Yan Wang

Bio: Yan Wang is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Medicine & Materials science. The author has an hindex of 48, co-authored 562 publications receiving 9737 citations. Previous affiliations of Yan Wang include Hunan University of Science and Technology & Johns Hopkins University.

DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection

Abstract: Contour detection serves as the basis of a variety of computer vision tasks such as image segmentation and object recognition. The mainstream works to address this problem focus on designing engineered gradient features. In this work, we show that contour detection accuracy can be improved by instead making the use of the deep features learned from convolutional neural networks (CNNs). While rather than using the networks as a blackbox feature extractor, we customize the training strategy by partitioning contour (positive) data into subclasses and fitting each subclass by different model parameters. A new loss function, named positive-sharing loss, in which each subclass shares the loss for the whole positive class, is proposed to learn the parameters. Compared to the sofmax loss function, the proposed one, introduces an extra regularizer to emphasizes the losses for the positive and negative classes, which facilitates to explore more discriminative features. Our experimental results demonstrate that learned deep features can achieve top performance on Berkeley Segmentation Dataset and Benchmark (BSDS500) and obtain competitive cross dataset generalization result on the NYUD dataset.

Supporting Ultrathin ZnIn2 S4 Nanosheets on Co/N-Doped Graphitic Carbon Nanocages for Efficient Photocatalytic H2 Generation.

Abstract: Ultrathin ZnIn2 S4 nanosheets (NSs) are grown on Co/N-doped graphitic carbon (NGC) nanocages, composed of Co nanoparticles surrounded by few-layered NGC, to obtain hierarchical Co/NGC@ZnIn2 S4 hollow heterostructures for photocatalytic H2 generation with visible light. The photoredox functions of discrete Co, conductive NGC, and ZnIn2 S4 NSs are precisely combined into hierarchical composite cages possessing strongly hybridized shell and ultrathin layered substructures. Such structural and compositional virtues can expedite charge separation and mobility, offer large surface area and abundant reactive sites for water photosplitting. The Co/NGC@ZnIn2 S4 photocatalyst exhibits outstanding H2 evolution activity (e.g., 11270 µmol h-1 g-1 ) and high stability without engaging any cocatalyst.

Enhanced microwave absorption performances of polyaniline/graphene aerogel by covalent bonding

Abstract: In this paper, covalently bonded polyaniline (PANI)/graphene aerogel (GA) was synthesized using hydrothermal and in-situ polymerization techniques. The chemical bonding between PANI and GA and the micromorphological features of the aerogel were investigated employing several characterization methods, which included Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller (BET) analysis, field emission scanning electron microscopy (FESEM), and transmission electron microscopy (TEM). Due to combination of appropriate impedance match, synergistic effects, and hierarchical nanostructures of PANI and GA, the hybrids exhibited obviously improved microwave absorption performances, compared to GA. The PANI/GA showed the strongest reflection loss (RL) of −42.3 dB at 11.2 GHz with a matching thickness of 3 mm, and the corresponding absorption bandwidth (RL

Supramolecular Assembly-Induced Emission Enhancement for Efficient Mercury(II) Detection and Removal

Abstract: New strategies that can simultaneously detect and remove highly toxic environmental pollutants such as heavy metal ions are still in urgent need. Herein, through supramolecular host–guest interactions, a fluorescent supramolecular polymer has been facilely constructed from a newly designed [2]biphenyl-extended pillar[6]arene equipped with two thymine sites as arms (H) and a tetraphenylethylene (TPE)-bridged bis(quaternary ammonium) guest (G) with aggregation-induced emission (AIE) property. Interestingly, supramolecular assembly-induced emission enhancement (SAIEE) could be switched on upon addition of Hg2+ into the above-mentioned supramolecular polymer system to generate spherical-like supramolecular nanoparticles, due to the restriction of intramolecular rotation (RIR)-related AIE feature of G. Significantly, this supramolecular polymer with integrated modalities has been successfully used for real-time detection and removal of toxic heavy metal Hg2+ ions from water with quick response, high selectivit...

Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation

Abstract: We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach [46], which used prediction from the first (coarse) stage to indicate a smaller input region for the second (fine) stage. Despite its effectiveness, this algorithm dealt with two stages individually, which lacked optimizing a global energy function, and limited its ability to incorporate multi-stage visual cues. Missing contextual information led to unsatisfying convergence in iterations, and that the fine stage sometimes produced even lower segmentation accuracy than the coarse stage. This paper presents a Recurrent Saliency Transformation Network. The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration. This brings us two-fold benefits. In training, it allows joint optimization over the deep networks dealing with different input scales. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments in the NIH pancreas segmentation dataset demonstrate the state-of-the-art accuracy, which outperforms the previous best by an average of over 2%. Much higher accuracies are also reported on several small organs in a larger dataset collected by ourselves. In addition, our approach enjoys better convergence properties, making it more efficient and reliable in practice.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Integrative analysis of 111 reference human epigenomes

Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Fluorine in pharmaceutical industry: fluorine-containing drugs introduced to the market in the last decade (2001-2011).

Abstract: Introduced to the Market in the Last Decade (2001−2011) Jiang Wang,† María Sańchez-Rosello,́‡,§ Jose ́ Luis Aceña, Carlos del Pozo,‡ Alexander E. Sorochinsky, Santos Fustero,*,‡,§ Vadim A. Soloshonok,* and Hong Liu*,† †Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China ‡Department of Organic Chemistry, Faculty of Pharmacy, University of Valencia, Av. Vicente Andreś Estelleś, 46100 Burjassot, Valencia, Spain Laboratorio de Molećulas Orgańicas, Centro de Investigacioń Príncipe Felipe, C/ Eduardo Primo Yuf́era 3, 46012 Valencia, Spain Department of Organic Chemistry I, Faculty of Chemistry, University of the Basque Country UPV/EHU, Paseo Manuel Lardizab́al 3, 20018 San Sebastian, Spain IKERBASQUE, Basque Foundation for Science, Alameda Urquijo, 36-5 Plaza Bizkaia, 48011 Bilbao, Spain Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Murmanska Street 1, 02660 Kyiv-94, Ukraine