Tongji University

About: Tongji University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Computer science & Population. The organization has 76116 authors who have published 81176 publications receiving 1248911 citations. The organization is also known as: Tongji & Tóngjì Dàxué.

Single-Cell RNA Expression Profiling of ACE2, the Receptor of SARS-CoV-2.

Abstract: A novel coronavirus SARS-CoV-2 was identified in Wuhan, Hubei Province, China in December of 2019. According to WHO report, this new coronavirus has resulted in 76,392 confirmed infections and 2,348 deaths in China by 22 February, 2020, with additional patients being identified in a rapidly growing number internationally. SARS-CoV-2 was reported to share the same receptor, Angiotensin-converting enzyme 2 (ACE2), with SARS-CoV. Here based on the public database and the state-of-the-art single-cell RNA-Seq technique, we analyzed the ACE2 RNA expression profile in the normal human lungs. The result indicates that the ACE2 virus receptor expression is concentrated in a small population of type II alveolar cells (AT2). Surprisingly, we found that this population of ACE2-expressing AT2 also highly expressed many other genes that positively regulating viral entry, reproduction and transmission. This study provides a biological background for the epidemic investigation of the COVID-19, and could be informative for future anti-ACE2 therapeutic strategy development.

Tumor-derived exosomal miR-1247-3p induces cancer-associated fibroblast activation to foster lung metastasis of liver cancer.

Abstract: The communication between tumor-derived elements and stroma in the metastatic niche has a critical role in facilitating cancer metastasis. Yet, the mechanisms tumor cells use to control metastatic niche formation are not fully understood. Here we report that in the lung metastatic niche, high-metastatic hepatocellular carcinoma (HCC) cells exhibit a greater capacity to convert normal fibroblasts to cancer-associated fibroblasts (CAFs) than low-metastatic HCC cells. We show high-metastatic HCC cells secrete exosomal miR-1247-3p that directly targets B4GALT3, leading to activation of β1-integrin–NF-κB signaling in fibroblasts. Activated CAFs further promote cancer progression by secreting pro-inflammatory cytokines, including IL-6 and IL-8. Clinical data show high serum exosomal miR-1247-3p levels correlate with lung metastasis in HCC patients. These results demonstrate intercellular crosstalk between tumor cells and fibroblasts is mediated by tumor-derived exosomes that control lung metastasis of HCC, providing potential targets for prevention and treatment of cancer metastasis.

Learning to Optimize: Training Deep Neural Networks for Interference Management

Abstract: Numerical optimization has played a central role in addressing key signal processing (SP) problems Highly effective methods have been developed for a large variety of SP applications such as communications, radar, filter design, and speech and image analytics, just to name a few However, optimization algorithms often entail considerable complexity, which creates a serious gap between theoretical design/analysis and real-time processing In this paper, we aim at providing a new learning-based perspective to address this challenging issue The key idea is to treat the input and output of an SP algorithm as an unknown nonlinear mapping and use a deep neural network (DNN) to approximate it If the nonlinear mapping can be learned accurately by a DNN of moderate size, then SP tasks can be performed effectively—since passing the input through a DNN only requires a small number of simple operations In our paper, we first identify a class of optimization algorithms that can be accurately approximated by a fully connected DNN Second, to demonstrate the effectiveness of the proposed approach, we apply it to approximate a popular interference management algorithm, namely, the WMMSE algorithm Extensive experiments using both synthetically generated wireless channel data and real DSL channel data have been conducted It is shown that, in practice, only a small network is sufficient to obtain high approximation accuracy, and DNNs can achieve orders of magnitude speedup in computational time compared to the state-of-the-art interference management algorithm