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Search Results: 1 - 10 of 61692 matches for " Yan Hu "
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A Variational Model for Removing Multiple Multiplicative Noises  [PDF]
Xuegang Hu, Yan Hu
Open Journal of Applied Sciences (OJAppS) , 2015, DOI: 10.4236/ojapps.2015.512075
Abstract: The problem of multiplicative noise removal has been widely studied in recent years. Many methods have been used to remove it, but the final results are not very excellent. The total variation regularization method to solve the problem of the noise removal can preserve edge well, but sometimes produces undesirable staircasing effect. In this paper, we propose a variational model to remove multiplicative noise. An alternative algorithm is employed to solve variational model minimization problem. Experimental results show that the proposed model can not only effectively remove Gamma noise, but also Rayleigh noise, as well as the staircasing effect is significantly reduced.
Mixed Channel Service Competition Based on Different Service Pattern in Software-as-a-Service  [PDF]
Haiqing Hu, Jianyuan Yan
iBusiness (IB) , 2012, DOI: 10.4236/ib.2012.41008
Abstract: Software-as-a-Service (SaaS) realizes the transformation from product to Service through put the software into “clouds”. SaaS supply chain is composed of a SaaS ISV (Independent Software Vender) who sells from internet channel and a SaaS dealer. According to different service providers in distribution channel, we sort out three different service patterns, independent service, retailer integrated service and the ISV integrated service. We construct service competition model of three service patterns and study the optimizing decision of the ISV and the dealer. Further more, we explore the impact of customer’s service sensibility, channel preference, service cost of the ISV and the dealer, and customer base on service competition. Finally, from supply chain profit maximization perspective, we compared the three service patterns. We found that the advantages and disadvantages of the service pattern depend on the application scene. The paper extent the dual channel research to software service industry and enrich the theory model of dual channel, the result will guide the supply chain member to decide their service pattern.
The effects of steaming and roasting treatments on lipase activity and nutritional components of “oat rice” (OR): the peeled naked oat (Avena nuda) kernels  [PDF]
Xinzhong Hu, Jinting Yan, Xiaohui Xing
Agricultural Sciences (AS) , 2011, DOI: 10.4236/as.2011.22009
Abstract: Peeled naked oat kernels, named “oat rice” (OR) by Chinese food scientists and processors, are novel oat products in China. This study exam-ined the effects of steaming and roasting treat-ments on the enzyme activities, nutritional con-tents, and flour pasting properties of OR kernels. Results showed that a peeling time of 20 s caused 16.13% β-glucan loss, while a peeling time 25 s caused 34.29% β-glucan loss in the kernels. OR kernels with a 20 s peeling treatment demonstrated significantly higher starch levels and kernel whiteness compared with normal oat kernels (P<0.01). It was also found that normal pressure steaming, autoclaved steaming and infrared roasting treatments could exterminate lipase activities in the OR kernels, and provide the OR kernels with significantly lower final viscosities and setback values than normal kernels (P<0.01).
Quantitative Research on Trade in Value-Added and Emissions Responsibility in Global Value Chain  [PDF]
Yan Xiao, Leyi Hu, Qianying Chen
Technology and Investment (TI) , 2015, DOI: 10.4236/ti.2015.62013
Abstract: The obvious defects of traditional trade statistics methods are shown under the circumstances of international production fragmentation and highly integrated network, and the related problems of greenhouse gas emissions during the value-added process of products are also being focused. How to use qualitative research to measure the residual greenhouse gas in the global industrial chain during the value-added process of products in exporting countries is the key point of this research. This paper summarizes the characteristics of value-added industrial development through sorting out the relevant data of the value-added conditions of global value chain, based on the latest released TiVA database of OECD. Then, the paper selects five time nodes of 12 countries, quantifies the responsibilities of the remains of greenhouse gases in exporting countries, based on value-added part under the trade transaction, and further quantifies the corresponding responsibilities of the emission of greenhouse gases in consumer countries.
Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information
Jing Hu, Changhui Yan
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-297
Abstract: We compiled a set of 686 features that were derived from protein sequence. For each feature, the distance between the wild-type residue and mutant-type residue was computed. Then a greedy approach was used to select the features that were useful for the classification of SAPs. 10 features were selected. Using the selected features, a decision tree method can achieve 82.6% overall accuracy with 0.607 Matthews Correlation Coefficient (MCC) in cross-validation. When tested on an independent set that was not seen by the method during the training and feature selection, the decision tree method achieves 82.6% overall accuracy with 0.604 MCC. We also evaluated the proposed method on all SAPs obtained from the Swiss-Prot, the method achieves 0.42 MCC with 73.2% overall accuracy. This method allows users to make reliable predictions when protein structures are not available. Different from previous studies, in which only a small set of features were arbitrarily chosen and considered, here we used an automated method to systematically discover useful features from a large set of features well-annotated in public databases.The proposed method is a useful tool for the classification of SAPs, especially, when the structure of the protein is not available.It is estimated that around 90% of human genetic variations are differences in single bases of DNA, known as single nucleotide polymorphisms (SNPs) [1]. Among them, non-synonymous single nucleotide polymorphisms (nsSNPs), also known as single amino acid polymorphism (SAPs), that cause amino acid changes in proteins have the potential to affect both protein structure and protein function [2]. Some of the mutations in SAP sites are not associated with any changes in phenotype and are considered functional neutral, but others bringing deleterious effects to protein function and are responsible for many human genetic diseases [3,4]. Recent years have seen an explosion in the number of SAPs in public databases, such as dbSNP [5], HG
A tool for calculating binding-site residues on proteins from PDB structures
Jing Hu, Changhui Yan
BMC Structural Biology , 2009, DOI: 10.1186/1472-6807-9-52
Abstract: In this study, we have developed a tool for calculating binding-site residues on proteins, TCBRP http://yanbioinformatics.cs.usu.edu:8080/ppbindingsubmit webcite. For an input protein, TCBRP can quickly find all binding-site residues on the protein by automatically combining the information obtained from all PDB structures that consist of the protein of interest. Additionally, TCBRP presents the binding-site residues in different categories according to the interaction type. TCBRP also allows researchers to set the definition of binding-site residues.The developed tool is very useful for the research on protein binding site analysis and prediction.Proteins perform various functions through interactions with other molecules, such as DNA, RNA, proteins, carbohydrates, and ligands. To understand the mechanisms of these interactions, many studies have been performed to analyze the properties of binding sites on proteins, such as residue composition, secondary structure, geometric shape, electrostatic potentials, etc [1-10]. To perform such an analysis, researchers must first identify the amino acid residues (referred to as binding-site residues) that are involved in the interactions. In other studies where the goal is to build computational predictors for predicting functional sites on proteins (e.g. DNA-binding sites, RNA-binding sites, protein-protein binding sites), researchers also need to identify binding-site residues on the training and test sets to train and evaluate their predictors [11-17].In most, if not all, of the above-mentioned studies, the binding-site residues are calculated from the 3-dimensional (3D) structures deposited in Protein Data Bank (PDB) [18]. Usually, researchers collected a non-redundant set of PDB structures, and then calculated binding-sites based on the PDB structures. However, one serious problem with this approach is that a protein may have multiple binding sites that interact with different interacting partners, but one PDB structure
BS-KNN: An Effective Algorithm for Predicting Protein Subchloroplast Localization
Jing Hu and Xianghe Yan
Evolutionary Bioinformatics , 2012, DOI: 10.4137/EBO.S8681
Abstract: Chloroplasts are organelles found in cells of green plants and eukaryotic algae that conduct photosynthesis. Knowing a protein’s subchloroplast location provides in-depth insights about the protein’s function and the microenvironment where it interacts with other molecules. In this paper, we present BS-KNN, a bit-score weighted K-nearest neighbor method for predicting proteins’ subchloroplast locations. The method makes predictions based on the bit-score weighted Euclidean distance calculated from the composition of selected pseudo-amino acids. Our method achieved 76.4% overall accuracy in assigning proteins to 4 subchloroplast locations in cross-validation. When tested on an independent set that was not seen by the method during the training and feature selection, the method achieved a consistent overall accuracy of 76.0%. The method was also applied to predict subchloroplast locations of proteins in the chloroplast proteome and validated against proteins in Arabidopsis thaliana. The software and datasets of the proposed method are available at https://edisk.fandm.edu/jing.hu/bsknn/bsknn.html.
HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction
Jing Hu,Changhui Yan
Bioinformatics and Biology Insights , 2008,
Abstract:
HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction
Jing Hu,Changhui Yan
Bioinformatics and Biology Insights , 2008,
Abstract: α-helical transmembrane (TM) proteins play important and diverse functional roles in cells. The ability to predict the topology of these proteins is important for identifying functional sites and inferring function of membrane proteins. This paper presents a Hidden Markov Model (referred to as HMM_RA) that can predict the topology of α-helical transmembrane proteins with improved performance. HMM_RA adopts the same structure as the HMMTOP method, which has five modules: inside loop, inside helix tail, membrane helix, outside helix tail and outside loop. Each module consists of one or multiple states. HMM_RA allows using reduced alphabets to encode protein sequences. Thus, each state of HMM_RA is associated with n emission probabilities, where n is the size of the reduced alphabet set. Direct comparisons using two standard data sets show that HMM_RA consistently outperforms HMMTOP and TMHMM in topology prediction. Specifically, on a high-quality data set of 83 proteins, HMM_RA outperforms HMMTOP by up to 7.6% in topology accuracy and 6.4% in α-helices location accuracy. On the same data set, HMM_RA outperforms TMHMM by up to 6.4% in topology accuracy and 2.9% in location accuracy. Comparison also shows that HMM_RA achieves comparable performance as Phobius, a recently published method.
Inequalities of Aleksandrov body
Yan Hu,Junhua Jiang
Journal of Inequalities and Applications , 2011,
Abstract: A new concept of p-Aleksandrov body is firstly introduced. In this paper, p-Brunn-Minkowski inequality and p-Minkowski inequality on the p-Aleksandrov body are established. Furthermore, some pertinent results concerning the Aleksandrov body and the p-Aleksandrov body are presented. 2000 Mathematics Subject Classification: 52A20 52A40
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