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您的检索式：作者名="Haikun HONG"
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| 1 | Enhanced bio-decolorization of1-amino-4-bromoanthraquinone-2-sulfonic acid by Sphingomonas xenophaga with nutrient amendment显示文摘Bacterial decolorization of anthraquinone dye intermediates is a slow process under aerobic conditions. To speed up the process, in the present study, effects of various nutrients on 1-amino-4-bromoanthraquinone-2-sulfonic acid(ABAS) decolorization by Sphingomonas xenophaga QYY were investigated. The results showed that peptone, yeast extract and casamino acid amendments promoted ABAS bio-decolorization. In particular,the addition of peptone and casamino acids could improve the decolorization activity of strain QYY. Further experiments showed that L-proline had a more significant accelerating effect on ABAS decolorization compared with other amino acids. L-Proline not only supported cell growth, but also significantly increased the decolorization activity of strain QYY. Membrane proteins of strain QYY exhibited ABAS decolorization activities in the presence of L-proline or reduced nicotinamide adenine dinucleotide, while this behavior was not observed in the presence of other amino acids. Moreover, the positive correlation between L-proline concentration and the decolorization activity of membrane proteins was observed, indicating that L-proline plays an important role in ABAS decolorization. The above findings provide us not only a novel insight into bacterial ABAS decolorization, but also an L-proline-supplemented bioaugmentation strategy for enhancing ABAS bio-decolorization. | Hong Lu Xiaofan Guan Jing Wang Jiti Zhou Haikun Zhang | 2015 | Journal of Environmental Sciences2015，27，1： | 1 |
| 2 | Learning dynamic dependency network structure with time lag显示文摘Characterizing and understanding the structure and the evolution of networks is an important problem for many different fields. While in the real-world networks, especially the spatial networks, the influence from one node to another tends to vary over both space and time due to the different space distances and propagation speeds between nodes. Thus the time lag plays an essential role in interpreting the temporal causal dependency among nodes and also brings a big challenge in network structure learning.However most of the previous researches aiming to learn the dynamic network structure only treat the time lag as a predefined constant, which may miss important information or include noisy information if the time lag is set too small or too large. In this paper, we propose a dynamic Bayesian model with adaptive lags(DBAL)which simultaneously integrates two usually separate tasks, i.e., learning the dynamic dependency network structure and estimating time lags, within one unified framework. Specifically, we propose a novel weight kernel approach for time series segmenting and sampling via leveraging samples from adjacent segments to avoid the sample scarcity. Besides, an effective Bayesian scheme cooperated with reversible jump Markov chain Monte Carlo(RJMCMC) and expectation propagation(EP) algorithm is proposed for parameter inference. Extensive empirical evaluations are conducted on both synthetic and two real-world datasets,and the results demonstrate that our proposed model is superior to the traditional methods in learning the network structure and the temporal dependency. | Sizhen DU Guojie SONG Haikun HONG Dong LIU | 2018 | Science China(Information Sciences)2018，61，5： | 0 |
| 3 | Meta-Analysis on Efficacy of Vaccination against Staphylococcus aureus and Escherichia coli显示文摘Mastitis is a common disease responsible for the biggest economic loss in the dairy industry.Antibiotic therapy does not provide long-term protection.And residue is a major concern in food safety.Vaccination is an alternative control method with great potential for bovine mastitis.Our study focus on evaluating vaccine efficacy regarding reducing the incidence of clinical and subclinical mastitis.Meta-analysis was used to pool data extracted from previous studies.26 records from 13 studies were examined.A fixed effect model was constructed assigning incidence as the measurement of the outcome.Risk ratio(RR)was the parameter that measured the incidence differences between treated group and control group.Studies and records were categorised based on vaccine antigens.In vaccine against Staphylococcus aureus,RR was 0.76;95%CI(0.65,0.89),while in vaccine against Escherichia coli RR was 0.96;95%CI(0.86,1.08). | Ziyang Fu Haikun Liu Hong Cao Yongqiang Wang | 2020 | Veterinary Science Research2020，2，1： | 0 |
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