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Search Results: 1 - 10 of 27579 matches for " Hu Jinguan "
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Clinical Characteristics of Nephrotoxicity of Caulis AristolochiaeManshuriensis and Related Experimental Research

Hu Jinguan,Long Shaojiang,Gu Jian,

世界科学技术-中医药现代化 , 2003,
Abstract: The nephrotoxicity of Caulis Aristolochiae Manshuriensis has got more and more attention in China and abroad. This article makes the textual research of Caulis Aristolochiae Manshuriensis from the viewpoint of herbalism and summarizes the clinical characteristics of its nephrotoxicity as well as its experimental research carried out in animal models, toxicity mechanisms and pharmacokinetics in recent years.
Testing for Homogeneity of Between-individual Variances and Autocorrelation Coefficients in Longitudinal Nonlinear Models with Random Effects and AR(1) Errors

Lin Jinguan,Wei Bocheng,

数学物理学报(A辑) , 2008,
Abstract: Homogeneity of between-individual variances and/or autocorrelation coefficients is one of standard assumptions in longitudinal analysis. However, this assumption needs to be tested statistically. Zhang \& Weiss$^{15]}$ discussed the tests for heterogeneity of between - and/or within-individual variances in linear models with random effects. Lin \& Wei$^{10]}$ considered the tests for homogeneity of between-individual autocorrelationcoefficients in nonlinear models with AR(1) errors but without random effects. However, for such models, the tests for homogeneity of autocorrelation coefficients between individuals as autocorrelation on all individuals exists, have not been considered. This paper is devoted to the tests for homogeneity of between-individual variances and/or autocorrelation coefficientsin the framework of nonlinear regression models with random effects and AR(1) errors. Several diagnostic tests using score statistic are constructed. The properties of test statistics are nvestigated through Monte Carlo simulations. An real-data and simulated-dat examples are analyzed in Section 5 to illustrate the proposed methodology.
Dose-Finding Based on Bivariate Efficacy-Toxicity Outcome Using Archimedean Copula
Yuxi Tao, Junlin Liu, Zhihui Li, Jinguan Lin, Tao Lu, Fangrong Yan
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0078805
Abstract: In dose-finding clinical study, it is common that multiple endpoints are of interest. For instance, efficacy and toxicity endpoints are both primary in clinical trials. In this article, we propose a joint model for correlated efficacy-toxicity outcome constructed with Archimedean Copula, and extend the continual reassessment method (CRM) to a bivariate trial design in which the optimal dose for phase III is based on both efficacy and toxicity. Specially, considering numerous cases that continuous and discrete outcomes are observed in drug study, we will extend our joint model to mixed correlated outcomes. We demonstrate through simulations that our algorithm based on Archimedean Copula model has excellent operating characteristics.
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
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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