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Search Results: 1 - 10 of 27571 matches for " Shunlei Hu "
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Effects of Reticuloendotheliosis Virus Infection on Cytokine Production in SPF Chickens
Mei Xue, Xingming Shi, Yan Zhao, Hongyu Cui, Shunlei Hu, Xianlan Cui, Yunfeng Wang
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0083918
Abstract: Infection with reticuloendotheliosis virus (REV), a gammaretrovirus in the Retroviridae family, can result in immunosuppression and subsequent increased susceptibility to secondary infections. The effects of REV infection on expression of mRNA for cytokine genes in chickens have not been completely elucidated. In this study, using multiplex branched DNA (bDNA)?technology, we identified molecular mediators that participated in the regulation of the immune response during REV infection in chickens. Cytokine and chemokine mRNA expression levels were evaluated in the peripheral blood mononuclear cells (PBMCs). Expression levels of interleukin (IL)-4, IL-10, IL-13 and?tumor necrosis factor (TNF)-α were significantly up-regulated while interferon (IFN)-α, IFN-β, IFN-γ, IL-1β,IL-2, IL-3, IL-15, IL-17F, IL-18 and colony-stimulating factor (CSF)-1 were markedly decreased in PBMCs at all stages of infection. Compared with controls, REV infected chickens showed greater expression levels of IL-8 in PBMCs 21 and 28 days post infection. In addition, REV regulates host immunity as a suppressor of T cell proliferative responses. The results in this study will help us to understand the host immune response to virus pathogens.
Identification of a Conserved B-cell Epitope on Reticuloendotheliosis Virus Envelope Protein by Screening a Phage-displayed Random Peptide Library
Mei Xue, Xingming Shi, Jing Zhang, Yan Zhao, Hongyu Cui, Shunlei Hu, Hongbo Gao, Xianlan Cui, Yun-Feng Wang
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0049842
Abstract: Background The gp90 protein of avian reticuloendotheliosis-associated virus (REV-A) is an important envelope glycoprotein, which is responsible for inducing protective antibody immune responses in animals. B-cell epitopes on the gp90 protein of REV have not been well studied and reported. Methods and Results This study describes the identification of a linear B-cell epitope on the gp90 protein by screening a phage-displayed 12-mer random peptide library with the neutralizing monoclonal antibody (mAb) A9E8 directed against the gp90. The mAb A9E8 recognized phages displaying peptides with the consensus motif SVQYHPL. Amino acid sequence of the motif exactly matched 213SVQYHPL219 of the gp90. Further identification of the displayed B cell epitope was conducted using a set of truncated peptides expressed as GST fusion proteins and the Western blot results indicated that 213SVQYHPL219 was the minimal determinant of the linear B cell epitope recognized by the mAb A9E8. Moreover, an eight amino acid peptide SVQYHPLA was proven to be the minimal unit of the epitope with the maximal binding activity to mAb A9E8. The REV-A-positive chicken serum reacted with the minimal linear epitopes in Western blot, revealing the importance of the eight amino acids of the epitope in antibody-epitope binding activity. Furthermore, we found that the epitope is a common motif shared among REV-A and other members of REV group. Conclusions and Significance We identified 213SVQYHPL219 as a gp90-specific linear B-cell epitope recognized by the neutralizing mAb A9E8. The results in this study may have potential applications in development of diagnostic techniques and epitope-based marker vaccines against REV-A and other viruses of the REV group.
Detection of Infectious Laryngotracheitis Virus by Real-Time PCR in Naturally and Experimentally Infected Chickens
Yan Zhao, Congcong Kong, Xianlan Cui, Hongyu Cui, Xingming Shi, Xiaomin Zhang, Shunlei Hu, Lianwei Hao, Yunfeng Wang
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0067598
Abstract: Infectious laryngotracheitis (ILT) is an acute, highly contagious upper-respiratory infectious disease of chickens. In this study, a real-time PCR method was developed for fast and accurate detection and quantitation of ILTV DNA of chickens experimentally infected with ILTV strain LJS09 and naturally infected chickens. The detection lower limit of the assay was 10 copies of DNA. There were no cross reactions with the DNA and RNA of infectious bursal disease virus, chicken anemia virus, reticuloendotheliosis virus, avian reovirus, Newcastle disease virus, and Marek's disease virus. The real-time PCR was reproducible as the coefficients of variation of reproducibility of the intra-assay and the inter-assay were less than 2%. The real-time PCR was used to detect the levels of the ILTV DNA in the tissues of specific pathogen free (SPF) chickens infected with ILTV at different times post infection. ILTV DNA was detected by real-time PCR in the heart, liver, spleen, lung, kidney, larynx, tongue, thymus, glandular stomach, duodenum, pancreatic gland, small intestine, large intestine, cecum, cecal tonsil, bursa of Fabricius, and brain of chickens in the infection group and the contact-exposure group. The sensitivity, specificity, and reproducibility of the ILTV real-time PCR assay revealed its suitability for detection and quantitation of ILTV in the samples from clinically and experimentally ILTV infected chickens.
On Relationship between Pediatric Shi Ji and Fever  [PDF]
Xiangyu Hu, Lina Hu
Open Journal of Pediatrics (OJPed) , 2015, DOI: 10.4236/ojped.2015.53036
Abstract: Based on the clinical effect of the treatment on 546 Pediatric Shi Ji fever cases, the thesis tries to explore the effectiveness of Traditional Chinese Medicine(TCM) treatment on Pediatric Shi Ji and the relationship between Pediatric Shi Ji and fever. The methodology applied is a retrospective analysis on the clinical curative effect of TCM treatment on Shi Ji fever cases in our hospital from January 2008 to December 2012. And the results show that a total effective rate of 96.3% could be guaranteed through either oral Chinese Medicinal Herbs, Chinese Medicine Enema, Massage Therapy, or navel administration with TCM. The thesis holds that Pediatric Shi Ji may cause fever, which could be cured simply by applying TCM treatment (promoting digestion to eliminate stagnation) with less or no use of antibiotics.
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.
Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem  [PDF]
Wei Hu, James Hu
Natural Science (NS) , 2019, DOI: 10.4236/ns.2019.111003
Abstract: Artificial intelligence has permeated all aspects of our lives today. However, to make AI behave like real AI, the critical bottleneck lies in the speed of computing. Quantum computers employ the peculiar and unique properties of quantum states such as superposition, entanglement, and interference to process information in ways that classical computers cannot. As a new paradigm of computation, quantum computers are capable of performing tasks intractable for classical processors, thus providing a quantum leap in AI research and making the development of real AI a possibility. In this regard, quantum machine learning not only enhances the classical machine learning approach but more importantly it provides an avenue to explore new machine learning models that have no classical counterparts. The qubit-based quantum computers cannot naturally represent the continuous variables commonly used in machine learning, since the measurement outputs of qubit-based circuits are generally discrete. Therefore, a continuous-variable (CV) quantum architecture based on a photonic quantum computing model is selected for our study. In this work, we employ machine learning and optimization to create photonic quantum circuits that can solve the contextual multi-armed bandit problem, a problem in the domain of reinforcement learning, which demonstrates that quantum reinforcement learning algorithms can be learned by a quantum device.
Q Learning with Quantum Neural Networks  [PDF]
Wei Hu, James Hu
Natural Science (NS) , 2019, DOI: 10.4236/ns.2019.111005
Abstract: Applying quantum computing techniques to machine learning has attracted widespread attention recently and quantum machine learning has become a hot research topic. There are three major categories of machine learning: supervised, unsupervised, and reinforcement learning (RL). However, quantum RL has made the least progress when compared to the other two areas. In this study, we implement the well-known RL algorithm Q learning with a quantum neural network and evaluate it in the grid world environment. RL is learning through interactions with the environment, with the aim of discovering a strategy to maximize the expected cumulative rewards. Problems in RL bring in unique challenges to the study with their sequential nature of learning, potentially long delayed reward signals, and large or infinite size of state and action spaces. This study extends our previous work on solving the contextual bandit problem using a quantum neural network, where the reward signals are immediate after each action.
Reinforcement Learning with Deep Quantum Neural Networks  [PDF]
Wei Hu, James Hu
Journal of Quantum Information Science (JQIS) , 2019, DOI: 10.4236/jqis.2019.91001
Abstract: The advantage of quantum computers over classical computers fuels the recent trend of developing machine learning algorithms on quantum computers, which can potentially lead to breakthroughs and new learning models in this area. The aim of our study is to explore deep quantum reinforcement learning (RL) on photonic quantum computers, which can process information stored in the quantum states of light. These quantum computers can naturally represent continuous variables, making them an ideal platform to create quantum versions of neural networks. Using quantum photonic circuits, we implement Q learning and actor-critic algorithms with multilayer quantum neural networks and test them in the grid world environment. Our experiments show that 1) these quantum algorithms can solve the RL problem and 2) compared to one layer, using three layer quantum networks improves the learning of both algorithms in terms of rewards collected. In summary, our findings suggest that having more layers in deep quantum RL can enhance the learning outcome.
Distributional Reinforcement Learning with Quantum Neural Networks  [PDF]
Wei Hu, James Hu
Intelligent Control and Automation (ICA) , 2019, DOI: 10.4236/ica.2019.102004
Abstract:
Traditional reinforcement learning (RL) uses the return, also known as the expected value of cumulative random rewards, for training an agent to learn an optimal policy. However, recent research indicates that learning the distribution over returns has distinct advantages over learning their expected value as seen in different RL tasks. The shift from using the expectation of returns in traditional RL to the distribution over returns in distributional RL has provided new insights into the dynamics of RL. This paper builds on our recent work investigating the quantum approach towards RL. Our work implements the quantile regression (QR) distributional Q learning with a quantum neural network. This quantum network is evaluated in a grid world environment with a different number of quantiles, illustrating its detailed influence on the learning of the algorithm. It is also compared to the standard quantum Q learning in a Markov Decision Process (MDP) chain, which demonstrates that the quantum QR distributional Q learning can explore the environment more efficiently than the standard quantum Q learning. Efficient exploration and balancing of exploitation and exploration are major challenges in RL. Previous work has shown that more informative actions can be taken with a distributional perspective. Our findings suggest another cause for its success: the enhanced performance of distributional RL can be partially attributed to its superior ability to efficiently explore the environment.
Receptor binding specificity and origin of 2009 H1N1 pandemic influenza virus  [PDF]
Wei Hu
Natural Science (NS) , 2011, DOI: 10.4236/ns.2011.33030
Abstract: Recently, a genetic variant of 2009 H1N1 has become the predominant virus circulating in the southern hemisphere, particularly Australia and New Zealand, and in Singapore during the winter of 2010. It was associated with several vaccine breakthroughs and fatal cases. We analyzed three reported mutations D94N, N125D, and V250A in the HA protein of this genetic variant. It appeared that the reason for D94N and V250A to occur in pairs was to maintain the HA binding to human type receptor, so the virus could replicate in humans efficiently. Guided by this interpretation, we discovered a new mutation V30A that could compensate for N125D as V250A did for D94N. We demonstrated that the presence of amino acids 30A and 125N in HA enhanced the binding to human type receptor, while 30V and 125D favored the receptors of avian type and of A/South Carolina/1/18 (H1N1). Furthermore, a combination of 94D, 125D, and 250V made the primary binding preference similar to that of A/South Carolina/1/18 (H1N1) and a combination of 94N, 125D, and 250A resulted in the primary binding affinity for avian type receptor, which clearly differed from that of A/California/07/2009 (H1N1), a strain used in the vaccine for 2009 H1N1. We also re-examined the origin of 2009 H1N1 to refine our knowledge of this important issue. Although the NP, PA, PB1, and PB2 of 2009 H1N1 were closest to North American swine H3N2 in sequence identity, their interaction patterns were closest to swine H1N1 in North America.
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