As the potential drug targets, G-protein coupled receptors (GPCRs) and nuclear receptors (NRs) are the focuses in pharmaceutical research. It is of great practical significance to develop an automated and reliable method to facilitate the identification of novel receptors. In this study, a method of fast Fourier transform-based support vector machine was proposed to classify GPCRs and NRs from the hydrophobicity of proteins. The models for all the GPCR families and NR subfamilies were trained and validated using jackknife test and the results thus obtained are quite promising. Meanwhile, the performance of the method was evaluated on GPCR and NR independent datasets with good performance. The good results indicate the applicability of the method. Two web servers implementing the prediction are available at http://chem.scu.edu.cn/blast/Pred-GPCR and http://chem.scu.edu.cn/blast/Pred-NR.

As an important transmembrane protein family in eukaryon, G-protein coupled receptors (GPCRs) play a significant role in cellular signal transduction and are important targets for drug design. However, it is very difficult to resolve their tertiary structure by X-ray crystallography. In this study, we have developed a Delaunay model, which constructs a series of simplexes with latent variables to classify the families of GPCRs and projects unknown sequences to principle component space (PC-space) to predict their topology. Computational results show that, for the classification of GPCRs, the method achieves the accuracy of 91.0 and 87.6% for Class A, more than 80% for the other three classes in differentiating GPCRs from non-GPCRs and 70% for discriminating between four major classes of GPCR, respectively. When recognizing the structure of GPCRs, all the N-terminals of sequences can be determined correctly. The maximum accuracy of predicting transmembrane segments is achieved in the 7th transmembrane segment of Rhodopsin, which is 99.4%, and the average error is 2.1 amino acids, which is the lowest in all of the segments prediction. This method could provide structural information of a novel GPCR as a tool for experiments and other algorithms of structure prediction of GPCRs. Academic users should send their request for the MATLAB program for classifying GPCRs and predicting the topology of them at liml@scu.edu.cn .

In this study, conventional and novel gas sparging regimes have been evaluated for a municipal wastewater granular anaerobic MBR to identify how best to achieve high sustainable fluxes whilst simultaneously conserving energy demand. Using continuous gas sparging in combination with continuous filtration, flux was strongly dependent upon shear rate, which imposed a considerable energy demand. Intermittent gas sparging was subsequently evaluated to reduce energy demand whilst delivering an analogous shear rate. For a flux of 5 L m-2 h-1 , a fouling rate below 1 mbar h-1 was sustained with low gas sparging frequency and gas sparging rates. However, to sustain low fouling rates for fluxes above 10 L m-2 h-1 , a gas sparging frequency of 50 % (i.e. 10 s on/10 s off) and an increase in gas sparging rate is needed, indicating the importance of shear rate and gas sparging frequency. An alternative gas sparging regime was subsequently tested in which filtration was conducted without gas sparging, followed by membrane relaxation for a short period coupled with gas sparging, to create a pseudo dead-end filtration cycle. Fouling characterisation evidenced considerable cake fouling rates of 200-250 mbar h-1 within each filtration cycle. However, long term fouling transient analysis demonstrated low residual fouling resistance, suggesting the cake formed during filtration was almost completely reversible, despite operating at a flux of 15 L m-2 h-1 , which was equivalent or higher than the critical flux of the suspension. It is therefore asserted that by operating filtration in the absence of shear, fouling is less dependent upon the preferential migration of the submicron particle fraction and is instead governed by the compressibility of the heterogeneous cake formed, which enables higher operational fluxes to be achieved. Comparison of energy demand for the three gas sparging regimes to the energy recovered from municipal wastewater AnMBR demonstrated that only by using dead-end filtration can energy neutral wastewater treatment be realised which is the ultimate ambition for the technology.

SUMMARYThis study uses current epidemiological data to evaluate whether phytoestrogen intake is associated with a reduced risk of prostate cancer. We performed a random-effect meta-analysis of published data retrieved from PubMed, Web of Science, ProQuest, and CNKI, which was supplemented by a manual search of relevant references. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). Subgroup analysis and meta-regression were performed to explore the source of heterogeneity. Sensitivity analysis was evaluated to assess the stability of the results. Egger's test and funnel plots were used to detect the existence of publication bias. We retrieved 507 papers, and 29 studies were ultimately confirmed as eligible. The meta-analysis showed that phytoestrogen intake was significantly associated with a reduced risk of prostate cancer, with an odds ratio (OR) of 0.77 (95% CI 0.66-0.88; I 2 = 77.6%). The food/nutritional sources that were significantly associated with a reduced risk of prostate cancer included soy and soy products, tofu, legumes, daidzein, and genistein. Subgroup analysis indicated that the associations were significant among Asians and Caucasians, but not among Africans. Meta-regression revealed that the pooled OR increased with the number of cases in the studies. The results might be affected by publication bias based on the Eggers' test (p = 0.011) and the asymmetry of the funnel plot. Phytoestrogen intake may reduce the risk of prostate cancer in Asians and Caucasians. Regular intake of food that is rich in phytoestrogens, such as soy/soy products or legumes, should be recommended.

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