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Bis-tetraazamacrocycles such as the bicyclam AMD3100 (1) are a class of potent and selective anti-HIV-1 agents that inhibit virus replication by binding to the chemokine receptor CXCR4, the coreceptor for entry of X4 viruses. By sequential replacement and/or deletion of the amino groups within the azamacrocyclic ring systems, we have determined the minimum structural features required for potent antiviral activity in this class of compounds. All eight amino groups are not required for activity, the critical amino groups on a per ring basis are nonidentical, and the overall charge at physiological pH can be reduced without compromising potency. This approach led to the identification of several single ring azamacrocyclic analogues such as AMD3465 (3d), 36, and 40, which exhibit EC(50)'s against the cytopathic effects of HIV-1 of 9.0, 1.0, and 4.0 nM, respectively, antiviral potencies that are comparable to 1 (EC(50) against HIV-1 of 4.0 nM). More importantly, however, the key structural elements of 1 required for antiviral activity may facilitate the design of nonmacrocyclic CXCR4 antagonists suitable for HIV treatment via oral administration.

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A novel green bacterial activation method for the synthesis of a lignin derived carbon material with excellent electrochemical performances.

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With the deep integration of cyber physical production systems in the era of Industry 4.0, smart workshop dramatically increases the amount of data collected by smart device. A key factor in achieving smart manufacturing is to use data analysis methods for evaluating the equipment reliability and for supporting the predictive maintenance of equipment. Based on these insights, this paper proposes a deep learning-based approach that uses time series data for equipment reliability analysis. First, a framework of the TensorFlow-enabled deep neural networks (DNN) model for equipment reliability analysis is presented. Secondly, using time series equipment data, an evaluation strategy of equipment reliability based on deep learning is proposed. Finally, the reliability of a cylinder, an important part of the small trolley in automobile assembly line, is evaluated in a case study. Compared with the traditional reliability analysis method such as PCA and HMM, the prediction results show a significant improvement in prediction accuracy. This work contributes to promoting artificial intelligence algorithms for realizing highly efficient manufacturing.

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