Dalian University of Technology

About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.

PDTD: a web-accessible protein database for drug target identification

Abstract: Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Tar get Fis hing Dock ing) http://www.dddc.ac.cn/tarfisdock , which has been used widely by others. Recently, we have constructed a protein target database, P otential D rug T arget D atabase (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation. PDTD is a web-accessible protein database for in silico target identification. It currently contains >1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores. PDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/ .

Structured Siamese Network for Real-Time Visual Tracking

Abstract: Local structures of target objects are essential for robust tracking. However, existing methods based on deep neural networks mostly describe the target appearance from the global view, leading to high sensitivity to non-rigid appearance change and partial occlusion. In this paper, we circumvent this issue by proposing a local structure learning method, which simultaneously considers the local patterns of the target and their structural relationships for more accurate target tracking. To this end, a local pattern detection module is designed to automatically identify discriminative regions of the target objects. The detection results are further refined by a message passing module, which enforces the structural context among local patterns to construct local structures. We show that the message passing module can be formulated as the inference process of a conditional random field (CRF) and implemented by differentiable operations, allowing the entire model to be trained in an end-to-end manner. By considering various combinations of the local structures, our tracker is able to form various types of structure patterns. Target tracking is finally achieved by a matching procedure of the structure patterns between target template and candidates. Extensive evaluations on three benchmark data sets demonstrate that the proposed tracking algorithm performs favorably against state-of-the-art methods while running at a highly efficient speed of 45 fps.

Optimal sensor placement for structural health monitoring based on multiple optimization strategies

Abstract: SUMMARY Careful selection and placement of sensors are the critical issue in the construction and implementation of an effective structural health monitoring system. A hybrid method termed the optimal sensor placement strategy (OSPS) based on multiple optimization methods is proposed in this paper. The initial sensor placement is firstly obtained by the QR factorization. Then, using the minimization of the off-diagonal elements in the modal assurance criterion matrix as a measure of the utility of a sensor configuration, the quantity of the sensors is determined by the forward and backward sequential sensor placement algorithm together. Finally, the locations of the sensor are determined by the dual-structure coding-based generalized genetic algorithm (GGA). Taking the scientific calculation software matlab (MathWorks, Natick, MA, USA) as a platform, an OSPS toolbox, which is working as a black box, is developed based on the command-line compiling and graphical user interface-aided graphical interface design. The characteristic and operation method of the toolbox are introduced in detail, and the scheme selection of the OSP is carried out on the world's tallest TV tower (Guangzhou New TV Tower) based on the developed toolbox. The results indicate that the proposed method is effective and the software package has a friendly interface, plenty of functions, good expansibility and is easy to operate, which can be easily applied in practical engineering. Copyright © 2011 John Wiley & Sons, Ltd.

13.6% Efficient Organic Dye-Sensitized Solar Cells by Minimizing Energy Losses of the Excited State

Abstract: The electron-injection energy losses of dye-sensitized solar cells (DSSCs) are among the fundamental problems hindering their successful breakthrough application. Two triazatruxene (TAT)-based sensitizers, with one containing a flexible Z-type double bond and another a rigid single bond, coded as ZL001 and ZL003, respectively, have been synthesized and applied in DSSCs to probe the energy losses in the process of electron injection. Using time-resolved laser spectroscopic techniques in the kinetic study, ZL003 with the rigid single bond promotes much faster electron injection into the conductive band of TiO2 especially in the locally excited state (hot injection), which leads to higher electron density in TiO2 and a higher Voc. The devices based on ZL003 exhibited a champion power conversion efficiency (PCE) of 13.6% with Voc = 956 mV, Jsc = 20.73 mA cm–2, and FF = 68.5%, which are among the highest recorded results to date on single dye-sensitized DSSCs. An independent certified PCE of 12.4% has been obt...

Catalytically active single-atom niobium in graphitic layers

Abstract: Carbides of groups IV through VI (Ti, V and Cr groups) have long been proposed as substitutes for noble metal-based electrocatalysts in polymer electrolyte fuel cells. However, their catalytic activity has been extremely limited because of the low density and stability of catalytically active sites. Here we report the excellent performance of a niobium-carbon structure for catalysing the cathodic oxygen reduction reaction. A large number of single niobium atoms and ultra small clusters trapped in graphitic layers are directly identified using state-of-the-art aberration-corrected scanning transmission electron microscopy. This structure not only enhances the overall conductivity for accelerating the exchange of ions and electrons, but it suppresses the chemical/thermal coarsening of the active particles. Experimental results coupled with theory calculations reveal that the single niobium atoms incorporated within the graphitic layers produce a redistribution of d-band electrons and become surprisingly active for O2 adsorption and dissociation, and also exhibit high stability.