Shanghai Jiao Tong University

About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.

Inflammatory Cytokine-Induced Intercellular Adhesion Molecule-1 and Vascular Cell Adhesion Molecule-1 in Mesenchymal Stem Cells Are Critical for Immunosuppression

Abstract: Cell–cell adhesion mediated by ICAM-1 and VCAM-1 is critical for T cell activation and leukocyte recruitment to the inflammation site and, therefore, plays an important role in evoking effective immune responses. However, we found that ICAM-1 and VCAM-1 were critical for mesenchymal stem cell (MSC)-mediated immunosuppression. When MSCs were cocultured with T cells in the presence of T cell Ag receptor activation, they significantly upregulated the adhesive capability of T cells due to the increased expression of ICAM-1 and VCAM-1. By comparing the immunosuppressive effect of MSCs toward various subtypes of T cells and the expression of these adhesion molecules, we found that the greater expression of ICAM-1 and VCAM-1 by MSCs, the greater the immunosuppressive capacity that they exhibited. Furthermore, ICAM-1 and VCAM-1 were found to be inducible by the concomitant presence of IFN-γ and inflammatory cytokines (TNF-α or IL-1). Finally, MSC-mediated immunosuppression was significantly reversed in vitro and in vivo when the adhesion molecules were genetically deleted or functionally blocked, which corroborated the importance of cell–cell contact in immunosuppression by MSCs. Taken together, these findings reveal a novel function of adhesion molecules in immunoregulation by MSCs and provide new insights for the clinical studies of antiadhesion therapies in various immune disorders.

Production of offspring from a germline stem cell line derived from neonatal ovaries

Abstract: 6. However, the existence of female germline stem cells (FGSCs) in postnatal mammalian ovaries still remains a controversial issue among reproductive biologists and stem cell researchers 6–10 . We have now established a neonatal mouse FGSC line, with normal karyotype and high telomerase activity, by immunomagnetic isolation and culture for more than 15 months. FGSCs from adult mice were isolated and cultured for more than 6 months. These FGSCs were infected with GFP virus and transplanted into ovaries of infertile mice. Transplanted cells underwent oogenesis and the mice produced offspring that had the GFP transgene. These findings contribute to basic research into oogenesis and stem cell self-renewal and open up new possibilities for use of FGSCs in biotechnology and medicine.

Truly Fluorescent Excitation-Dependent Carbon Dots and Their Applications in Multicolor Cellular Imaging and Multidimensional Sensing

Abstract: Truly fluorescent excitation-dependent carbon dots are prepared, and the relationship between their chemical composition and fluorescent emission is discussed. Furthermore, potential applications of the as-prepared carbon dots to multicolor bio-labeling and multidimodal sensing are demonstrated.

Large-scale supervised multimodal hashing with semantic correlation maximization

Abstract: Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.

DKN: Deep Knowledge-Aware Network for News Recommendation

Abstract: Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. To solve the above problem, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users» diverse interests, we also design an attention module in DKN to dynamically aggregate a user»s history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.