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 & Cancer. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.

Phenylalkylamine Passivation of Organolead Halide Perovskites Enabling High-Efficiency and Air-Stable Photovoltaic Cells

Abstract: Benzylamine is introduced as a surface passivation molecule that improves the moisture-resistance of the perovskites while simultaneously enhancing their electronic properties. Solar cells based on benzylamine-modified formamidinium lead iodide perovskite films exhibit a champion efficiency of 19.2% and an open-circuit voltage of 1.12 V. The modified FAPbI3 films exhibit no degradation after >2800 h air exposure.

From Points to Parts: 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network

Abstract: 3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications. In this paper, we extend our preliminary work PointRCNN to a novel and strong point-cloud-based 3D object detection framework, the part-aware and aggregation neural network (Part- $A^2$ A 2 net). The whole framework consists of the part-aware stage and the part-aggregation stage. First, the part-aware stage for the first time fully utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to simultaneously predict high quality 3D proposals and accurate intra-object part locations. The predicted intra-object part locations within the same proposal are grouped by our new-designed RoI-aware point cloud pooling module, which results in an effective representation to encode the geometry-specific features of each 3D proposal. Then the part-aggregation stage learns to re-score the box and refine the box location by exploring the spatial relationship of the pooled intra-object part locations. Extensive experiments are conducted to demonstrate the performance improvements from each component of our proposed framework. Our Part- $A^2$ A 2 net outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D object detection dataset by utilizing only the LiDAR point cloud data.

Inflammatory processes in renal fibrosis.

Abstract: Many types of kidney injury induce inflammation as a protective response. However, unresolved inflammation promotes progressive renal fibrosis, which can culminate in end-stage renal disease. Kidney inflammation involves cells of the immune system as well as activation of intrinsic renal cells, with the consequent production and release of profibrotic cytokines and growth factors that drive the fibrotic process. In glomerular diseases, the development of glomerular inflammation precedes interstitial fibrosis; although the mechanisms linking these events are poorly understood, an important role for tubular epithelial cells in mediating this link is gaining support. Data have implicated macrophages in promoting both glomerular and interstitial fibrosis, whereas limited evidence suggests that CD4(+) T cells and mast cells are involved in interstitial fibrosis. However, macrophages can also promote renal repair when the cause of renal injury can be resolved, highlighting their plasticity. Understanding the mechanisms by which inflammation drives renal fibrosis is necessary to facilitate the development of therapeutics to halt the progression of chronic kidney disease.

Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection

Abstract: Objective : False positive reduction is one of the most crucial components in an automated pulmonary nodule detection system, which plays an important role in lung cancer diagnosis and early treatment. The objective of this paper is to effectively address the challenges in this task and therefore to accurately discriminate the true nodules from a large number of candidates. Methods : We propose a novel method employing three-dimensional (3-D) convolutional neural networks (CNNs) for false positive reduction in automated pulmonary nodule detection from volumetric computed tomography (CT) scans. Compared with its 2-D counterparts, the 3-D CNNs can encode richer spatial information and extract more representative features via their hierarchical architecture trained with 3-D samples. More importantly, we further propose a simple yet effective strategy to encode multilevel contextual information to meet the challenges coming with the large variations and hard mimics of pulmonary nodules. Results : The proposed framework has been extensively validated in the LUNA16 challenge held in conjunction with ISBI 2016, where we achieved the highest competition performance metric (CPM) score in the false positive reduction track. Conclusion : Experimental results demonstrated the importance and effectiveness of integrating multilevel contextual information into 3-D CNN framework for automated pulmonary nodule detection in volumetric CT data. Significance : While our method is tailored for pulmonary nodule detection, the proposed framework is general and can be easily extended to many other 3-D object detection tasks from volumetric medical images, where the targeting objects have large variations and are accompanied by a number of hard mimics.

Detection of Aberrant p16 Methylation in the Plasma and Serum of Liver Cancer Patients

Abstract: We have studied the feasibility of detecting tumor-associated aberrant p16 methylation in the circulation of patients with hepatocellular carcinoma (HCC). We extracted DNA from the tumor tissues and peripheral blood plasma or serum of 22 HCC patients. p16 methylation was found in 73% (16 of 22) of HCC tissues using methylation-specific PCR. Among the 16 cases with aberrant methylation in the tumor tissues, similar changes were also detected in the plasma/serum samples of 81% (13 of 16) of the cases. No methylated p16 sequences were detected in the peripheral plasma/serum of the six HCC cases without these changes in the tumor, in 38 patients with chronic hepatitis/cirrhosis, or in 10 healthy control subjects. These results suggest that circulating liver tumor DNA may be detected using tumor-associated DNA methylation changes. Because methylation abnormalities have been found in many other genes and tumor types, this approach may have implications for the noninvasive detection of a wide variety of cancers.