AIM:To investigate the differences in cultivable gut bacteria and peroxisome proliferator-activated receptor γ2(PPAR-γ2) gene Pro12Ala variation in obese and normal-weight Chinese people.METHODS:Using culture method...AIM:To investigate the differences in cultivable gut bacteria and peroxisome proliferator-activated receptor γ2(PPAR-γ2) gene Pro12Ala variation in obese and normal-weight Chinese people.METHODS:Using culture methods,the amounts of Escherichia coli,Enterococci,Bacteroides,Lactobacilli,Bif idobacteria and Clostridium perfringens(C.perfringens) in the feces of 52 obese participants [body mass index(BMI):≥ 28 kg/m2] and 52 participants of normalweight(BMI:18.5-24 kg/m2) were obtained.Study participants completed comprehensive questionnaires and underwent clinical laboratory tests.The polymerase chain reaction-restriction fragment length polymorphism(PCR-PFLP) assay was used to analyze PPAR-γ2 gene Pro12Ala variation.RESULTS:The obese group exhibited a lower amount of C.perfringens(6.54 ± 0.65 vs 6.94 ± 0.57,P = 0.001)and Bacteroides(9.81 ± 0.58 vs 10.06 ± 0.39,P = 0.012) than their normal-weight counterparts.No major differences were observed in Pro12Ala genotype distribution between the two groups;however,obese individuals with a Pro/Ala genotype had a signif icantly lower level of Bacteroides(9.45 ± 0.62 vs 9.93 ± 0.51,P = 0.027) than those with a Pro/Pro genotype.In addition,the obese group demonstrated a higher stool frequency(U = 975,P < 0.001) and a looser stool(U = 1062,P = 0.015) than the normal-weight group.CONCLUSION:Our results indicated interactions among cultivable gut flora,host genetic factors and obese phenotype and this might be helpful for obesity prevention.展开更多
By Monte Carlo simulations, the effect of the dispersion of particle size distribution on the spatial density distributions and correlations of a quasi one-dimensional polydisperse granular gas with fractal size distr...By Monte Carlo simulations, the effect of the dispersion of particle size distribution on the spatial density distributions and correlations of a quasi one-dimensional polydisperse granular gas with fractal size distribution is investigated in the same inelasticity. The dispersive degree of the particle size distribution can be measured by a fractal dimension dr, and the smooth particles are constrained to move along a circle of length L, colliding inelastically with each other and thermalized by a viscosity heat bath. When the typical relaxation time τ of the driving Brownian process is longer than the mean collision time To, the system can reach a nonequilibrium steady state. The average energy of the system decays exponentially with time towards a stable asymptotic value, and the energy relaxation time τB to the steady state becomes shorter with increasing values of df. In the steady state, the spatial density distribution becomes more clusterized as df increases, which can be quantitatively characterized by statistical entropy of the system. Furthermore, the spatial correlation functions of density and velocities are found to be a power-law form for small separation distance of particles, and both of the correlations become stronger with the increase of df. Also, tile density clusterization is explained from the correlations.展开更多
Forecasting economic indices on the basis of information extracted from text documents, like newspaper articles is an attractive idea. With the help of text mining techniques, in particular sentiment analysis, we eval...Forecasting economic indices on the basis of information extracted from text documents, like newspaper articles is an attractive idea. With the help of text mining techniques, in particular sentiment analysis, we evaluate the tone of individual New York Times （NYT） articles and compare our results to the Chicago Fed National Activity Index （CFNAI）. In this paper, we present a simple, intuitive framework to derive sentiment scores from text documents In particular articles are tagged based on terms and their connotated sentiment. Subsequently, we forecast the CFNAI movements via support vector machines （SVM） trained on a subset of the observed sentiment scores. We apply our model into two different data sets, the whole NYT articles and the articles categorized as NYT business news. On both data sets, we applied a simple performance measure to evaluate forecasting accuracy of the CFNAI展开更多
基金Supported by Danone Institute China Diet Nutrition Research and Communication grant (2006)
文摘AIM:To investigate the differences in cultivable gut bacteria and peroxisome proliferator-activated receptor γ2(PPAR-γ2) gene Pro12Ala variation in obese and normal-weight Chinese people.METHODS:Using culture methods,the amounts of Escherichia coli,Enterococci,Bacteroides,Lactobacilli,Bif idobacteria and Clostridium perfringens(C.perfringens) in the feces of 52 obese participants [body mass index(BMI):≥ 28 kg/m2] and 52 participants of normalweight(BMI:18.5-24 kg/m2) were obtained.Study participants completed comprehensive questionnaires and underwent clinical laboratory tests.The polymerase chain reaction-restriction fragment length polymorphism(PCR-PFLP) assay was used to analyze PPAR-γ2 gene Pro12Ala variation.RESULTS:The obese group exhibited a lower amount of C.perfringens(6.54 ± 0.65 vs 6.94 ± 0.57,P = 0.001)and Bacteroides(9.81 ± 0.58 vs 10.06 ± 0.39,P = 0.012) than their normal-weight counterparts.No major differences were observed in Pro12Ala genotype distribution between the two groups;however,obese individuals with a Pro/Ala genotype had a signif icantly lower level of Bacteroides(9.45 ± 0.62 vs 9.93 ± 0.51,P = 0.027) than those with a Pro/Pro genotype.In addition,the obese group demonstrated a higher stool frequency(U = 975,P < 0.001) and a looser stool(U = 1062,P = 0.015) than the normal-weight group.CONCLUSION:Our results indicated interactions among cultivable gut flora,host genetic factors and obese phenotype and this might be helpful for obesity prevention.
基金supported by National Natural Science Foundation of China under Grant Nos.10675048 and 1068006the Natural Science Foundation of Xianning College under Grant No.KZ0916
文摘By Monte Carlo simulations, the effect of the dispersion of particle size distribution on the spatial density distributions and correlations of a quasi one-dimensional polydisperse granular gas with fractal size distribution is investigated in the same inelasticity. The dispersive degree of the particle size distribution can be measured by a fractal dimension dr, and the smooth particles are constrained to move along a circle of length L, colliding inelastically with each other and thermalized by a viscosity heat bath. When the typical relaxation time τ of the driving Brownian process is longer than the mean collision time To, the system can reach a nonequilibrium steady state. The average energy of the system decays exponentially with time towards a stable asymptotic value, and the energy relaxation time τB to the steady state becomes shorter with increasing values of df. In the steady state, the spatial density distribution becomes more clusterized as df increases, which can be quantitatively characterized by statistical entropy of the system. Furthermore, the spatial correlation functions of density and velocities are found to be a power-law form for small separation distance of particles, and both of the correlations become stronger with the increase of df. Also, tile density clusterization is explained from the correlations.
文摘Forecasting economic indices on the basis of information extracted from text documents, like newspaper articles is an attractive idea. With the help of text mining techniques, in particular sentiment analysis, we evaluate the tone of individual New York Times （NYT） articles and compare our results to the Chicago Fed National Activity Index （CFNAI）. In this paper, we present a simple, intuitive framework to derive sentiment scores from text documents In particular articles are tagged based on terms and their connotated sentiment. Subsequently, we forecast the CFNAI movements via support vector machines （SVM） trained on a subset of the observed sentiment scores. We apply our model into two different data sets, the whole NYT articles and the articles categorized as NYT business news. On both data sets, we applied a simple performance measure to evaluate forecasting accuracy of the CFNAI
