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.

TianQin: a space-borne gravitational wave detector

Abstract: TianQin is a proposal for a space-borne detector of gravitational waves in the millihertz frequencies. The experiment relies on a constellation of three dragfree spacecraft orbiting the Earth. Inter-spacecraft laser interferometry is used to monitor the distances between the test masses. The experiment is designed to be capable of detecting a signal with high confidence from a single source of gravitational waves within a few months of observing time. We describe the preliminary mission concept for TianQin, including the candidate source and experimental designs. We present estimates for the major constituents of the experiment's error budget and discuss the project's overall feasibility. Given the current level of technological readiness, we expect TianQin to be flown in the second half of the next decade.

Identification of genomic alterations in oesophageal squamous cell cancer

Abstract: Oesophageal cancer is one of the most aggressive cancers and is the sixth leading cause of cancer death worldwide(1). Approximately 70% of global oesophageal cancer cases occur in China, with oesophageal squamous cell carcinoma (ESCC) being the histopathological form in the vast majority of cases (>90%)(2,3). Currently, there are limited clinical approaches for the early diagnosis and treatment of ESCC, resulting in a 10% five-year survival rate for patients. However, the full repertoire of genomic events leading to the pathogenesis of ESCC remains unclear. Here we describe a comprehensive genomic analysis of 158 ESCC cases, as part of the International Cancer Genome Consortium research project. We conducted whole-genome sequencing in 17 ESCC cases and whole-exome sequencing in 71 cases, of which 53 cases, plus an additional 70 ESCC cases not used in the whole-genome and whole-exome sequencing, were subjected to array comparative genomic hybridization analysis. We identified eight significantly mutated genes, of which six are well known tumour-associated genes (TP53, RB1, CDKN2A, PIK3CA, NOTCH1, NFE2L2), and two have not previously been described in ESCC (ADAM29 and FAM135B). Notably, FAM135B is identified as a novel cancer-implicated gene as assayed for its ability to promote malignancy of ESCC cells. Additionally, MIR548K, a microRNA encoded in the amplified 11q13.3-13.4 region, is characterized as a novel oncogene, and functional assays demonstrate that MIR548K enhances malignant phenotypes of ESCC cells. Moreover, we have found that several important histone regulator genes (MLL2 (also called KMT2D), ASH1L, MLL3 (KMT2C), SETD1B, CREBBP and EP300) are frequently altered in ESCC. Pathway assessment reveals that somatic aberrations are mainly involved in the Wnt, cell cycle and Notch pathways. Genomic analyses suggest that ESCC and head and neck squamous cell carcinoma share some common pathogenic mechanisms, and ESCC development is associated with alcohol drinking. This study has explored novel biological markers and tumorigenic pathways that would greatly improve therapeutic strategies for ESCC.

Is Faster R-CNN Doing Well for Pedestrian Detection?

Abstract: Detecting pedestrian has been arguably addressed as a special topic beyond general object detection. Although recent deep learning object detectors such as Fast/Faster R-CNN have shown excellent performance for general object detection, they have limited success for detecting pedestrian, and previous leading pedestrian detectors were in general hybrid methods combining hand-crafted and deep convolutional features. In this paper, we investigate issues involving Faster R-CNN for pedestrian detection. We discover that the Region Proposal Network (RPN) in Faster R-CNN indeed performs well as a stand-alone pedestrian detector, but surprisingly, the downstream classifier degrades the results. We argue that two reasons account for the unsatisfactory accuracy: (i) insufficient resolution of feature maps for handling small instances, and (ii) lack of any bootstrapping strategy for mining hard negative examples. Driven by these observations, we propose a very simple but effective baseline for pedestrian detection, using an RPN followed by boosted forests on shared, high-resolution convolutional feature maps. We comprehensively evaluate this method on several benchmarks (Caltech, INRIA, ETH, and KITTI), presenting competitive accuracy and good speed. Code will be made publicly available.

A Stable Pentagonal Bipyramidal Dy(III) Single-Ion Magnet with a Record Magnetization Reversal Barrier over 1000 K.

Abstract: Single-molecule magnets (SMMs) with a large spin reversal barrier have been recognized to exhibit slow magnetic relaxation that can lead to a magnetic hysteresis loop. Synthesis of highly stable SMMs with both large energy barriers and significantly slow relaxation times is challenging. Here, we report two highly stable and neutral Dy(III) classical coordination compounds with pentagonal bipyramidal local geometry that exhibit SMM behavior. Weak intermolecular interactions in the undiluted single crystals are first observed for mononuclear lanthanide SMMs by micro-SQUID measurements. The investigation of magnetic relaxation reveals the thermally activated quantum tunneling of magnetization through the third excited Kramers doublet, owing to the increased axial magnetic anisotropy and weaker transverse magnetic anisotropy. As a result, pronounced magnetic hysteresis loops up to 14 K are observed, and the effective energy barrier (Ueff = 1025 K) for relaxation of magnetization reached a breakthrough among the SMMs.

National differences in gender–science stereotypes predict national sex differences in science and math achievement

Abstract: About 70% of more than half a million Implicit Association Tests completed by citizens of 34 countries revealed expected implicit stereotypes associating science with males more than with females. We discovered that nation-level implicit stereotypes predicted nation-level sex differences in 8th-grade science and mathematics achievement. Self-reported stereotypes did not provide additional predictive validity of the achievement gap. We suggest that implicit stereotypes and sex differences in science participation and performance are mutually reinforcing, contributing to the persistent gender gap in science engagement.