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Carbon nanofibers (CNFs) suspended epoxy resin nanocomposites and the corresponding polymer nanocomposites are fabricated. The surface of CNFs is introduced a functional amine terminated groups via silanization, which in situ react with epoxy monomers. This in situ reaction favors the CNFs dispersion and improves the interfacial interaction between CNFs and monomers. Effects of particle loading, surface treatment and operating temperatures of rheological tests on the complex viscosity, storage modulus and loss modulus are systematically studied. Unique rheological phenomena ''a decreased viscosity with a better dispersion'' are observed and explained in terms of the improved filler dispersion quality. Meanwhile, significant increase in the tensile property and storage modulus is observed and related to the better dispersion and the introduced strong interfacial interaction as revealed by SEM imaging. Finally, electrical conductivity is investigated and an unusual deficiency of surface treatment to improve the electrical conductivity is explained by an insulating coating layer.

In recent years, microblog systems such as Twitter and Sina Weibo have averaged multimillion active users. On the other hand, the microblog system has become a new means of rumor-spreading platform. In this paper, we investigate the machine-learning-based rumor identification approaches. We observed that feature design and selection has a stronger impact on the rumor identification accuracy than the selection of machine-learning algorithms. Meanwhile, the rumor publishers' behavior may diverge from normal users', and a rumor post may have different responses from a normal post. However, mass behavior on rumor posts has not been explored adequately. Hence, we investigate rumor identification schemes by applying five new features based on users' behaviors, and combine the new features with the existing well-proved effective user behaviorbased features, such as followers' comments and reposting, to predict whether a microblog post is a rumor. Experiment results on real-world data from Sina Weibo demonstrate the efficacy and efficiency of our proposed method and features. From the experiments, we conclude that the rumor detection based on mass behaviors is more effective than the detection based on microblogs' inherent features.

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Talaromyces marneffei causes life-threatening opportunistic infections, mainly in Southeast Asia and South China. T. marneffei mainly infects patients with human immunodeficiency virus (HIV) but also infects individuals without known immunosuppression. Here we investigated the involvement of anti–IFN-γ autoantibodies in severe T. marneffei infections in HIV-negative patients. We enrolled 58 HIV-negative adults with severe T. marneffei infections who were otherwise healthy. We found a high prevalence of neutralizing anti–IFN-γ autoantibodies (94.8%) in this cohort. The presence of anti–IFN-γ autoantibodies was strongly associated with HLA-DRB1*16:02 and -DQB1*05:02 alleles in these patients. We demonstrated that adult-onset acquired immunodeficiency due to autoantibodies against IFN-γ is the major cause of severe T. marneffei infections in HIV-negative patients in regions where this fungus is endemic. The high prevalence of anti–IFN-γ autoantibody–associated HLA class II DRB1*16:02 and DQB1*05:02 alleles may account for severe T. marneffei infections in Southeast Asia. Our findings clarify the pathogenesis of T. marneffei infection and pave the way for developing novel treatments.