The NS5A protein plays a critical role in the replication of HCV and has been the focus of numerous research efforts over the past few years. NS5A inhibitors have shown impressive in vitro potency profiles in HCV replicon assays, making them attractive components for inclusion in all oral combination regimens. Early work in the NS5A arena led to the discovery of our first clinical candidate, MK-4882 [2-((S)-pyrrolidin-2-yl)-5-(2-(4-(5-((S)-pyrrolidin-2-yl)-1H-imidazol-2-yl)phenyl)benzofuran-5-yl)-1H-imidazole]. While preclinical proof-of-concept studies in HCV-infected chimpanzees harboring chronic genotype 1 infections resulted in significant decreases in viral load after both single- and multiple-dose treatments, viral breakthrough proved to be a concern, thus necessitating the development of compounds with increased potency against a number of genotypes and NS5A resistance mutations. Modification of the MK-4882 core scaffold by introduction of a cyclic constraint afforded a series of tetracyclic inhibitors, which showed improved virologic profiles. Herein we describe the research efforts that led to the discovery of MK-8742, a tetracyclic indole-based NS5A inhibitor, which is currently in phase 2b clinical trials as part of an all-oral, interferon-free regimen for the treatment of HCV infection.
A novel quality-related statistical process monitoring method based on global and local partial least-squares projection (QGLPLS) is proposed in this paper. The main idea of the QGLPLS method is to integrate the advantages of locality-preserving projections (LPP) and partial least squares (PLS) and extract meaningful low-dimensional representations of high-dimensional process and quality data. QGLPLS can exploit the underlying geometrical structure that contains both global and local information pertaining to the sampled data, including the process variable and quality variable measurements. It is well-known that the PLS method can find only the global structure information and ignores the local features of data sets and that the LPP method can preserve local features of data sets well without considering the product quality variables. The capacity for the preservation of global and local projections of the proposed method is compared to that of the PLS and LPP methods; the comparison results demonstrate that the QGLPLS method can effectively capture meaningful information hidden in the process and quality data. Next, a unified optimization framework, i.e., global covariance maximum and local graph minimum in the process measurement and quality data space, is constructed, and QGLPLS-based T 2 and squared prediction error statistic control charts are developed for online process monitoring. Finally, two typical chemical processes, the Tennessee Eastman process and the penicillin fermentation process, are used to test the validity and effectiveness of the QGLPLS-based monitoring method. The experimental results show that the obtained process monitoring performances are better than those when using traditional monitoring methods, such as PLS, principal component analysis, LPP, and global–local structure analysis.
Today social media has become the primary source for news. Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Therefore, it is extremely important to detect fake news as early as possible. Recently, deep learning based approaches have shown improved performance in fake news detection. However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. The annotator can automatically assign weak labels for unlabeled news based on users' reports. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detector's prediction performance. The fake news detector aims to identify fake news based on the news content. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.
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