Browsing by Author "Xu, Jiawei"

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    CXCR5+ follicular cytotoxic T cells control viral infection in B cell follicles
    (Nature, 2016) Leong, Yew Ann; Chen, Yaping; Ong, Hong Sheng; Wu, Di; Man, Kevin; Deleage, Claire; Minnich, Martina; Meckiff, Benjamin J.; Wei, Yunbo; Hou, Zhaohua; Zotos, Dimitra; Fenix, Kevin A.; Atnerkar, Anurag; Preston, Simon; Chipman, Jeffrey G.; Beilman, Greg J.; Allison, Cody C.; Sun, Lei; Wang, Peng; Xu, Jiawei; Toe, Jesse G.; Lu, Hao K.; Tao, Yong; Palendira, Umaimainthan; Dent, Alexander L.; Landay, Alan L.; Pellegrini, Marc; Comerford, Iain; McColl, Shaun R.; Schacker, Timothy W.; Long, Heather M.; Estes, Jacob D.; Busslinger, Meinrad; Belz, Gabrielle T.; Lewin, Sharon R.; Kallies, Axel; Yu, Di; Department of Microbiology and Immunology, IU School of Medicine
    During unresolved infections, some viruses escape immunological control and establish a persistant reservoir in certain cell types, such as human immunodeficiency virus (HIV), which persists in follicular helper T cells (TFH cells), and Epstein-Barr virus (EBV), which persists in B cells. Here we identified a specialized group of cytotoxic T cells (TC cells) that expressed the chemokine receptor CXCR5, selectively entered B cell follicles and eradicated infected TFH cells and B cells. The differentiation of these cells, which we have called 'follicular cytotoxic T cells' (TFC cells), required the transcription factors Bcl6, E2A and TCF-1 but was inhibited by the transcriptional regulators Blimp1, Id2 and Id3. Blimp1 and E2A directly regulated Cxcr5 expression and, together with Bcl6 and TCF-1, formed a transcriptional circuit that guided TFC cell development. The identification of TFC cells has far-reaching implications for the development of strategies to control infections that target B cells and TFH cells and to treat B cell–derived malignancies.
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    User Leaving Detection Via MMwave Imaging
    (2023-08) Xu, Jiawei; King, Brian; Li, Tao; Zhang, Qingxue
    The use of smart devices such as smartphones, tablets, and laptops skyrocketed in the last decade. These devices enable ubiquitous applications for entertainment, communication, productivity, and healthcare but also introduce big concern about user privacy and data security. In addition to various authentication techniques, automatic and immediate device locking based on user leaving detection is an indispensable way to secure the devices. Current user leaving detection techniques mainly rely on acoustic ranging and do not work well in environments with multiple moving objects. In this paper, we present mmLock, a system that enables faster and more accurate user leaving detection in dynamic environments. mmLock uses a mmWave FMCW radar to capture the user’s 3D mesh and detects the leaving gesture from the 3D human mesh data with a hybrid PointNet-LSTM model. Based on explainable user point clouds, mmLock is more robust than existing gesture recognition systems which can only identify the raw signal patterns. We implement and evaluate mmLock with a commercial off-the-shelf (COTS) TI mmWave radar in multiple environments and scenarios. We train the PointNet-LSTM model out of over 1 TB mmWave signal data and achieve 100% true-positive rate in most scenarios.