Key exchange process of PIM-SM-based for Multiple Group Communication in P2P

Peer-to-Peer Networking and Applications - Recently, as the P2P service environment repeats evolution, its usage in mobile is rapidly increasing. Applications are being developed in various mobile...     

Characterization and Evaluation of Neuronal Trans-Differentiation with Electrophysiological Properties of Mesenchymal Stem Cells Isolated from Porcine Endometrium

Endometrial stromal cells (EMSCs) obtained from porcine uterus (n = 6) were positive for mesenchymal stem cell markers (CD29, CD44 and CD90), and negative for epithelial marker CD9 and hematopoietic markers CD34, CD45 analyzed by flow cytometry. Further the cells were positive for expression of mesenchymal markers, CD105, CD140b, and CD144 by PCR. Pluripotent markers OCT4, SOX2, and NANOG were positively expressed in EMSCs analyzed by Western blotting and PCR. Further, differentiation into adipocytes and osteocytes was confirmed by cytochemical staining and lineage specific gene expression by quantitative realtime-PCR. Adipocyte (FABP, LPL, AP2) and osteocyte specific genes (ON, BG, RUNX2) in differentiated EMSCs showed significant (p < 0.05) increase in expression compared to undifferentiated control cells. Neurogenic transdifferentiation of EMSCs exhibited distinctive dendritic morphology with axon projections and neuronal specific genes, NFM, NGF, MBP, NES, B3T and MAP2 and proteins, B3T, NFM, NGF, and TRKA were positively expressed in neuronal differentiated cells. Functional analysis of neuronal differentiated EMSCs displayed voltage-dependence and kinetics for transient outward K⁺ currents (I_to), at holding potential of −80 mV, Na⁺ currents and during current clamp, neuronal differentiated EMSCs was more negative than that of control EMSCs. Porcine EMSCs is a suitable model for studying molecular mechanism of transdifferentiation, assessment of electrophysiological properties and their efficiency during in vivo transplantation.     

A geospatial technique for detecting distance and Reflection Angle between real and virtual objects

This paper presents a geospatial collision detection technique consisting of two methods: Find Object Distance (FOD) and Find Reflection Angle (FRA). We show how the geospatial collision detection technique using a computer vision system detects a computer generated virtual object and a real object manipulated by a human user and how the virtual object can be reflected on a real floor after being detected by a real object. In the geospatial collision detection technique, the FOD method detects the real and virtual objects, and the FRA method predicts the next moving directions of virtual objects. We demonstrate the two methods by implementing a floor based Augmented Reality (AR) game, Ting Ting, which is played by bouncing fire-shaped virtual objects projected on a floor using bamboo-shaped real objects. The results reveal that the FOD and the FRA methods of the geospatial collision detection technique enable the smooth interaction between a real object manipulated by a human user and a virtual object controlled by a computer. The proposed technique is expected to be used in various AR applications as a low cost interactive collision detection engine such as in educational materials, interactive contents including games, and entertainment equipments. Keywords: Augmented reality, collision detection, computer vision, game, human computer interaction, image processing, interfaces.