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Objective: To investigate the gut microbiota differences of obese children compared with the control healthy cohort to result in further understanding of the mechanism of obesity development., Methods: We evaluated the 16S rRNA gene, the enterotypes, and quantity of the gut microbiota among obese children and the control cohort and learned the differences of the gut microbiota during the process of weight reduction in obese children., Results: In the present study, we learned that the gut microbiota composition was significantly different between obese children and the healthy cohort. Next we found that functional changes, including the phosphotransferase system, ATP-binding cassette transporters, flagellar assembly, and bacterial chemotaxis were overrepresented, while glycan biosynthesis and metabolism were underrepresented in case samples. Moreover, we learned that the amount of Bifidobacterium and Lactobacillus increased among the obese children during the process of weight reduction., Conclusion: Our results might enrich the research between gut microbiota and obesity and further provide a clinical basis for therapy for obesity. We recommend that Bifidobacterium and Lactobacillus might be used as indicators of healthy conditions among obese children, as well as a kind of prebiotic and probiotic supplement in the diet to be an auxiliary treatment for obesity.

Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues's method by employing the protein-protein interaction (PPI) data, domain-domain interaction (DDI) data, weighted domain frequency score (DFS), and cancer linker degree (CLD) data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I) using individual algorithm, (II) combining algorithms, and (III) combining the same classification types of algorithms. When compared with Aragues's method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.

For most fluorescent oxygen sensors developed today, their fabrication process is either time-consuming or needs specialized knowledge. In this work, a robust fluorescent oxygen sensor is facilely constructed by dissolving pyrene molecules into CTAB aqueous solution. The as-prepared pyrene@micelle sensors have submicron-sized diameter, and the concentration of utilized pyrene can be reduced as low as 0.8 mM but still can exhibit dominant excimer emission. The excimer fluorescence is sensitive to dissolved oxygen in both intensity and lifetime, and the respective Stern-Volmer plot follows a nonlinear behavior justified by a two-site model. Because of the merits of large Stokes shift (~140 nm), easy fabrication, and robustness, the pyrene@micelle sensors are very attractive for practical determination of oxygen.