No abstract

Cyber-physical systems are becoming part of our daily life, and a large number of data are generated at such an unprecedented rate that it becomes larger than ever before in social cyber-physical systems. As a consequence, it is highly desired to process these big data so that meaningful knowledge can be extracted from those vast and diverse data. Based on those large-scale data, using collaborative filtering recommendation methods to recommend some valuable clients or products for those e-commerce websites or users is considered as an effective way. In this work, we present an integrated collaborative filtering recommendation approach that combines item ratings, user ratings, and social trust for making better recommendations. In contrast to previous collaborative filtering recommendation works, integrated collaborative filtering recommendation approach takes full advantage of the correlation between data and takes into consideration the similarity between items, the similarity between users and two kinds of trust among users to select nearest neighbors of both users and items providing the most valuable information for recommendation. On the basis of neighbors selected, integrated collaborative filtering recommendation provides an approach combining two aspects to recommend valuable and suitable items for users. And the concrete process is illustrated as following: (1) the potentially interesting items are obtained by the shopping records of neighbors of a certain user, (2) the potentially interesting items are figured out according to the item neighbors of those items of the user, and (3) determine a few most interesting items combining the two sets of potential items obtained from previous process. A large number of experimental results show that the proposed integrated collaborative filtering recommendation approach can effectively enhance the recommendation performance in terms of mean absolute error and root mean square error. Integrated collaborative filtering recommendation approach could reduce mean absolute error and root mean square error by up to 27.5% and 15.7%, respectively.

As the endoplasmic reticulum paralogue of Hsp90, Grp94 chaperones a small set of client proteins associated with some diseases, including cancer, primary open-angle glaucoma, and inflammatory disorders. Grp94selective inhibition has been a potential therapeutic strategy for these diseases. In this study, inspired by the conclusion that ligand-induced "Phe199 shift" effect is the structural basis of Grp94-selective inhibition, a series of novel Grp94 selective inhibitors incorporating "benzamide" moiety were developed, among which compound 54 manifested the most potent Grp94 inhibitory activity with an IC 50 value of 2 nM and over 1000-fold selectivity to Grp94 against Hsp90α. In a DSSinduced mouse model of ulcerative colitis (UC), compound 54 exhibited significant anti-inflammatory efficacy. This work provides a potent Grp94 selective inhibitor as probe compound for the biological study of Grp94 and represents the first study that confirms the potential therapeutic efficacy of Grp94-selective inhibitors against UC.

To discover effective agents for AD treatment, one promising strategy is to integrate various technology clusters related to anti-AD drugs in terms of the extremely dispersed patent citation network in this area. In this context, governments should pay more attention to encourage basic research, especially to focus on cross-mechanism anti-AD agents. New theories and targets for AD, such as the tau protein hypothesis, are worthy of researcher note. Drugs targeting β-amyloid peptide theory show promise for investors.

Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information and make full use of it. In this paper, we propose a SceneEncoder module to impose a scene-aware guidance to enhance the effect of global information. The module predicts a scene descriptor, which learns to represent the categories of objects existing in the scene and directly guides the point-level semantic segmentation through filtering out categories not belonging to this scene. Additionally, to alleviate segmentation noise in local region, we design a region similarity loss to propagate distinguishing features to their own neighboring points with the same label, leading to the enhancement of the distinguishing ability of point-wise features. We integrate our methods into several prevailing networks and conduct extensive experiments on benchmark datasets ScanNet and ShapeNet. Results show that our methods greatly improve the performance of baselines and achieve state-of-the-art performance.