Tongji University

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

Easy-to-operate and low-temperature synthesis of gram-scale nitrogen-doped graphene and its application as cathode catalyst in microbial fuel cells.

Abstract: Nitrogen-doped graphene (NG), with unique electronic properties, is showing great promise for a wide range of practical applications. However, the reported approaches for NG synthesis are usually complex, require high temperatures, produce lower atomic ratios of nitrogen to carbon (N/C), and do not deliver products in a reasonably large quantity. Here we report an easy-to-operate and low-temperature method to synthesize NG in gram-scale quantities with a denotation process. High-resolution transmission electron microscopy, Raman spectroscopy, and X-ray diffraction characterization suggested that the synthesized NG films were uniformly multilayered and had a high crystalline quality. In the graphene sheets the existence of nitrogen substitution with an atomic ratio of N/C 12.5%, which was greater than those reported in the literature, was confirmed by X-ray photoelectron spectroscopic analysis. In the neutral phosphate buffer solution, the synthesized NG was demonstrated to act as a metal-free electrode wi...

Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification

Abstract: Person re-identification (Re-ID) has achieved great improvement with deep learning and a large amount of labelled training data. However, it remains a challenging task for adapting a model trained in a source domain of labelled data to a target domain of only unlabelled data available. In this work, we develop a self-training method with progressive augmentation framework (PAST) to promote the model performance progressively on the target dataset. Specially, our PAST framework consists of two stages, namely, conservative stage and promoting stage. The conservative stage captures the local structure of target-domain data points with triplet-based loss functions, leading to improved feature representations. The promoting stage continuously optimizes the network by appending a changeable classification layer to the last layer of the model, enabling the use of global information about the data distribution. Importantly, we propose a new self-training strategy that progressively augments the model capability by adopting conservative and promoting stages alternately. Furthermore, to improve the reliability of selected triplet samples, we introduce a ranking-based triplet loss in the conservative stage, which is a label-free objective function based on the similarities between data pairs. Experiments demonstrate that the proposed method achieves state-of-the-art person Re-ID performance under the unsupervised cross-domain setting. Code is available at: tinyurl.com/PASTReID

Upregulation of long non-coding RNA MALAT1 correlates with tumor progression and poor prognosis in clear cell renal cell carcinoma

Abstract: Long noncoding RNAs (lncRNAs) have been investigated as a new class of regulators of cellular processes, such as cell growth, apoptosis, and carcinogenesis. LncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) has recently been identified to be involved in tumorigenesis of several cancers such as lung cancer, pancreatic cancer, and cervical cancer. However, the role of lncRNA MALAT1 in clear cell renal cell carcinoma (ccRCC) remains unclear. Expression levels of lncRNA MALAT1 in ccRCC tissues and renal cancer cell lines were evaluated by quantitative real-time PCR (qRT-PCR), and its association with overall survival of patients was analyzed by statistical analysis. Small interfering RNA (siRNA) was used to suppress MALAT1 expression in renal cancer cells. In vitro assays were conducted to further explore its role in tumor progression. The expression level of MALAT1 was higher in ccRCC tissues and renal cancer cells compared to adjacent non-tumor tissues and normal human proximal tubule epithelial cells HK-2. The ccRCC patients with higher MALAT1 expression had an advanced clinical features and a shorter overall survival time than those with lower MALAT1 expression. And multivariate analysis showed that the status of MALAT1 expression was an independent predictor of overall survival in ccRCC. Additionally, our data indicated that knockdown expression of MALAT1 decreased renal cancer cell proliferation, migration, and invasion. Our data suggested that lncRNA MALAT1 was a novel molecule involved in ccRCC progression, which provided a potential prognostic biomarker and therapeutic target.