Beijing Jiaotong University

About: Beijing Jiaotong University is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Computer science & Artificial neural network. The organization has 35880 authors who have published 37902 publications receiving 376048 citations. The organization is also known as: Northern Jiaotong University & BJTU.

Genetics of rheumatoid arthritis contributes to biology and drug discovery

Abstract: A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2, 3, 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci6 and pathway analyses7, 8, 9—as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes—to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

An Electrochemical Avenue to Green-Luminescent Graphene Quantum Dots as Potential Electron-Acceptors for Photovoltaics

Abstract: Graphene, the two-dimensional (2D) single-atom carbon sheet, has attracted tremendous research interest due to its large surface area, high carrier transport mobility, superior mechanical fl exibility and excellent thermal/chemical stability. [ 1 ] In particular, its high transport mobility [ 2 , 3 ] and environmentally friendly nature meet important requirements in the fabrication of optoelectronic devices. Apart from the conducting fi lm [ 4 , 5 ] and transparent anode [ 6 ] developed previously, its high mobility renders it a promising alternative as an electron-accepting material for photovoltaic device applications. However, the easy aggregation and the poor dispersion of 2D graphene sheets in common solvents limit its application in such devices. Although effort has been made to prepare solution-processable functionalized graphenes (SPFGs), [ 7 ] the non-uniform size and shape, on a scale of several hundred nanometers and even micrometers of SPFGs, remain big challenges for the fabrication of highperformance photovoltaic cells with active layer thicknesses of only nanometer scale. To facilitate the application of graphene in nanodevices and to effectively tune the bandgap of graphenes, a promising approach is to convert the 2D graphene sheets into 0D graphene quantum dots (GQDs). Apart from unique electron transportation properties, [ 8 ] new phenomena from GQDs associated with quantum confi nement and edge effects are expected. [ 9 ] QDs are important for various applications in bioimaging, [ 10 ] lasing, [ 11 ]

Medium-Voltage Multilevel Converters—State of the Art, Challenges, and Requirements in Industrial Applications

Abstract: This paper gives an overview of medium-voltage (MV) multilevel converters with a focus on achieving minimum harmonic distortion and high efficiency at low switching frequency operation. Increasing the power rating by minimizing switching frequency while still maintaining reasonable power quality is an important requirement and a persistent challenge for the industry. Existing solutions are discussed and analyzed based on their topologies, limitations, and control techniques. As a preferred option for future research and application, an inverter configuration based on three-level building blocks to generate five-level voltage waveforms is suggested. This paper shows that such an inverter may be operated at a very low switching frequency to achieve minimum on-state and dynamic device losses for highly efficient MV drive applications while maintaining low harmonic distortion.

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Abstract: Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of transportation. However, it is very challenging since the traffic flows usually show high nonlinearities and complex patterns. Most existing traffic flow prediction methods, lacking abilities of modeling the dynamic spatial-temporal correlations of traffic data, thus cannot yield satisfactory prediction results. In this paper, we propose a novel attention based spatial-temporal graph convolutional network (ASTGCN) model to solve traffic flow forecasting problem. ASTGCN mainly consists of three independent components to respectively model three temporal properties of traffic flows, i.e., recent, daily-periodic and weekly-periodic dependencies. More specifically, each component contains two major parts: 1) the spatial-temporal attention mechanism to effectively capture the dynamic spatialtemporal correlations in traffic data; 2) the spatial-temporal convolution which simultaneously employs graph convolutions to capture the spatial patterns and common standard convolutions to describe the temporal features. The output of the three components are weighted fused to generate the final prediction results. Experiments on two real-world datasets from the Caltrans Performance Measurement System (PeMS) demonstrate that the proposed ASTGCN model outperforms the state-of-the-art baselines.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

Abstract: This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.