UNLABELLED: : Identifying chemical probes or seeking scaffolds for a specific biological target is important for protein function studies. Therefore, we create the Annotated Scaffold Database (ASDB), a computer-readable and systematic target-annotated scaffold database, to serve such needs. The scaffolds in ASDB were derived from public databases including ChEMBL, DrugBank and TCMSP, with a scaffold-based classification approach. Each scaffold was assigned with an InChIKey as its unique identifier, energy-minimized 3D conformations, and other calculated properties. A scaffold is also associated with drugs, natural products, drug targets and medical indications. The database can be retrieved through text or structure query tools. ASDB collects 333 601 scaffolds, which are associated with 4368 targets. The scaffolds consist of 3032 scaffolds derived from drugs and 5163 scaffolds derived from natural products. For given scaffolds, scaffold-target networks can be generated from the database to demonstrate the relations of scaffolds and targets. AVAILABILITY AND IMPLEMENTATION: ASDB is freely available at http://www.rcdd.org.cn/asdb/with the major web browsers. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

To facilitate the construction of wiring project and lowering the network costs, Pyxos chip can become the node of a Pyxos network by embedding said chip into the temperature sensor, creating an intelligent building monitoring system. The system consists of Pyxos detecting layer, Lon Works control layer and internet data layer. The system is used to control the electrical and mechanical equipment in the buildings locally and remotely, manage and control power consumption, indoor temperature and other parameters. It is a centralized management and multi-target integrated control system.

Beam search is used to maintain tractability in large search spaces at the expense of completeness and optimality. We study supervised learning of linear ranking functions for controlling beam search. The goal is to learn ranking functions that allow for beam search to perform nearly as well as unconstrained search while gaining computational efficiency. We first study the computational complexity of the learning problem, showing that even for exponentially large search spaces the general consistency problem is in NP. We also identify tractable and intractable subclasses of the learning problem. Next, we analyze the convergence of recently proposed and modified online learning algorithms. We first provide a counter-example to an existing convergence result and then introduce alternative notions of "margin" that do imply convergence. Finally, we study convergence properties for ambiguous training data.