The Induction of Dendritic Cell Endoplasmic Reticulum Stress by Irradiated-Tumor Derived Extracellular Vesicles Supports the Adoption of a Pro-Tumor Phenotype

The Induction of Macrophage Endoplasmic Reticulum Stress by Irradiated-Tumor Derived Extracellular Vesicles Supports the Adoption of a Pro-Tumor Phenotype Sitara Mahmoodi, Depts. of Biology and Chemistry, with Dr. Sarah Golding, Dept. of Biology Recent studies have shown that long term exposure of tumor cells to sub-lethal levels of endoplasmic reticulum (ER) stress leads to the suppression of anti-tumor immunity through the manipulation of myeloid cells in the tumor microenvironment.1 While this effect seems to be dependent upon the ability of cancer cells to “transfer” the state of ER stress to myeloid cells, i.e. to initiate ER stress signaling in myeloid cells independent of the original stimulus, exactly how stressed cancer cells accomplish this is still not well understood1. Our focus is on exosomes which are extracellular vesicles and how they play a significant role in this mechanism. In recent studies, we demonstrated how extracellular vesicles secreted by irradiated melanoma cancer cells (IR-EVs) induce ER stress in Bone Marrow Dendritic Cells (BMDCs). In addition, BMDCs treated with IR-EVs demonstrated enhanced STAT3 and p38 signaling, two related pathways that have been demonstrated to induce tolerogenic DC phenotypes, in an ER stress dependent manner2. We have also found that IR- EVs stimulate the production of IL-10, a major negative regulator of antitumor immunity, from BMDCs and that this expression can be eliminated by STAT3 inhibition2. However, using a T-Cell Receptor/ tumor- associated antigen (TCR/TAA) system to model the interaction between BMDCs and cytotoxic T cells from a tumor rejection antigen (Pmel/gp100), we have observed that pharmaceutical ER stress or STAT3 inhibition dramatically inhibits T cell proliferation and IFN-gamma expression in response to antigen pulsed BMDCs. This suggests that ER stress and STAT3 signaling are both necessary for the presentation of tumor antigens to cytotoxic T cells, indicating that inhibition of these pathways would not be a desirable approach to enhance antitumor immunity in vivo. Thus, our current focus is on finding a way to inhibit the production or activity of these IR-EVs directly, inhibiting their effects on DCs in the body while leaving STAT3 signaling in proliferating T cells unaltered.https://scholarscompass.vcu.edu/uresposters/1346/thumbnail.jp

φ\varphi-contractibility and φ\varphi-Connes amenability coincide with some older notions

It is shown that various definitions of $\varphi$-Connes amenability as introduced independently in \cite{Gh-Ja, mah, Sh-Am}, are just rediscovering existing notions and presenting them in different ways. It is also proved that even $\varphi$-contractibility as defined in \cite{Sangani}, is equivalent to an older and simpler concept.Comment: 4 page

Bessel Filter Analysis

In this technical report, we prove two fundamental theorems for an edge detection algorithm based on a Bessel filter. These theorems are bases of a scale invariant feature extraction method in which extracted features are independent of scale

Discontinuity Preserving Noise Removal Method based on Anisotropic Diffusion for Band Pass Signals

nonlinear discontinuity-preserving method for noise removal for band pass signals such as signals modulated with Binary Phase-Shift Keying (BPSK) modulation is proposed in this paper. This method is inspired by the anisotropic diffusion algorithm to remove noise and preserve discontinuities in band pass signals modulated with a single frequency. It is demonstrated here that nonlinear noise removal method for a real valued band pass signal requires a solution for a nonlinear partial differential equation which is of fourth order in space and second order in time. The results presented in this work show better performance in nonlinear noise removal for real valued band pass signals in comparison with the previous work in the literature

A nonlinear variational method for signal segmentation and reconstruction using level set algorithm

A nonlinear functional is considered in this letter for segmentation and noise removal of piecewise continuous signals containing binary information contaminated with Gaussian noise. A discontinuity is defined as points in time scale that separates two signal segments with different amplitude spectra. Segmentation and noise removal of a piecewise continuous signal are obtained by deriving equations minimising the nonlinear functional. An algorithm based on the level set method is employed to implement the solutions minimising the functional. The proposed method is robust in noisy signals and can avoid local minima

Analysis of Uplink Scheduling for Haptic Communications

While new mechanisms and configurations of the 5G radio are offering step forward in delivery of ultra-reliable low latency communication services in general, and haptic communications in particular, they could inversely impact the remainder of traffic services. In this paper, we investigate the uplink access procedure, how different advances in this procedure enhance delivery of haptic communication, and how it affects the remainder of traffic services in the network. We model this impact as the remainder of service, using stochastic network calculus. Our results show how best the tradeoff between faster or more resource efficient uplink access can be made depending on the rate of haptic data, which is directly relevant to the application domain of haptic communication.Comment: 8 pages, 14 figures, conference pape

Signal segmentation and denoising algorithm based on energy optimisation

A nonlinear functional is considered in this short communication for time interval segmentation and noise reduction of signals. An efficient algorithm that exploits the signal geometrical properties is proposed to optimise the nonlinear functional for signal smoothing. Discontinuities separating consecutive time intervals of the original signal are initially detected by measuring the curvature and arc length of the smoothed signal. The nonlinear functional is then optimised for each time interval to achieve noise reduction of the original noisy signal. This algorithm exhibits robustness for signals characterised by very low signal to noise ratios

Contour evolution scheme for variational image segmentation and smoothing

An algorithm, based on the Mumford–Shah (M–S) functional, for image contour segmentation and object smoothing in the presence of noise is proposed. However, in the proposed algorithm, contour length minimisation is not required and it is demonstrated that the M–S functional without contour length minimisation becomes an edge detector. Optimisation of this nonlinear functional is based on the method of calculus of variations, which is implemented by using the level set method. Fourier and Legendre’s series are also employed to improve the segmentation performance of the proposed algorithm. The segmentation results clearly demonstrate the effectiveness of the proposed approach for images with low signal-to-noise ratios