Shanghai Jiao Tong University

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.

Genome-Wide Analysis of Basic/Helix-Loop-Helix Transcription Factor Family in Rice and Arabidopsis

Abstract: The basic/helix-loop-helix (bHLH) transcription factors and their homologs form a large family in plant and animal genomes. They are known to play important roles in the specification of tissue types in animals. On the other hand, few plant bHLH proteins have been studied functionally. Recent completion of whole genome sequences of model plants Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) allows genome-wide analysis and comparison of the bHLH family in flowering plants. We have identified 167 bHLH genes in the rice genome, and their phylogenetic analysis indicates that they form well-supported clades, which are defined as subfamilies. In addition, sequence analysis of potential DNA-binding activity, the sequence motifs outside the bHLH domain, and the conservation of intron/exon structural patterns further support the evolutionary relationships among these proteins. The genome distribution of rice bHLH genes strongly supports the hypothesis that genome-wide and tandem duplication contributed to the expansion of the bHLH gene family, consistent with the birth-and-death theory of gene family evolution. Bioinformatics analysis suggests that rice bHLH proteins can potentially participate in a variety of combinatorial interactions, endowing them with the capacity to regulate a multitude of transcriptional programs. In addition, similar expression patterns suggest functional conservation between some rice bHLH genes and their close Arabidopsis homologs.

False rumors detection on Sina Weibo by propagation structures

Abstract: This paper studies the problem of automatic detection of false rumors on Sina Weibo, the popular Chinese microblogging social network. Traditional feature-based approaches extract features from the false rumor message, its author, as well as the statistics of its responses to form a flat feature vector. This ignores the propagation structure of the messages and has not achieved very good results. We propose a graph-kernel based hybrid SVM classifier which captures the high-order propagation patterns in addition to semantic features such as topics and sentiments. The new model achieves a classification accuracy of 91.3% on randomly selected Weibo dataset, significantly higher than state-of-the-art approaches. Moreover, our approach can be applied at the early stage of rumor propagation and is 88% confident in detecting an average false rumor just 24 hours after the initial broadcast.

Knowledge Graph Convolutional Networks for Recommender Systems

Abstract: To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the attributes are not isolated but connected with each other, which forms a knowledge graph (KG). In this paper, we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG. To automatically discover both high-order structure information and semantic information of the KG, we sample from the neighbors for each entity in the KG as their receptive field, then combine neighborhood information with bias when calculating the representation of a given entity. The receptive field can be extended to multiple hops away to model high-order proximity information and capture users' potential long-distance interests. Moreover, we implement the proposed KGCN in a minibatch fashion, which enables our model to operate on large datasets and KGs. We apply the proposed model to three datasets about movie, book, and music recommendation, and experiment results demonstrate that our approach outperforms strong recommender baselines.

Dark matter and dark energy interactions: theoretical challenges, cosmological implications and observational signatures

Abstract: Models where dark matter and dark energy interact with each other have been proposed to solve the coincidence problem. We review the motivations underlying the need to introduce such interaction, its influence on the background dynamics and how it modifies the evolution of linear perturbations. We test models using the most recent observational data and we find that the interaction is compatible with the current astronomical and cosmological data. Finally, we describe the forthcoming data sets from current and future facilities that are being constructed or designed that will allow a clearer understanding of the physics of the dark sector.

Screening ethnically diverse human embryonic stem cells identifies a chromosome 20 minimal amplicon conferring growth advantage

Abstract: The International Stem Cell Initiative analyzed 125 human embryonic stem (ES) cell lines and 11 induced pluripotent stem (iPS) cell lines, from 38 laboratories worldwide, for genetic changes occurring during culture. Most lines were analyzed at an early and late passage. Single-nucleotide polymorphism (SNP) analysis revealed that they included representatives of most major ethnic groups. Most lines remained karyotypically normal, but there was a progressive tendency to acquire changes on prolonged culture, commonly affecting chromosomes 1, 12, 17 and 20. DNA methylation patterns changed haphazardly with no link to time in culture. Structural variants, determined from the SNP arrays, also appeared sporadically. No common variants related to culture were observed on chromosomes 1, 12 and 17, but a minimal amplicon in chromosome 20q11.21, including three genes expressed in human ES cells, ID1, BCL2L1 and HM13, occurred in >20% of the lines. Of these genes, BCL2L1 is a strong candidate for driving culture adaptation of ES cells.