Distinct Role of IL-27 in Immature and LPS-Induced Mature Dendritic Cell-Mediated Development of CD4

Interleukin-27 (IL-27) plays an important role in regulation of anti-inflammatory responses and autoimmunity; however, the molecular mechanisms of IL-27 in modulation of immune tolerance and autoimmunity have not been fully elucidated. Dendritic cells (DCs) play a central role in regulating immune responses mediated by innate and adaptive immune systems, but regulatory mechanisms of DCs in CD4+ T cell-mediated immune responses have not yet been elucidated. Here we show that IL-27 treated mature DCs induced by LPS inhibit immune tolerance mediated by LPS-stimulated DCs. IL-27 treatment facilitates development of the CD4+ CD127+3G11+ regulatory T cell subset in vitro and in vivo. By contrast, IL-27 treated immature DCs fail to modulate development of the CD4+CD127+3G11+ regulatory T cell sub-population in vitro and in vivo. Our results suggest that IL-27 may break immune tolerance induced by LPS-stimulated mature DCs through modulating development of a specific CD4+ regulatory T cell subset mediated by 3G11 and CD127. Our data reveal a new cellular regulatory mechanism of IL-27 that targets DC-mediated immune responses in autoimmune diseases such as multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE). © 2018 Zhou, Zhang and Rostami

Mycoflora of fungal contamination in wheat storage (silos) in golestan province, north of Iran

Background: Cereal products are susceptible to mould damage during pre- and post-harvesting stages of the production. The regional specificity of Golestan province in the northern region of of Iran, with its high temperature and high relative humidity, acts as a leading factor for the growth of aflatoxin-producing fungi. It is well known that contamination of starch-based ingredients with mycotoxigenic fungi is a risk factor among the consumers due to its aflatoxins. Objectives: This survey was carried out to determine the extent of fungal contamination of wheat in three silos of Golestan province in Iran. Materials and Methods: 34 samples from three active silos were collected in order to clean the polyethylene bags. Wheat analyzed for fungal contamination and aflatoxins extracted by immunoaffinity column chromatography, and measured by HPLC method. Results: The most common moulds isolated were Alternaria spp. 26.7%, Aspergillus niger 21.4%, Fusarium spp. 17.8%, Aspergillus flavus 10.7%, Cladosporium spp. 10.7%, Penicillium spp. 8.9%, and Rhizopus spp. 3.5%. The screening of aflatoxin, B1, B2, G1 and G2 was carried out. 10(29.4%) samples of wheat had traces of aflatoxin, but in a level lower than the standard levels [Institute of Standards and Industrial Research of Iran (ISIR< 15 ng/g)]. Conclusions: Despite the lower detected aflatoxin levels (lower than the ISIR level), the fungal contamination rate could not be neglected. Since the isolated mycotoxigenic fungi such as Aspergillus spp. and Fusarium spp. are important in food industry, it would be possible that the increased retention time of samples might have raised the detected contamination rate. © 2013, Ahvaz Jundishapur University of Medical Sciences

Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles