Semantic Neighbor Graph Hashing for Multimodal Retrieval.

Hashing methods have been widely used for approximate nearest neighbor search in recent years due to its computational and storage effectiveness. Most existing multimodal hashing methods try to preserve the similarity relationship based on either metric distances or semantic labels in a procrustean way, while ignoring the intra-class and inter-class variations inherent in the metric space. In this paper, we propose a novel multimodal hashing method, termed as semantic neighbor graph hashing (SNGH), which aims to preserve the fine-grained similarity metric based on the semantic graph that is constructed by jointly pursuing the semantic supervision and the local neighborhood structure. Specifically, the semantic graph is constructed to capture the local similarity structure for the image modality and the text modality, respectively. Furthermore, we define a function based on the local similarity in particular to adaptively calculate multi-level similarities by encoding the intra-class and inter-class variations. After obtaining the unified hash codes, the logistic regression with kernel trick is employed to learn view-specific hash functions independently for each modality. Extensive experiments are conducted on four widely used multimodal data sets. The experimental results demonstrate the superiority of the proposed SNGH method compared with the state-of-the-art multimodal hashing methods.

Human Parsing with Contextualized Convolutional Neural Network.

In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, semantic edge context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network. Given an input human image, Co-CNN produces the pixelwise categorization in an end-to-end way. First, the cross-layer context is captured by our basic local-to-global-to-local structure, which hierarchically combines the global semantic information and the local fine details across different convolutional layers. Second, the global image-level label prediction is used as an auxiliary objective in the intermediate layer of the Co-CNN, and its outputs are further used for guiding the feature learning in subsequent convolutional layers to leverage the global image-level context. Third, semantic edge context is further incorporated into Co-CNN, where the high-level semantic boundaries are leveraged to guide pixel-wise labeling. Finally, to further utilize the local super-pixel contexts, the within-super-pixel smoothing and cross-super-pixel neighbourhood voting are formulated as natural sub-components of the Co-CNN to achieve the local label consistency in both training and testing process. Comprehensive evaluations on two public datasets well demonstrate the significant superiority of our Co-CNN over other state-of-the-arts for human parsing. In particular, the F-1 score on the large dataset [1] reaches 81.72 percent by Co-CNN, significantly higher than 62.81 percent and 64.38 percent by the state-of-the-art algorithms, M-CNN [2] and ATR [1], respectively. By utilizing our newly collected large dataset for training, our Co-CNN can achieve 85.36 percent in F-1 score.

Analysis on the current performance and countermeasures on privilege management of medical qualifications in China / 中华医院管理杂志

Beginning with an introduction to overseas practices of privilege management, this paper analyzed the current privilege management of medical qualifications in China. On such basis, the authors introduced their insights and specific recommendations for the design and implementation of such management in China. These include: a platform for privilege management, a classified catalogue for medical qualifications, a procedure for the application, approval, examination and dynamic management of the qualifications, and an informatization platform for privilege management.

Analysis on the current performance and countermeasures on privilege management of medical qualifications in China / 中华医院管理杂志

Beginning with an introduction to overseas practices of privilege management, this paper analyzed the current privilege management of medical qualifications in China. On such basis, the authors introduced their insights and specific recommendations for the design and implementation of such management in China. These include: a platform for privilege management, a classified catalogue for medical qualifications, a procedure for the application, approval, examination and dynamic management of the qualifications, and an informatization platform for privilege management.

Beyond Object Proposals: Random Crop Pooling for Multi-Label Image Recognition.

Learning high-level image representations using object proposals has achieved remarkable success in multi-label image recognition. However, most object proposals provide merely coarse information about the objects, and only carefully selected proposals can be helpful for boosting the performance of multi-label image recognition. In this paper, we propose an object-proposal-free framework for multi-label image recognition: random crop pooling (RCP). Basically, RCP performs stochastic scaling and cropping over images before feeding them to a standard convolutional neural network, which works quite well with a max-pooling operation for recognizing the complex contents of multi-label images. To better fit the multi-label image recognition task, we further develop a new loss function-the dynamic weighted Euclidean loss-for the training of the deep network. Our RCP approach is amazingly simple yet effective. It can achieve significantly better image recognition performance than the approaches using object proposals. Moreover, our adapted network can be easily trained in an end-to-end manner. Extensive experiments are conducted on two representative multi-label image recognition data sets (i.e., PASCAL VOC 2007 and PASCAL VOC 2012), and the results clearly demonstrate the superiority of our approach.

Deep Learning Driven Visual Path Prediction From a Single Image.

Capabilities of inference and prediction are the significant components of visual systems. Visual path prediction is an important and challenging task among them, with the goal to infer the future path of a visual object in a static scene. This task is complicated as it needs high-level semantic understandings of both the scenes and underlying motion patterns in video sequences. In practice, cluttered situations have also raised higher demands on the effectiveness and robustness of models. Motivated by these observations, we propose a deep learning framework, which simultaneously performs deep feature learning for visual representation in conjunction with spatiotemporal context modeling. After that, a unified path-planning scheme is proposed to make accurate path prediction based on the analytic results returned by the deep context models. The highly effective visual representation and deep context models ensure that our framework makes a deep semantic understanding of the scenes and motion patterns, consequently improving the performance on visual path prediction task. In experiments, we extensively evaluate the model's performance by constructing two large benchmark datasets from the adaptation of video tracking datasets. The qualitative and quantitative experimental results show that our approach outperforms the state-of-the-art approaches and owns a better generalization capability.

The clinical and pathological features of Alport syndrome in children / 临床儿科杂志

Objective To analyze the clinical and pathological characteristics of Alport syndrome in children. Methods Clinical and pathological information gathered from 62 patients during March 1989 to August 2012 was retrospectively analyzed. Results Four autosomal recessive Alport syndromes (AR-AS) and 58 X-linked Alport syndromes (XL-AS) were analyzed. Of the XL-AS, 47 were boys and 11 were girls. Most of patients induced by upper respiratory tract infections, and onset with hematuria and proteinuria. There was no signiifcant gender difference in family history, impaired renal tubular proteins, hypertension, im-paired renal function, hearing loss, ocular abnormalities or renal pathological changes under light microscopy. However, extensive lamination and split of glomerular basement membrane (GBM) dense layers were found in 83.0%male and 18.2%female patients (P=0.000) and the rest patients were presented with limited distribution of typical GBM changes. Proteinuria progressed signiif-cantly with age in XL-AS males (r=0.501, P=0.000). Five XL-AS patients developed to end stage renal disease (ESRD) between 11 to 16 years old. Conclusions XL-AS is the main inherited type and severe changes of GBM are common in XL-AS males. Proteinuria increases remarkably with age. The detection of type IV collagen in renal tissue or skin is helpful to diagnose Alport syndrome and conifrm inheritance modes.

Clinical and pathological features of dense deposit disease in children / 临床儿科杂志

10.3969/j.issn.1000-3606.2013.06.020

Effects of tanshinone ⅡA on transforming growth factor beta 1/Smads signaling pathway in cardiac fibroblasts / 中国组织工程研究

BACKGROUND: One of important mechanisms underlying myocardial fibrosis is that transforming growth factor β1(TGF-β1) stimulates the proliferation and differentiation of cardiac fibroblasts via Smads signaling pathway.Previous studies have confirmed that tanshinone ⅡA can effectively inhibit myocardial fibrosis.But whether blockage of TGF-β1/Smads signaling pathway is involved in this process remains unclear. OBJECTIVE: To investigate the effects of tanshinone ⅡA on TGF-β1 signal transduction in rat cardiac fibroblasts. METHODS: Neonatal rat cardiac fibroblasts were harvested by trypsin digestion and differential attachment and treated with 5 μg/L TGF-βI and different concentrations of tanshinone Ⅱ A(106,10-5 and 10-4 mol/L).At 6,12,and 24 hours after TGF-β1 application,fibronectin expression was detected by reverse transcription-polymerase chain reaction and Western blot analysis.At 15,30,60,and 120 minutes after TGF-β1 application,Smads protein expression was determined by Western blot analysis. RESULTS AND CONCLUSION: Fibronectin mRNA and protein expression began to increase at 6 hours after TGF-β1 application and was 1.3 and 1.8 times higher than initial level,respectively(P < 0.01),at 24 hours after TGF-β1 application.Phosphorylated Smad2/3 protein expression began to increase at 15 minutes after TGF-β1 application,peaked at 1 hour,decreased at 2 hours,but it was still 3.9 times higher than initial level(P < 0.01).Tanshinone ⅡA(10-5 and 10-4 mol/L)pretreatment downregulated fibronectin and phosphorylated Smad2/3 expression(P < 0.05 or P < 0.01)in a dose-dependent manner.These findings demonstrate that TGF-β1 induced fibronectin protein and mRNA expression and Smad2/3 protein expression in a time-dependent manner.Tanshinone ⅡA against myocardial fibrosis was likely related to its inhibition of TGF-β1-induced Smad2/3 phosphorylation and blockage of TGF-β1/Smads signaling pathways within cardiac fibroblasts.