PRMT5-Selective Inhibitors Suppress Inflammatory T Cell Responses and Experimental Autoimmune Encephalomyelitis

In the autoimmune disease multiple sclerosis and its animal model, experimental autoimmune encephalomyelitis (EAE), expansion of pathogenic, myelin-specific Th1 cell populations drives active disease; selectively targeting this process may be the basis for a new therapeutic approach. Previous studies have hinted at a role for protein arginine methylation in immune responses, including T cell–mediated autoimmunity and EAE. However, a conclusive role for the protein arginine methyltransferase (PRMT) enzymes that catalyze these reactions has been lacking. PRMT5 is the main PRMT responsible for symmetric dimethylation of arginine residues of histones and other proteins. PRMT5 drives embryonic development and cancer, but its role in T cells, if any, has not been investigated. In this article, we show that PRMT5 is an important modulator of CD4+ T cell expansion. PRMT5 was transiently upregulated during maximal proliferation of mouse and human memory Th cells. PRMT5 expression was regulated upstream by the NF-κB pathway, and it promoted IL-2 production and proliferation. Blocking PRMT5 with novel, highly selective small molecule PRMT5 inhibitors severely blunted memory Th expansion, with preferential suppression of Th1 cells over Th2 cells. In vivo, PRMT5 blockade efficiently suppressed recall T cell responses and reduced inflammation in delayed-type hypersensitivity and clinical disease in EAE mouse models. These data implicate PRMT5 in the regulation of adaptive memory Th cell responses and suggest that PRMT5 inhibitors may be a novel therapeutic approach for T cell–mediated inflammatory disease

Pattern exploration and event detection from geo-tagged tweets

Twitter is one of the most famous social networking services in the world. With 313 million monthly active users, Twitter can produce around 6,000 tweets per second, which corresponds to around 500 million tweets per day and around 200 billion tweets per year. Besides being a successful company, Twitter provides a great opportunity to scientists from various disciplines. Twitter allows users to tweet with a location tag, which enables the connection of virtual networks to the events happening in real life. Because of the massive amount of valuable geographic information, location-based services, targeted advertising, and social network studies could benefit considerably from the Twitter dataset. There are two primary objectives in this research. One is to identify the tweeting patterns of individual users; the other is to retrieve public events as well as to detect potential events. To identify the patterns of an individual user, this research selects the tweets from this user within a particular time period. The tweets are grouped by the hour of the day and then the density-based spatial clustering of applications with noise (DBSCAN) method is applied to cluster the tweets from every hour. Based on this method, the tweets are classified into different clusters without predefining the number of clusters. With the calculation of the spatial and temporal probability of every cluster, the probability of the appearance of the user in a particular area at a given time can be predicted. In event detection, the whole dataset is grouped by the day of the year, and the daily dataset is classified into clusters through ST- DBSCAN (Spatial-Temporal DBSCAN) to discover events. The word frequency of every cluster is analyzed. The Latent Dirichlet Allocation (LDA) algorithm is applied to every cluster to understand the potential topics. The proposed workflows for these objectives are tested in four college cities: (1) West Lafayette, Indiana; (2) Bloomington, Indiana; (3) Ann Arbor, Michigan; and (4) Columbus, Ohio. The results and analyses are presented in this thesis. On this basis, several recommendations on producing better results and dealing with special cases are presented

Automatically estimating iSAX parameters

The Symbolic Aggregate Approximation (iSAX) is widely used in time series data mining. Its popularity arises from the fact that it largely reduces time series size, it is symbolic, allows lower bounding and is space efficient. However, it requires setting two parameters: the symbolic length and alphabet size, which limits the applicability of the technique. The optimal parameter values are highly application dependent. Typically, they are either set to a fixed value or experimentally probed for the best configuration. In this work we propose an approach to automatically estimate iSAX’s parameters. The approach – AutoiSAX – not only discovers the best parameter setting for each time series in the database, but also finds the alphabet size for each iSAX symbol within the same word. It is based on simple and intuitive ideas from time series complexity and statistics. The technique can be smoothly embedded in existing data mining tasks as an efficient sub-routine. We analyze its impact in visualization interpretability, classification accuracy and motif mining. Our contribution aims to make iSAX a more general approach as it evolves towards a parameter-free method

Predicting Climate Change Effects on Riverine Mayfly (Ephemeroptera), Stonefly (Plecoptera), and Caddisfly (Trichoptera) Species in the Upper Midwest

unpublishednot peer reviewedOpe

ANALISIS TINGKAT PEMAHAMAN DAN PERSEPSI WAJIB PAJAK UMKM MENGENAI TAX AMNESTY

Penelitian ini merupakan studi deskriptif kualitatif yang bertujuan untuk menganalisis dan menggambarkan tingkat pemahaman wajib pajak UMKM mengenai Tax Amnesty. variabel yang diteliti adalah tingkat pemahaman wajib pajak umkm Penelitian dilakukan pada wajib pajak UMKM di kantor KPP Pratama kota Padang. Data yang digunakan dalam penelitian adalah data primer yang dikumpulkan dengan menggunakan kuesioner yang disebar kepada responden. Metode analisis yang digunakan adalah statistik deskriptif. Hasil penelitian diharapkan mampu memberikan analisis tingkat tingkat pemahaman wajib pajak UMKM mengenai Tax Amnest

PRMT5-Selective Inhibitors Suppress Inflammatory T Cell Responses and Experimental Autoimmune Encephalomyelitis

Poster Division: Biological Sciences: 2nd Place (The Ohio State University Edward F. Hayes Graduate Research Forum)In the autoimmune disease multiple sclerosis and its animal model, experimental autoimmune encephalomyelitis (EAE), ex- pansion of pathogenic, myelin-specific Th1 cell populations drives active disease; selectively targeting this process may be the basis for a new therapeutic approach. Previous studies have hinted at a role for protein arginine methylation in immune responses, in- cluding T cell–mediated autoimmunity and EAE. However, a conclusive role for the protein arginine methyltransferase (PRMT) enzymes that catalyze these reactions has been lacking. PRMT5 is the main PRMT responsible for symmetric dimethylation of arginine residues of histones and other proteins. PRMT5 drives embryonic development and cancer, but its role in T cells, if any, has not been investigated. In this article, we show that PRMT5 is an important modulator of CD4+ T cell expansion. PRMT5 was transiently upregulated during maximal proliferation of mouse and human memory Th cells. PRMT5 expression was regulated upstream by the NF-kB pathway, and it promoted IL-2 production and proliferation. Blocking PRMT5 with novel, highly selective small molecule PRMT5 inhibitors severely blunted memory Th expansion, with preferential suppression of Th1 cells over Th2 cells. In vivo, PRMT5 blockade efficiently suppressed recall T cell responses and reduced inflammation in delayed-type hypersensitivity and clinical disease in EAE mouse models. These data implicate PRMT5 in the regulation of adaptive memory Th cell responses and suggest that PRMT5 inhibitors may be a novel therapeutic approach for T cell–mediated inflammatory disease. The Journal of Immunology, 2017, 198: 000–000.A three-year embargo was granted for this item