Review : Purification and Characterization of a Novel Extracellular Thermostable Alkaline Protease from Streptomyces sp. M30

( Yan Xin ),( Zhibin Sun ),( Qiongzhen Chen ),( Jue Wang ),( Yicheng Wang ),( Linfeng Luogong ),( Shuhuan Li ),( Weiliang Dong ),( Zhongli Cui ),( Yan Huang ) 한국미생물 · 생명공학회 2015 Journal of microbiology and biotechnology Vol.25 No.11

A novel alkaline protease from Streptomyces sp. M30, SapHM, was purified by ammonium sulfate precipitation, hydrophobic interaction chromatography, and DEAE-Sepharose chromatography, with a yield of 15.5% and a specific activity of 29,070 U/mg. Tryptic fragments of the purified SapHM were obtained by electrospray ionization quadrupole timeof- flight mass spectrometry. Nucleotide sequence analysis revealed that the gene sapHM contained 1,179 bp, corresponding to 392 amino acids with conserved Asp156, His187, and Ser339 residues of alkaline protease. The first 24 amino acid residues were predicted to be a signal peptide, and the molecular mass of the mature peptide was 37.1 kDa based on amino acid sequences and mass spectrometry. Pure SapHM was optimally active at 80°C in 50 mM glycine-NaOH buffer (pH 9.0), and was broadly stable at 0-50°C and pH 4.0-9.0. The protease relative activity was increased in the presence of Ni2+, Mn2+, and Cu2+ to 112%, 113%, and 147% of control, respectively. Pure SapHM was also activated by dimethylformamide, dimethyl sulfoxide, Tween 80, and urea. The activity of the purified enzyme was completely inhibited by phenylmethylsulfonyl fluoride, indicating that it is a serine-type protease. The Km and Vmax values were estimated to be 35.7 mg/ml, and 5 × 104 U/mg for casein. Substrate specificity analysis showed that SapH was active on casein, bovine serum albumin, and bovine serum fibrin.

Wireless Energy-Harvesting Cognitive Radio with Feature Detectors

( Yan Gao ),( Yunfei Chen ),( Zhibin Xie ),( Guobing Hu ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.10

The performances of two commonly used feature detectors for wireless energy-harvesting cognitive radio systems are compared with the energy detector under energy causality and collision constraints. The optimal sensing duration is obtained by analyzing the effect of the detection threshold on the average throughput and collision probability. Numerical examples show that the covariance detector has the optimal sensing duration depending on an appropriate choice of the detection threshold, but no optimal sensing duration exists for the ratio of average energy to minimum eigenvalue detector.

Characterization of Energy Conversion of Synechococcus sp. PCC7942 under Photoautotrophic Conditions Based on Metabolic Flux and Chlorophyll Fluorescence Analysis

Riming Yan,Zhibin Zhang,Qinggui Zeng,Du Zhu,Ju Chu 한국생물공학회 2011 Biotechnology and Bioprocess Engineering Vol.16 No.3

Energy conversion efficiency of photoautotrophic microalgae plays an important role in the utilization of light energy for cell growth and production of metabolites. To understand the utilization of light energy,Synechococcus sp. PCC7942 was cultivated at different incident light intensities of 15.8, 47.3, and 94.6 μmol/m^2/sec in continuous culture. The influence of light on the carbon and energy metabolism of microalgae was investigated by combining metabolic flux analysis (MFA) and chlorophyll fluorescence analysis (CFA). Results showed that the yields of biomass based on ATP (Y_(ATP)) and total light energy (Y_E) both declined with increasing light, and the maximal values of Y_(ATP) and Y_E were estimated to be 4.73 g/mol-ATP, and 17.10 × 10_(−3) g/kJ respectively, at the examined conditions. The overall efficiency of energy conversion against total absorbed energy changed with the varying irradiances. However, the actual conversion efficiency of total energy based on CFA was almost constant,regardless of the different irradiances used in the present study.

Numerical Simulation of Fatigue Behavior for Cable-stayed Orthotropic Steel Deck Bridges using Mixed-dimensional Coupling Method

Fei Yan,Zhibin Lin,Ying Huang 대한토목학회 2017 KSCE Journal of Civil Engineering Vol.21 No.6

Effective methods and technologies in fatigue behavior and assessment for cable-stayed orthotropic steel deck bridges are critical to ensure their safety and serviceability. In this study, a mixed-dimensional finite element coupling method is used for structural fatigue assessment. A general framework of the Mix-dimensional Coupling (MDC) method is constructed on the basis of a compromise between simplicity and efficiency as compared to conventional sub-modeling or substructure method. Fatigue details and performance at welded joints have been investigated through the MDC method. Efficiency of the MDC method is demonstrated by a comparison with the simplified Bridge-deck-system (BDS) method. Besides the benefits of the saving time, the numerical simulation also indicated that the MDC method can effectively capture the global behavior for better fatigue prediction, that be ignored in the conventional BDS method as expected. Findings suggest that the MDC method is a cost-effective alternative for fatigue behavior and fatigue assessment of large-span orthotropic steel deck bridges.

Stabilization of Linear Continuous Systems with Limited Information

Yuanlong Li,Zhibin Yan,Jian Wang 제어·로봇·시스템학회 2010 International Journal of Control, Automation, and Vol.8 No.3

A relationship between transmitting data rate of communication channel and the state expanding rate of continuous linear system is derived to guarantee that the system is asymptotically stabilizable through networked control.

Functional roles and mechanisms of ginsenosides from Panax ginseng in atherosclerosis

Qianqian Xue,Ningning He,Zhibin Wang,Xiuxiu Fu,Lynn Htet Htet Aung,Yan Liu,Min Li,Jae Youl Cho,Yanyan Yang,Tao Yu 고려인삼학회 2021 Journal of Ginseng Research Vol.45 No.1

Atherosclerosis (AS) is a leading cause of cardiovascular diseases (CVDs) and it results in a high rate of death worldwide, with an increased prevalence with age despite advances in lifestyle management and drug therapy. Atherosclerosis is a chronic progressive inflammatory process, and it mainly presents with lipid accumulation, foam cell proliferation, inflammatory response, atherosclerotic plaque formation and rupture, thrombosis, and vascular calcification. Therefore, there is a great need for reliable therapeutic drugs or remedies to cure or alleviate atherosclerosis and reduce the societal burden. Ginsenosides are natural steroid glycosides and triterpene saponins obtained mainly from the plant ginseng. Several recent studies have reported that ginsenosides have a variety of pharmacological activities against several diseases including inflammation, cancer and cardiovascular diseases. This review focuses on describing the different pharmacological functions and underlying mechanisms of various active ginsenosides (Rb1,-Rd, -F, -Rg1, -Rg2, and -Rg3, and compound K) for atherosclerosis, which could provide useful insights for developing novel and effective anti-cardiovascular drugs.

Deep BBN Learning for Health Assessment toward Decision-Making on Structures under Uncertainties

Hong Pan,Guoqing Gui,Zhibin Lin,Changhui Yan 대한토목학회 2018 KSCE Journal of Civil Engineering Vol.22 No.3

Structural systems are often exposed to harsh environment, while these environmental factors in turn could degrade the system over time. Their health state and structural conditions are key for structural safety control and decision-making management. Although great efforts have been paid on this field, the high level of variability due to noise and other interferences, and the uncertainties associated with data collection, structural performance and in-service operational environments post great challenges in finding information to assist decision making. The machine learning techniques in recent years have been gaining increasing attentions due to their merits capturing information from statistical representation of events and thus enabling making decision. In this study, the deep Bayesian Belief Network Learning (DBBN) was used to extract structural information and probabilistically determine structural conditions. Different to conventional shallow learning that highly relies on the quality of the hand-crafted features, the deep learning is an end-to-end method to encode the information and interpret vast amount of data with minimizing or no features. A case study was conducted to address the methods for structure under viabilities and uncertainties due to operation, damage and noise interferences. Numerical results revealed that the deep learning exhibits considerably enhanced accuracy for structural diagnostics, as compared to the supervised shallow learning. With predetermined training set, the DBBN could accurately determine the structural health state in terms of damage level, which could dramatically help decision making for further structural retrofit or not. Note that the noise interference could contaminate the data representation and in turn increase the difficulty of the data mining, though the deep learning could reduce the impacts, as compared to conventional shallow learning techniques.

Genetic variations in DROSHA and DICER and survival of advanced non-small cell lung cancer: a two-stage study in Chinese population

Shuangshuang Wu,Yun Pan,Songyu Cao,Jiali Xu,Yan Liang,Yan Wang,Lei Chen,Yunyan Wei,Chongqi Sun,Weihong Zhao,Zhibin Hu,Hongxia Ma,Hongbing Shen,Jianqing Wu 한국유전학회 2015 Genes & Genomics Vol.37 No.7

MicroRNAs (miRNA) are a class of small, noncoding RNA molecules involved in carcinogenesis. Genetic variations in miRNA processing genes may affect the biogenesis of miRNAs, and consequently affect miRNAs regulation and development and progression of human cancer. Therefore, we hypothesized that polymorphisms in two main miRNA biosynthesis genes (DROSHA and DICER) may modulate the survival of advanced non-small cell lung cancer (NSCLC) patients in China. We selected 36 common tagging SNPs in DROSHA and DICER and evaluated the associations of these SNPs with survival of advanced NSCLC patients by a two-stage study in Chinese Han population (discovery cohort: 303 patients; replication cohort: 340 patients). Thirty-six SNPs were detected in the discovery cohort and 12 promising SNPs were validated in the replication cohort. The results showed that DROSHA rs3805525 was marginally associated with the survival of NSCLC patients in the replication cohort (dominant model: HR 0.69, 95 % CI 0.46–1.03, P = 0.071), which was in the same direction as that in the discovery cohort. When combing all patients into one group, three SNPs (rs3805525, rs17410035 and rs7719497) in DROSHA showed significantly associations with NSCLC survival (additive model: HR 0.82, 95 % CI 0.68–0.99 for rs3805525; HR 0.79, 95 % CI 0.62–1.00 for rs17410035; HR 0.76, 95 % CI 0.62–0.93 for rs7719497). Additionally, the combined analysis of those three SNPs showed a significant locus-dosage effect between number of favorable alleles and death risk of NSCLC (Trend P = 0.002). Genetic variations in DROSHA might be associated with the survival of advanced NSCLC patients in Chinese population.