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Institution

Shanghai Jiao Tong University

EducationShanghai, Shanghai, China
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.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a structural VAR analysis of the stock market response to oil price shocks is presented, showing that the magnitude, duration, and even direction of response by stock market in a country to price shocks highly depend on whether the country is a net importer or exporter in the world oil market, and whether changes in oil price are driven by supply or aggregate demand.

395 citations

Journal ArticleDOI
TL;DR: It is demonstrated that CSC phenotype could be precisely defined by co‐expression of CD133 and CD44 cell surface markers, which suggest that CD133+CD44+ cells might represent true cancer stem/progenitor cells in HCC, which could allow a better understanding of HCC initiation and progression, and establish a precise target for the development of more effective therapies.
Abstract: Both our previous study and other reports have suggested that CD133, originally classified as a hematopoietic stem cell marker, could be used for enrichment of cancer stem cells (CSCs) in human hepatocellular carcinoma (HCC). It was also noted that not all of CD133(+) cells were representative of CSCs. Further identification and characterization of CSCs or tumor-initiating cells in HCC are necessary to better understand hepatocarcinogenesis. In present study, we demonstrated that CSC phenotype could be precisely defined by co-expression of CD133 and CD44 cell surface markers. CD133(+)CD44(+) HCC cells showed stem cell properties, including extensive proliferation, self-renewal, and differentiation into the bulk of cancer cells. In vivo xenograft experiments revealed that, actually, the highly tumorigenic capacity of CD133(+) cells as previously described was primarily attributed to CD133(+)CD44(+) cell subpopulation, instead of their CD133(+)CD44(-) counterparts. Moreover, cells double-positive for CD133 and CD44 exhibited preferential expression of some stem cell-associated genes and were more resistant to chemotherapeutic agents due to the upregulation of ATP-binding cassette (ABC) superfamily transporters, including ABCB1, ABCC1, and ABCG2, further supporting these cells as HCC cell origin. Our findings suggest that CD133(+)CD44(+) cells might represent true cancer stem/progenitor cells in HCC, which could allow a better understanding of HCC initiation and progression, as well as establish a precise target for the development of more effective therapies.

395 citations

Journal ArticleDOI
TL;DR: An obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object is introduced, and five extra target variables are added to the regression head of faster R-CNN, which requires ignorable extra computation time.
Abstract: Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this paper, we propose a simple yet effective framework to detect multi-oriented objects. Instead of directly regressing the four vertices, we glide the vertex of the horizontal bounding box on each corresponding side to accurately describe a multi-oriented object. Specifically, We regress four length ratios characterizing the relative gliding offset on each corresponding side. This may facilitate the offset learning and avoid the confusion issue of sequential label points for oriented objects. To further remedy the confusion issue for nearly horizontal objects, we also introduce an obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object. We add these five extra target variables to the regression head of faster R-CNN, which requires ignorable extra computation time. Extensive experimental results demonstrate that without bells and whistles, the proposed method achieves superior performances on multiple multi-oriented object detection benchmarks including object detection in aerial images, scene text detection, pedestrian detection in fisheye images.

395 citations

Journal ArticleDOI
TL;DR: It is suggested that the mucosa-associated microbiota is dynamically associated with CRC, which may provide evidences for microbiota-associated diagnostic, prognostic, preventive, and therapeutic strategies for CRC.
Abstract: The dysbiosis of the human intestinal microbiota is linked to sporadic colorectal carcinoma (CRC). The present study was designed to investigate the gut microbiota distribution features in CRC patients. We performed pyrosequencing based analysis of the 16S rRNA gene V3 region to investigate microbiota of the cancerous tissue and adjacent noncancerous normal tissue in proximal and distal CRC samples. The results revealed that the microbial structures of the CRC patients and healthy individuals differed significantly. Firmicutes and Fusobacteria were over-represented whereas Proteobacteria was under-represented in CRC patients. In addition, Lactococcus and Fusobacterium exhibited a relatively higher abundance while Pseudomonas and Escherichia-Shigella was reduced in cancerous tissues compared to adjacent noncancerous tissues. Meanwhile, the overall microbial structures of proximal and distal colon cancerous tissues were similar; but certain potential pro-oncogenic pathogens were different. These results suggested that the mucosa-associated microbiota is dynamically associated with CRC, which may provide evidences for microbiota-associated diagnostic, prognostic, preventive and therapeutic strategies for CRC.

394 citations

Journal ArticleDOI
TL;DR: In this article, the role of the cathode in non-aqueous Li-air batteries including the reaction mechanisms and the properties and morphologies of cathode materials, followed by approaches to optimize cathode performance.
Abstract: Rechargeable lithium air (Li–air) batteries, especially the non-aqueous type, are considered the most promising energy storage and conversion device candidates for use in future electric vehicle applications due to their ultrahigh energy density. The air cathode has been identified as a key factor affecting the overall performance of Li–air batteries. The current low level performance of air cathodes is the major challenge hindering commercial applications of Li–air batteries. In the past decade, a great many cathode materials, structures and fabrication processes have been developed and investigated with the goal of enhancing cathode performance. This paper reviews, the role of the cathode in non-aqueous Li–air batteries including the cathode reaction mechanisms and the properties and morphologies of cathode materials, followed by approaches to optimize cathode performance. The most recently published global progress and the main achievements in the field of Li–air batteries are also systematically and critically reviewed in terms of cathode materials, structures and fabrication processes, with the objective of providing some state-of-the-art information. Technical challenges are analyzed, and insights into future research directions for overcoming these development challenges of rechargeable non-aqueous Li–air battery cathodes are also identified in this review paper.

393 citations


Authors

Showing all 158621 results

NameH-indexPapersCitations
Meir J. Stampfer2771414283776
Richard A. Flavell2311328205119
Jie Zhang1784857221720
Yang Yang1712644153049
Lei Jiang1702244135205
Gang Chen1673372149819
Thomas S. Huang1461299101564
Barbara J. Sahakian14561269190
Jean-Laurent Casanova14484276173
Kuo-Chen Chou14348757711
Weihong Tan14089267151
Xin Wu1391865109083
David Y. Graham138104780886
Bin Liu138218187085
Jun Chen136185677368
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023415
20222,316
202120,875
202019,462
201916,699
201814,250