentioned epitopes and MHC class II I-Ag7 were analyzed. The theoretical results showed that the epitope p524, which induced protective Tregs, possessed negative surface-electrostatic potential and bound two chains of MHC class II I-Ag7, while the epitopes p509 and p530 which had no such ability exhibited positive surface-electrostatic potential and bound one chain of I-Ag7. Furthermore, p524 bound to I-Ag7 more stably than p509 and p530. Of importance, we hypothesized and subsequently confirmed experimentally that the epitope (GAD570–585, p570), which displayed similar characteristics to p524, was a protective epitope by showing that p570-expanded CD4+CD25+ T cells suppressed the onset of diabetes in NOD mice. Conclusions/Significance These data suggest that molecular modeling-based structural analysis of epitopes may be an instrumental tool for prediction of protective epitopes to expand functional Tregs.
Identification of wounding and topping responsive small RNAs in tobacco (Nicotiana tabacum)
She Tang, Yu Wang, Zefeng Li, Yijie Gui, Bingguang Xiao, Jiahua Xie, Qian-Hao Zhu, Longjiang Fan
BMC Plant Biology , 2012, DOI: 10.1186/1471-2229-12-28
Abstract: To get insight into the role of small RNAs in damage-induced responses, we sequenced and analysed small RNA populations in roots and leaves from wounding or topping treated tobacco plants. In addition to confirmation of expression of 27 known miRNA families, we identified 59 novel tobacco-specific miRNA members of 38 families and a large number of loci generating phased 21- or 24-nt small RNAs (including ta-siRNAs). A number of miRNAs and phased small RNAs were found to be responsive to wounding or topping treatment. Targets of small RNAs were further surveyed by degradome sequencing.The expression changes of miRNAs and phased small RNAs responsive to wounding or topping and identification of defense related targets for these small RNAs suggest that the inducible defense response in tobacco might be controlled by pathways involving small RNAs.Small RNAs are a group of regulatory molecules that fall into two major classes, microRNAs (miRNAs) and short interfering RNAs (siRNAs). They play important roles in biological systems in eukaryotes by suppressing expression of target genes at the transcriptional and/or post-transcriptional level through specific base pairing with their targets [1]. In plants, siRNAs are further classified into trans-acting siRNAs (ta-siRNAs), natural antisense transcript-derived siRNAs (nat-siRNAs), and repeat-associated siRNAs (ra-siRNAs) [2]. In addition, a novel class of bacteria-induced 30- to 40-nt endogenous small RNAs, long siRNAs (lsiRNAs), was identified in Arabidopsis [3].As an important group of small RNAs, miRNA has attracted much attention. A number of studies have been performed to reveal the biogenesis of miRNAs and the mechanisms of miRNA-mediated gene regulation [4-6]. In plants, miRNA derives from primary miRNA transcript (pri-miRNA), which is transcribed by RNA polymerase II. After formation of a stem-loop secondary structure [7,8], pri-miRNA is cleaved twice by DICER-LIKE1 (DCL1), a RNase III enzyme [9]. The first cleavage
English Sentence Recognition Based on HMM and Clustering  [PDF]
Xinguang Li, Jiahua Chen, Zhenjiang Li
American Journal of Computational Mathematics (AJCM) , 2013, DOI: 10.4236/ajcm.2013.31005
Abstract:

For English sentences with a large amount of feature data and complex pronunciation changes contrast to words, there are more problems existing in Hidden Markov Model (HMM), such as the computational complexity of the Viterbi algorithm and mixed Gaussian distribution probability. This article explores the segment-mean algorithm for dimensionality reduction of speech feature parameters, the clustering cross-grouping algorithm and the HMM grouping algorithm, which are proposed for the implementation of the speaker-independent English sentence recognition system based on HMM and clustering. The experimental result shows that, compared with the single HMM, it improves not only the recognition rate but also the recognition speed of the system.

Inference for Multivariate Normal Mixtures
Jiahua Chen,Xianming Tan
Mathematics , 2008,
Abstract: Multivariate normal mixtures provide a flexible model for high-dimensional data. They are widely used in statistical genetics, statistical finance, and other disciplines. Due to the unboundedness of the likelihood function, classical likelihood-based methods, which may have nice practical properties, are inconsistent. In this paper, we recommend a penalized likelihood method for estimating the mixing distribution. We show that the maximum penalized likelihood estimator is strongly consistent when the number of components has a known upper bound. We also explore a convenient EM-algorithm for computing the maximum penalized likelihood estimator. Extensive simulations are conducted to explore the effectiveness and the practical limitations of both the new method and the ratified maximum likelihood estimators. Guidelines are provided based on the simulation results.
Hypothesis test for normal mixture models: The EM approach
Jiahua Chen,Pengfei Li
Statistics , 2009, DOI: 10.1214/08-AOS651
Abstract: Normal mixture distributions are arguably the most important mixture models, and also the most technically challenging. The likelihood function of the normal mixture model is unbounded based on a set of random samples, unless an artificial bound is placed on its component variance parameter. Moreover, the model is not strongly identifiable so it is hard to differentiate between over dispersion caused by the presence of a mixture and that caused by a large variance, and it has infinite Fisher information with respect to mixing proportions. There has been extensive research on finite normal mixture models, but much of it addresses merely consistency of the point estimation or useful practical procedures, and many results require undesirable restrictions on the parameter space. We show that an EM-test for homogeneity is effective at overcoming many challenges in the context of finite normal mixtures. We find that the limiting distribution of the EM-test is a simple function of the $0.5\chi^2_0+0.5\chi^2_1$ and $\chi^2_1$ distributions when the mixing variances are equal but unknown and the $\chi^2_2$ when variances are unequal and unknown. Simulations show that the limiting distributions approximate the finite sample distribution satisfactorily. Two genetic examples are used to illustrate the application of the EM-test.
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