Wuhan University

About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Computer science & Population. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.

Environmental risk factors in inflammatory bowel disease: a population-based case-control study in Asia-Pacific

Abstract: Objective The rising incidence of inflammatory bowel disease in Asia supports the importance of environmental risk factors in disease aetiology. This prospective population-based case-control study in Asia-Pacific examined risk factors prior to patients developing IBD. Design 442 incident cases (186 Crohn9s disease (CD); 256 UC; 374 Asians) diagnosed between 2011 and 2013 from eight countries in Asia and Australia and 940 controls (frequency-matched by sex, age and geographical location; 789 Asians) completed an environmental factor questionnaire at diagnosis. Unconditional logistic regression models were used to estimate adjusted ORs (aOR) and 95% CIs. Results In multivariate model, being breast fed >12 months (aOR 0.10; 95% CI 0.04 to 0.30), antibiotic use (aOR 0.19; 0.07 to 0.52), having dogs (aOR 0.54; 0.35 to 0.83), daily tea consumption (aOR 0.62; 0.43 to 0.91) and daily physical activity (aOR 0.58; 0.35 to 0.96) decreased the odds for CD in Asians. In UC, being breast fed >12 months (aOR 0.16; 0.08 to 0.31), antibiotic use (aOR 0.48; 0.27 to 0.87), daily tea (aOR 0.63; 0.46 to 0.86) or coffee consumption (aOR 0.51; 0.36 to 0.72), presence of hot water tap (aOR 0.65; 0.46 to 0.91) and flush toilet in childhood (aOR 0.71; 0.51 to 0.98) were protective for UC development whereas ex-smoking (aOR 2.02; 1.22 to 3.35) increased the risk of UC. Conclusions This first population-based study of IBD risk factors in Asia-Pacific supports the importance of childhood immunological, hygiene and dietary factors in the development of IBD, suggesting that markers of altered intestinal microbiota may modulate risk of IBD later in life.

Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction

Abstract: In this paper, we propose a method for the dimensionality reduction (DR) of spectral-spatial features in hyperspectral images (HSIs), under the umbrella of multilinear algebra, i.e., the algebra of tensors. The proposed approach is a tensor extension of conventional supervised manifold-learning-based DR. In particular, we define a tensor organization scheme for representing a pixel's spectral-spatial feature and develop tensor discriminative locality alignment (TDLA) for removing redundant information for subsequent classification. The optimal solution of TDLA is obtained by alternately optimizing each mode of the input tensors. The methods are tested on three public real HSI data sets collected by hyperspectral digital imagery collection experiment, reflective optics system imaging spectrometer, and airborne visible/infrared imaging spectrometer. The classification results show significant improvements in classification accuracies while using a small number of features.

A Nonlocal Weighted Joint Sparse Representation Classification Method for Hyperspectral Imagery

Abstract: As a powerful and promising statistical signal modeling technique, sparse representation has been widely used in various image processing and analysis fields. For hyperspectral image classification, previous studies have shown the effectiveness of the sparsity-based classification methods. In this paper, we propose a nonlocal weighted joint sparse representation classification (NLW-JSRC) method to improve the hyperspectral image classification result. In the joint sparsity model (JSM), different weights are utilized for different neighboring pixels around the central test pixel. The weight of one specific neighboring pixel is determined by the structural similarity between the neighboring pixel and the central test pixel, which is referred to as a nonlocal weighting scheme. In this paper, the simultaneous orthogonal matching pursuit technique is used to solve the nonlocal weighted joint sparsity model (NLW-JSM). The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparsity-based algorithms and the classical support vector machine hyperspectral classifier.