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Search Results: 1 - 10 of 36802 matches for " Guorui Yan "
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Cone Beam Micro-CT System for Small Animal Imaging and Performance Evaluation
Shouping Zhu,Jie Tian,Guorui Yan,Chenghu Qin,Jinchao Feng
International Journal of Biomedical Imaging , 2009, DOI: 10.1155/2009/960573
Abstract: A prototype cone-beam micro-CT system for small animal imaging has been developed by our group recently, which consists of a microfocus X-ray source, a three-dimensional programmable stage with object holder, and a flat-panel X-ray detector. It has a large field of view (FOV), which can acquire the whole body imaging of a normal-size mouse in a single scan which usually takes about several minutes or tens of minutes. FDK method is adopted for 3D reconstruction with Graphics Processing Unit (GPU) acceleration. In order to reconstruct images with high spatial resolution and low artifacts, raw data preprocessing and geometry calibration are implemented before reconstruction. A method which utilizes a wire phantom to estimate the residual horizontal offset of the detector is proposed, and 1D point spread function is used to assess the performance of geometric calibration quantitatively. System spatial resolution, image uniformity and noise, and low contrast resolution have been studied. Mouse images with and without contrast agent are illuminated in this paper. Experimental results show that the system is suitable for small animal imaging and is adequate to provide high-resolution anatomic information for bioluminescence tomography to build a dual modality system.
A Personalized Recommendation Algorithm Based on Associative Sets  [PDF]
Guorui JIANG, Hai QING, Tiyun HUANG
Journal of Service Science and Management (JSSM) , 2009, DOI: 10.4236/jssm.2009.24048
Abstract: During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users’ real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to solve this problem. It uses frequent item sets to filter out noise data, and makes recommendations according to users’ real preferences, so as to enhance the accuracy of recommending results. Test results have proved the superiority of this algorithm.
Quantum-Inspired Bee Colony Algorithm  [PDF]
Guorui Li, Mu Sun, Panchi Li
Open Journal of Optimization (OJOp) , 2015, DOI: 10.4236/ojop.2015.43007
Abstract: To enhance the performance of the artificial bee colony optimization by integrating the quantum computing model into bee colony optimization, we present a quantum-inspired bee colony optimization algorithm. In our method, the bees are encoded with the qubits described on the Bloch sphere. The classical bee colony algorithm is used to compute the rotation axes and rotation angles. The Pauli matrices are used to construct the rotation matrices. The evolutionary search is achieved by rotating the qubit about the rotation axis to the target qubit on the Bloch sphere. By measuring with the Pauli matrices, the Bloch coordinates of qubit can be obtained, and the optimization solutions can be presented through the solution space transformation. The proposed method can simultaneously adjust two parameters of a qubit and automatically achieve the best match between two adjustment quantities, which may accelerate the optimization process. The experimental results show that the proposed method is obviously superior to the classical one for some benchmark functions.
Noncoding RNA in Oncogenesis: A New Era of Identifying Key Players
Guorui Deng,Guangchao Sui
International Journal of Molecular Sciences , 2013, DOI: 10.3390/ijms140918319
Abstract: New discoveries and accelerating progresses in the field of noncoding RNAs (ncRNAs) continuously challenges our deep-rooted doctrines in biology and sometimes our imagination. A growing body of evidence indicates that ncRNAs are important players in oncogenesis. While a stunning list of ncRNAs has been discovered, only a small portion of them has been examined for their biological activities and very few have been characterized for the molecular mechanisms of their action. To date, ncRNAs have been shown to regulate a wide range of biological processes, including chromatin remodeling, gene transcription, mRNA translation and protein function. Dysregulation of ncRNAs contributes to the pathogenesis of a variety of cancers and aberrant ncRNA expression has a high potential to be prognostic in some cancers. Thus, a new cancer research era has begun to identify novel key players of ncRNAs in oncogenesis. In this review, we will first discuss the function and regulation of miRNAs, especially focusing on the interplay between miRNAs and several key cancer genes, including p53, PTEN and c-Myc. We will then summarize the research of long ncRNAs (lncRNAs) in cancers. In this part, we will discuss the lncRNAs in four categories based on their activities, including regulating gene expression, acting as miRNA decoys, mediating mRNA translation, and modulating protein activities. At the end, we will also discuss recently unraveled activities of circular RNAs (circRNAs).
Hierarchical Latent Semantic Mapping for Automated Topic Generation
Guorui Zhou,Guang Chen
Computer Science , 2015,
Abstract: Much of information sits in an unprecedented amount of text data. Managing allocation of these large scale text data is an important problem for many areas. Topic modeling performs well in this problem. The traditional generative models (PLSA,LDA) are the state-of-the-art approaches in topic modeling and most recent research on topic generation has been focusing on improving or extending these models. However, results of traditional generative models are sensitive to the number of topics K, which must be specified manually. The problem of generating topics from corpus resembles community detection in networks. Many effective algorithms can automatically detect communities from networks without a manually specified number of the communities. Inspired by these algorithms, in this paper, we propose a novel method named Hierarchical Latent Semantic Mapping (HLSM), which automatically generates topics from corpus. HLSM calculates the association between each pair of words in the latent topic space, then constructs a unipartite network of words with this association and hierarchically generates topics from this network. We apply HLSM to several document collections and the experimental comparisons against several state-of-the-art approaches demonstrate the promising performance.
Robust estimation in Capital Asset Pricing Model
Wing-Keung Wong,Guorui Bian
Advances in Decision Sciences , 2000, DOI: 10.1155/s1173912600000043
Abstract: Bian and Dickey (1996) developed a robust Bayesian estimator for the vector of regression coefficients using a Cauchy-type g-prior. This estimator is an adaptive weighted average of the least squares estimator and the prior location, and is of great robustness with respect to at-tailed sample distribution. In this paper, we introduce the robust Bayesian estimator to the estimation of the Capital Asset Pricing Model (CAPM) in which the distribution of the error component is well-known to be flat-tailed. To support our proposal, we apply both the robust Bayesian estimator and the least squares estimator in the simulation of the CAPM and in the analysis of the CAPM for US annual and monthly stock returns. Our simulation results show that the Bayesian estimator is robust and superior to the least squares estimator when the CAPM is contaminated by large normal and/or non-normal disturbances, especially by Cauchy disturbances. In our empirical study, we find that the robust Bayesian estimate is uniformly more efficient than the least squares estimate in terms of the relative efficiency of one-step ahead forecast mean square error, especially for small samples.

Wu Guorui,

生态学报 , 1984,
Abstract: The brown planthopper (BPH) can not hibernate in the paddy rice fields of Zhejiang Province as we observed previously, so its population is increased fundamentally by the immigrants.In order to survey the macropterous adults' immigration,we set up a group of net traps directed to S,SW, W,NW, N,NE,Eand SE respectively on the top of a high mountain, 28°43'N, altitrde 1338 m in Quizhou County ard another group in Zhoushan archipelago, 30°N, 122°20'E, 271m. Observations started from 1977 to 1983 have shewn that...
Efficient video watermarking based on mapping relationship of DCT coefficients

Chen Jie,Feng Guorui,

中国图象图形学报 , 2011,
Abstract: 针对MPEG-2视频压缩标准,提出一种具备快速实现能力的鲁棒性视频水印嵌入方案。利用视频帧分块DCT系数与整体DCT系数间的映射关系,在水印嵌入和提取过程中避免对视频进行完全解码,减小计算量,提高水印算法实时嵌入、实时检测能力。实验结果表明,该方法能抵抗缩小攻击、高斯白噪声攻击、MPEG-2重编码压缩攻击,而且能够快速实现。
Development of a loop-mediated isothermal amplification method to rapidly detect porcine circovirus genotypes 2a and 2b
Qiu Xiaohuo,Li Tian,Zhang Guorui,Cao Jingjing
Virology Journal , 2012, DOI: 10.1186/1743-422x-9-318
Abstract: Background Porcine circovirus type 2 (PCV2), is nowadays associated with a number of diseases known as porcine circovirus-associated diseases (PCVAD), especially postweaning multisystemic wasting syndrome (PMWS). The epidemiological investigation of PCV2 infection was usually conducted by PCR, nested PCR, PCR-RFLP, TaqMan-based assay and nucleotide sequencing. However, there is still no rapid, sensitive and practical method for detecting PCV2 genotypes. As a novel nucleic acid amplification method, the loop-mediated isothermal amplification method (LAMP) has been used to detect a variety of pathogenic microorganisms. Results Herein, a LAMP method is developed to detect the genotypes of PCV2. The diagnostic sensitivity of LAMP is 1 copy/reaction for differentiating genotypes PCV2a and PCV2b. The reaction process was completed at 65°C for 1 hour in a water bath. Cross-reactivity assay shows that this method is specific for PCV2a and PCV2b and no reactive for PCV2c and other swine-origin viruses (i.e. CSFV, PRRSV, BVDV, TGEV and PEDV, etc). Identity between LAMP and nested PCR was 92.3% on 52 field clinical samples. Conclusions LAMP method provides a rapid, sensitive, reliable way to detect PCV2a and PCV2b, and a better means for the large scale investigation of PCV2a and PCV2b infection.
The Arabidopsis pentatricopeptide repeat protein PDM1 is associated with the intergenic sequence of S11-rpoA for rpoA monocistronic RNA cleavage
QianQian Yin,YongLan Cui,GuoRui Zhang,HongDao Zhang,XiaoMeng Wang,ZhongNan Yang
Chinese Science Bulletin , 2012, DOI: 10.1007/s11434-012-5278-9
Abstract: The PDM1 gene encodes a pentatricopeptide repeat protein of the PLS subfamily. It is essential for rpoA polycistronic processing in Arabidopsis. In this study, we performed functional analysis of PDM1 in rpoA monocistronic cleavage and chloroplast development. The pdm1 mutants display an albino lethal phenotype with severe chloroplast development defects. When the construct of PDM1 fused with the GFP gene was introduced into Arabidopsis protoplasts, the GFP signal was exclusively observed in chloroplasts. This shows that PDM1 is localized to chloroplast. In the wild type, the rpoA transcript of about 990 nt is processed. In pdm1, this transcript is absent. However, Western blot showed that the RpoA protein in pdm1 is accumulated at a level approximately 1/3 of the wild type. This suggests that certain other transcripts processed from L23-L2-S19-L22-S3-L16-L14-S8-L36-S11-rpoA polycistronic precursor may be used as templates for protein translation. To determine whether PDM1 can bind to the rpoA pre-mRNA, we generated a transgenic Arabidopsis line with PDM1 fused to FLAG tag that was capable of complementing the pdm1-1 mutant phenotype. RNA immunoprecipitation analysis showed that PDM1 is associated with S11-rpoA intergenic sequence. This indicates that PDM1 may bind to the S11-rpoA intergenic region to perform rpoA processing.
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