Cancer exosomes are unique and complex mechanisms that suppress effector T lymphocyte functions

It is still unknown how tumor exosomes influence malignant cell survival and alter cell-to-cell communication to modulate the immune system by, in part, manipulating the activity of cytotoxic T lymphocytes. This study focuses on two critical parts regarding cancer exosomes. The first part is to tailor existing bionanotechnology methods to account for the nanoscale aspects of exosome biology. SEM, TEM, Bioanalyzer and flow cytometry were used to characterize exosome morphologies, identify specific protein biomarkers HER1 and HER2, as well as the quality of RNAs enclosed in exosomes. Competing methods related to exosome isolation, production, preservation, stability and analysis were evaluated. Based on these studies, we recommend improved experimental methods that aim to ensure a consistent framework to identify the roles that exosomes play. With these improved methods, the second part is to characterize the immunosuppressive role that melanoma exosomes play, especially from the perspective of delivering a payload of mRNAs to immune cells. Toward this second aim, melanoma exosomes were purified and cytokine receptor IL12Rbeta2 and specific mRNA enrichment were identified. Microarray and pathway analysis suggested that mRNAs derived from melanoma impact a variety of immune signaling pathways. Induction effects of PTPN11 and DNMT3A from the exosomal mRNAs were characterized in T lymphocytes. Specifically, we showed that PTPN11 upregulation impeded CTLL-2 cytotoxic T cell proliferation in response to IL2 stimulation, and DNMT3A upregulation hindered IFN-gamma production in 2D6 TH1 cells. These findings provide insights regarding the specific immunosuppression effects that tumor-infiltrating lymphocytes (TILs) may encounter in tumor microenvironment. Understanding those immunosuppression effects is important to engineer anti-tumor immunity for innovative and improved treatments against cancer

Representation Learning for Scale-free Networks

Network embedding aims to learn the low-dimensional representations of vertexes in a network, while structure and inherent properties of the network is preserved. Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while the macroscopic scale-free property is largely ignored. Scale-free property depicts the fact that vertex degrees follow a heavy-tailed distribution (i.e., only a few vertexes have high degrees) and is a critical property of real-world networks, such as social networks. In this paper, we study the problem of learning representations for scale-free networks. We first theoretically analyze the difficulty of embedding and reconstructing a scale-free network in the Euclidean space, by converting our problem to the sphere packing problem. Then, we propose the "degree penalty" principle for designing scale-free property preserving network embedding algorithm: punishing the proximity between high-degree vertexes. We introduce two implementations of our principle by utilizing the spectral techniques and a skip-gram model respectively. Extensive experiments on six datasets show that our algorithms are able to not only reconstruct heavy-tailed distributed degree distribution, but also outperform state-of-the-art embedding models in various network mining tasks, such as vertex classification and link prediction.Comment: 8 figures; accepted by AAAI 201

Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai

Unprecedented human mobility has driven the rapid urbanization around the world. In China, the fraction of population dwelling in cities increased from 17.9% to 52.6% between 1978 and 2012. Such large-scale migration poses challenges for policymakers and important questions for researchers. To investigate the process of migrant integration, we employ a one-month complete dataset of telecommunication metadata in Shanghai with 54 million users and 698 million call logs. We find systematic differences between locals and migrants in their mobile communication networks and geographical locations. For instance, migrants have more diverse contacts and move around the city with a larger radius than locals after they settle down. By distinguishing new migrants (who recently moved to Shanghai) from settled migrants (who have been in Shanghai for a while), we demonstrate the integration process of new migrants in their first three weeks. Moreover, we formulate classification problems to predict whether a person is a migrant. Our classifier is able to achieve an F1-score of 0.82 when distinguishing settled migrants from locals, but it remains challenging to identify new migrants because of class imbalance. This classification setup holds promise for identifying new migrants who will successfully integrate into locals (new migrants that misclassified as locals).Comment: A modified version. The paper was accepted by AAAI 201

Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C$^2$SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.Comment: AAAI 201