Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

Transcription factor achaete-scute homologue 2 initiates follicular T-helper-cell development

Abstract: In immune responses, activated T cells migrate to B-cell follicles and develop into follicular T-helper (TFH) cells, a recently identified subset of CD4(+) T cells specialized in providing help to B lymphocytes in the induction of germinal centres. Although Bcl6 has been shown to be essential in TFH-cell function, it may not regulate the initial migration of T cells or the induction of the TFH program, as exemplified by C-X-C chemokine receptor type 5 (CXCR5) upregulation. Here we show that expression of achaete-scute homologue 2 (Ascl2)--a basic helix-loop-helix (bHLH) transcription factor--is selectively upregulated in TFH cells. Ectopic expression of Ascl2 upregulates CXCR5 but not Bcl6, and downregulates C-C chemokine receptor 7 (CCR7) expression in T cells in vitro, as well as accelerating T-cell migration to the follicles and TFH-cell development in vivo in mice. Genome-wide analysis indicates that Ascl2 directly regulates TFH-related genes whereas it inhibits expression of T-helper cell 1 (TH1) and TH17 signature genes. Acute deletion of Ascl2, as well as blockade of its function with the Id3 protein in CD4(+) T cells, results in impaired TFH-cell development and germinal centre response. Conversely, mutation of Id3, known to cause antibody-mediated autoimmunity, greatly enhances TFH-cell generation. Thus, Ascl2 directly initiates TFH-cell development.

FeVO4 as a highly active heterogeneous Fenton-like catalyst towards the degradation of Orange II

Abstract: The iron vanadate, FeVO4, was prepared and characterized by X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM) It was found that FeVO4 could effectively catalyze H2O2 to generate active hydroxyl radical OH, which was confirmed with electron spin resonance (ESR) spin-trapping technique Therefore, it was employed as a heterogeneous Fenton-like catalyst in the present contribution, and its catalytic activity was mainly evaluated in terms of the degradation efficiency of Orange II Compared with the conventional heterogeneous Fenton-like catalysts, α-Fe2O3, Fe3O4 and γ-FeOOH, FeVO4 possessed a much higher catalytic activity The high catalytic activity possibly involved in a special two-way Fenton-like mechanism, that is, the activation of H2O2 by both Fe(III) and V(V) in FeVO4 Moreover, FeVO4 possessed a wide applicable pH range and its catalytic activity was slightly affected by the solution pH values in the range of 3–8

Unsupervised Person Re-Identification by Soft Multilabel Learning

Abstract: Although unsupervised person re-identification (RE-ID) has drawn increasing research attentions due to its potential to address the scalability problem of supervised RE-ID models, it is very challenging to learn discriminative information in the absence of pairwise labels across disjoint camera views. To overcome this problem, we propose a deep model for the soft multilabel learning for unsupervised RE-ID. The idea is to learn a soft multilabel (real-valued label likelihood vector) for each unlabeled person by comparing the unlabeled person with a set of known reference persons from an auxiliary domain. We propose the soft multilabel-guided hard negative mining to learn a discriminative embedding for the unlabeled target domain by exploring the similarity consistency of the visual features and the soft multilabels of unlabeled target pairs. Since most target pairs are cross-view pairs, we develop the cross-view consistent soft multilabel learning to achieve the learning goal that the soft multilabels are consistently good across different camera views. To enable effecient soft multilabel learning, we introduce the reference agent learning to represent each reference person by a reference agent in a joint embedding. We evaluate our unified deep model on Market-1501 and DukeMTMC-reID. Our model outperforms the state-of-the-art unsupervised RE-ID methods by clear margins. Code is available at https://github.com/KovenYu/MAR.