The Chinese University of Hong Kong

About: The Chinese University of Hong Kong is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & Computer science. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.

Vascular Endothelial Cell Biology: An Update.

Abstract: The vascular endothelium, a monolayer of endothelial cells (EC), constitutes the inner cellular lining of arteries, veins and capillaries and therefore is in direct contact with the components and cells of blood. The endothelium is not only a mere barrier between blood and tissues but also an endocrine organ. It actively controls the degree of vascular relaxation and constriction, and the extravasation of solutes, fluid, macromolecules and hormones, as well as that of platelets and blood cells. Through control of vascular tone, EC regulate the regional blood flow. They also direct inflammatory cells to foreign materials, areas in need of repair or defense against infections. In addition, EC are important in controlling blood fluidity, platelet adhesion and aggregation, leukocyte activation, adhesion, and transmigration. They also tightly keep the balance between coagulation and fibrinolysis and play a major role in the regulation of immune responses, inflammation and angiogenesis. To fulfill these different tasks, EC are heterogeneous and perform distinctly in the various organs and along the vascular tree. Important morphological, physiological and phenotypic differences between EC in the different parts of the arterial tree as well as between arteries and veins optimally support their specified functions in these vascular areas. This review updates the current knowledge about the morphology and function of endothelial cells, particularly their differences in different localizations around the body paying attention specifically to their different responses to physical, biochemical and environmental stimuli considering the different origins of the EC.

Cumulative Attribute Space for Age and Crowd Density Estimation

Abstract: A number of computer vision problems such as human age estimation, crowd density estimation and body/face pose (view angle) estimation can be formulated as a regression problem by learning a mapping function between a high dimensional vector-formed feature input and a scalar-valued output. Such a learning problem is made difficult due to sparse and imbalanced training data and large feature variations caused by both uncertain viewing conditions and intrinsic ambiguities between observable visual features and the scalar values to be estimated. Encouraged by the recent success in using attributes for solving classification problems with sparse training data, this paper introduces a novel cumulative attribute concept for learning a regression model when only sparse and imbalanced data are available. More precisely, low-level visual features extracted from sparse and imbalanced image samples are mapped onto a cumulative attribute space where each dimension has clearly defined semantic interpretation (a label) that captures how the scalar output value (e.g. age, people count) changes continuously and cumulatively. Extensive experiments show that our cumulative attribute framework gains notable advantage on accuracy for both age estimation and crowd counting when compared against conventional regression models, especially when the labelled training data is sparse with imbalanced sampling.

The prevalence of EGFR mutation in patients with non-small cell lung cancer: a systematic review and meta-analysis

Abstract: // Yue-Lun Zhang 1, 2 , Jin-Qiu Yuan 1, 2 , Kai-Feng Wang 3 , Xiao-Hong Fu 1, 2 , Xiao-Ran Han 1, 2 , Diane Threapleton 1 , Zu-Yao Yang 1, 2 , Chen Mao 1, 2 , Jin-Ling Tang 1, 2 1 Division of Epidemiology, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China 2 Shenzhen Municipal Key Laboratory for Health Risk Analysis, Shenzhen Research Institute of The Chinese University of Hong Kong, Shenzhen, Guangdong Province, China 3 Division of Epidemiology, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, Guangdong Province, China Correspondence to: Chen Mao, email: maochen@cuhk.edu.hk Jin-Ling Tang, email: jltang@cuhk.edu.hk Keywords: non-small cell lung cancer, epidermal growth factor receptor, prevalence, systematic review, meta-analysis Received: May 17, 2016 Accepted: September 25, 2016 Published: October 12, 2016 ABSTRACT Objectives: Estimate the epidermal growth factor receptor ( EGFR ) mutation prevalence in all non-small cell lung cancer (NSCLC) patients and patient subgroups. Results: A total of 456 studies were included, reporting 30,466 patients with EGFR mutation among 115,815 NSCLC patients. The overall pooled prevalence for EGFR mutations was 32.3% (95% CI 30.9% to 33.7%), ranging from 38.4% (95% CI: 36.5% to 40.3%) in China to 14.1% (95% CI: 12.7% to 15.5%) in Europe. The pooled prevalence of EGFR mutation was higher in females (females vs. males: 43.7% vs. 24.0%; OR: 2.7, 95% CI: 2.5 to 2.9), non-smokers (non-smokers vs. past or current smokers: 49.3% vs. 21.5%; OR: 3.7, 95% CI: 3.4 to 4.0), and patients with adenocarcinoma (adenocarcinoma vs. non-adenocarcinoma: 38.0% vs. 11.7%; OR: 4.1, 95% CI: 3.6 to 4.8). Materials and Methods: PubMed, EMBASE, and the Cochrane Library were searched to June 2013. Eligible studies reported EGFR mutation prevalence and the association with at least one of the following factors: gender, smoking status and histology. Random-effects models were used to pool EGFR mutation prevalence data. Conclusion: This study provides the exact prevalence of EGFR mutations in different countries and NSCLC patient subgroups.

BOOST: A Fast Approach to Detecting Gene-Gene Interactions in Genome-wide Case-Control Studies

Abstract: Gene-gene interactions have long been recognized to be fundamentally important for understanding genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST). For the discovery of unknown gene-gene interactions that underlie complex diseases, BOOST allows examination of all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hr to completely evaluate all pairs of roughly 360,000 SNPs on a standard 3.0 GHz desktop with 4G memory running the Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, although both data sets share a very similar hit region in the WTCCC report. BOOST has also identified some disease-associated interactions between genes in the major histocompatibility complex region in the type 1 diabetes data set. We believe that our method can serve as a computationally and statistically useful tool in the coming era of large-scale interaction mapping in genome-wide case-control studies.

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

Abstract: We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds. Our proposed method deeply integrates both 3D voxel Convolutional Neural Network (CNN) and PointNet-based set abstraction to learn more discriminative point cloud features. It takes advantages of efficient learning and high-quality proposals of the 3D voxel CNN and the flexible receptive fields of the PointNet-based networks. Specifically, the proposed framework summarizes the 3D scene with a 3D voxel CNN into a small set of keypoints via a novel voxel set abstraction module to save follow-up computations and also to encode representative scene features. Given the high-quality 3D proposals generated by the voxel CNN, the RoI-grid pooling is proposed to abstract proposal-specific features from the keypoints to the RoI-grid points via keypoint set abstraction with multiple receptive fields. Compared with conventional pooling operations, the RoI-grid feature points encode much richer context information for accurately estimating object confidences and locations. Extensive experiments on both the KITTI dataset and the Waymo Open dataset show that our proposed PV-RCNN surpasses state-of-the-art 3D detection methods with remarkable margins by using only point clouds. Code is available at this https URL.