Shanghai University

About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Catalysis. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

Oxidative stress in zebrafish embryos induced by short-term exposure to bisphenol A, nonylphenol, and their mixture.

Abstract: Bisphenol A (BPA) and nonylphenol (NP) are well-known endocrine-disrupting chemicals (EDCs) present in the aquatic environment, but little is known about their oxidative stress effects on fish embryos. In the present study, we examined the oxidative stress indices and antioxidant parameters of zebrafish embryos after a short-term exposure to various concentrations of BPA, NP, and their mixture (BPA-NP) for 4 h postfertilization (hpf) to 168 hpf. Exposure to the chemicals was found to enhance the production of hydroxyl radicals and lipid peroxidation in a concentration-dependent manner. The content of total glutathione (TG), reduced glutathione (GSH), and oxidized glutathione (GSSH), as well as the activity of antioxidant enzymes including catalase, superoxide dismutase, glutathione peroxidase, glutathione reductase, and glutathione-S-transferase were all significantly inhibited after exposure to BPA, NP, and BPA-NP, indicating the occurrence of oxidative stress. Coexposure to BPA-NP resulted in an additive effect on some antioxidant parameters. In addition, the alkaline phosphatase activity was also significantly inhibited after exposure to BPA, NP, and their mixtures. Our results demonstrated that BPA, NP, and BPA-NP in aquatic systems can affect antioxidant responses in zebrafish embryos.

Aqueous Dispersion Polymerization of 2-Methoxyethyl Acrylate for the Synthesis of Biocompatible Nanoparticles Using a Hydrophilic RAFT Polymer and a Redox Initiator

Abstract: Aqueous dispersion polymerization systems mediated by reversible addition–fragmentation chain transfer (RAFT) process have been less studied in comparison with other heterogeneous polymerization systems due to limited number of monomer/polymer pairs that are suitable for such a condition. We report a novel dispersion polymerization system based on 2-methoxyethyl acrylate (MEA) which is highly water-soluble, but its polymer is not. Using a hydrophilic polymer, poly(poly(ethylene glycol) methyl ether methacrylate) (PPEGMA), as the macromolecular chain transfer agent (Macro-CTA), both solution and dispersion polymerization of MEA were studied. Chain extension by MEA from PPEGMA was successfully realized in DMF solution polymerization. In dispersion polymerization of MEA in water, PPEGMA was used as both a RAFT mediating species and a steric stabilizer for the formed nanoparticles. The dispersion polymerization of MEA in water was highly efficient using a redox initiator, potassium persulfate/sodium ascorbate...

ICNet: Information Conversion Network for RGB-D Based Salient Object Detection

Abstract: RGB-D based salient object detection (SOD) methods leverage the depth map as a valuable complementary information for better SOD performance. Previous methods mainly resort to exploit the correlation between RGB image and depth map in three fusion domains: input images, extracted features, and output results. However, these fusion strategies cannot fully capture the complex correlation between the RGB image and depth map. Besides, these methods do not fully explore the cross-modal complementarity and the cross-level continuity of information, and treat information from different sources without discrimination. In this paper, to address these problems, we propose a novel Information Conversion Network (ICNet) for RGB-D based SOD by employing the siamese structure with encoder-decoder architecture. To fuse high-level RGB and depth features in an interactive and adaptive way, we propose a novel Information Conversion Module (ICM), which contains concatenation operations and correlation layers. Furthermore, we design a Cross-modal Depth-weighted Combination (CDC) block to discriminate the cross-modal features from different sources and to enhance RGB features with depth features at each level. Extensive experiments on five commonly tested datasets demonstrate the superiority of our ICNet over 15 state-of-the-art RGB-D based SOD methods, and validate the effectiveness of the proposed ICM and CDC block.

Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

Abstract: The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels.