Autophagy dysfunction is a common feature in neurodegenerative disorders characterized by accumulation of toxic protein aggregates. Increasing evidence has demonstrated that activation of TFEB (transcription factor EB), a master regulator of autophagy and lysosomal biogenesis, can ameliorate neurotoxicity and rescue neurodegeneration in animal models. Currently known TFEB activators are mainly inhibitors of MTOR (mechanistic target of rapamycin [serine/threonine kinase]), which, as a master regulator of cell growth and metabolism, is involved in a wide range of biological functions. Thus, the identification of TFEB modulators acting without inhibiting the MTOR pathway would be preferred and probably less deleterious to cells. In this study, a synthesized curcumin derivative termed C1 is identified as a novel MTOR-independent activator of TFEB. Compound C1 specifically binds to TFEB at the N terminus and promotes TFEB nuclear translocation without inhibiting MTOR activity. By activating TFEB, C1 enhances autophagy and lysosome biogenesis in vitro and in vivo. Collectively, compound C1 is an orally effective activator of TFEB and is a potential therapeutic agent for the treatment of neurodegenerative diseases.

Searching on remote encrypted data (commonly known as searchable encryption) has become an important issue in secure data outsourcing, since it allows users to outsource encrypted data to an untrusted third party while maintains the capability of keyword search on the data.Searchable encryption can be achieved using the classical method called oblivious RAM, but the resultant schemes are too inefficient to be applied in the real-world scenarios (e.g., cloud computing). Recently, a number of efficient searchable encryption schemes have been proposed under weaker security guarantees. Such schemes, however, still leak statistical information about the users' search pattern.In this paper, we first present two concrete attack methods to show that the search pattern leakage will result in such a situation: an adversary who has some auxiliary knowledge can uncover the underlying keywords of user queries. To address this issue, we then develop a grouping-based construction (GBC) to transform an existing searchable encryption scheme to a new scheme hiding the search pattern. Finally, experiments based on the realworld dataset demonstrate the effectiveness of our attack methods and the feasibility of our construction.

Alignment of multiple multi-relational networks, such as knowledge graphs, is vital for AI applications. Different from the conventional alignment models, we apply the graph convolutional network (GCN) to achieve more robust network embedding for the alignment task. In comparison with existing GCNs which cannot fully utilize multi-relation information, we propose a vectorized relational graph convolutional network (VR-GCN) to learn the embeddings of both graph entities and relations simultaneously for multi-relational networks. The role discrimination and translation property of knowledge graphs are adopted in the convolutional process. Thereafter, AVR-GCN, the alignment framework based on VR-GCN, is developed for multi-relational network alignment tasks. Anchors are used to supervise the objective function which aims at minimizing the distances between anchors, and to generate new cross-network triplets to build a bridge between different knowledge graphs at the level of triplet to improve the performance of alignment. Experiments on real-world datasets show that the proposed solutions outperform the state-of-the-art methods in terms of network embedding, entity alignment, and relation alignment.

The first total synthesis of the indole alkaloid nocardioazine B was accomplished in 10 steps with an overall yield of 11.8%, establishing the absolute stereochemistry of the natural product.