An immunomodulating mycotoxin interferes with the development of autoimmune diabetes in diabetes-prone BB/Wor rats

Various fungal products have immunomodulating activity and some have been studied regarding prevention of transplantation rejection. Prior to this investigation, the mycotoxin, gliotoxin (GT), has never been investigated as an immunotherapeutic drug for autoimmune disease. GT is a fungal secondary metabolite and a member of the epipolythiodioxopiperazine (ETP) family which has been shown to inhibit phagocytosis, induction of cytolytic T cells and the proliferation of T cells following mitogen stimulation. GT also induces in vitro apoptosis in certain immune cell types. More importantly, GT exhibits selective activity towards cells of hemopoietic origin. Autoimmune diseases are disorders caused by immune responses to self antigens. Insulin dependent diabetes mellitus (IDDM) is an organ-specific autoimmune disease in which insulin secreting pancreatic islet β-cells are destroyed leading to hyperglycemia, ketoacidosis and various systemic complications. Because of its potential effects on the immune system, we evaluated GT for its ability to prevent IDDM. This study is the first to successfully use GT to prevent an autoimmune process. GT prevented IDDM in spontaneously diabetic DP/BB rats without causing significant adverse effects among the treated animals. GT treated rats developed diabetes at a rate of 55% by 120 days of age compared to 90% for control rats. GT treatment also significantly decreased serum glucose levels from an average 278 mg/dl to 185.67 mg/dl among non-diabetic/pre-diabetic animals. A series of studies was conducted on 65 days old DP/BB rats, prior to development of diabetes to phenotypically characterize the splenic lymphocytes recovered from animals chronically treated with GT. A parallel study examined the direct effects of GT on splenocyte preparations incubated with this mycotoxin. This study found that GT selectively affects certain lymphocyte subsets. Animals treated with GT showed involution of splenic follicles and several effects on lymphocyte subpopulations were found. In vitro treatment of splenocytes with GT revealed decreased CD4+ and increased CD8+ T cell subsets. CD8+ T cells function as an important regulator of autoimmunity, especially influencing the activity of CD4+ T cells. GT effects on CD4+ and CD8+ T cells are consistent with changes anticipated to inhibit IDDM pathogenesis. In vivo treatment with GT did not result in detectable alterations in relative CD4+ and CD8+ cell subsets, although this may have been more related to pharmacologic reasons than the physiological effects of GT. Importantly, this study found that both in vitro and in vivo GT treatments significantly enhanced the detectable RT6 surface marker. The RT6+ T cell subset is a key regulatory element in IDDM pathogenesis. Increased numbers of RT6 surface markers may be involved with IDDM prevention or may be a result of it. GT induced lymphocyte apoptosis among spleen cells from DP/BB rats was altered in vitro. The average increase in apoptotic cells due to GT treatment was nearly four fold. Results from this study suggested that the mechanism whereby GT prevents IDDM in DP/BB rats is through apoptosis. Coupled with the finding of altered lymphocyte populations, it may be suggested that apoptosis of regulatory cells, or effector cells is involved in diabetes prevention in this system. The finding that CD8+ cells and NK cells which include cytotoxic effectors that can promote pancreatic damage, were not decreased by GT treatment suggests that the effects may reside with regulatory cells rather than with effectors, although additional study is warranted to fully understand this process. This research is the first to show that GT has a protective effect against an autoimmune disease. We also found that GT is a selective immunomodulator altering the ratio of CD4+ and CD8+ lymphocytes and causing increased RT6+ surface marker to appear as an important subset of lymphocytes. This study is also the first to demonstrate that apoptosis due to GT treatment occurs in intact animals. Because indicators of systemic toxicity showed that GT is relatively benign in experimental animals, as evidenced by lack of irreversible histopathology, normal weight gain and normal leukocyte counts, and it has a beneficial effect on IDDM development, GT should be considered for continued evaluation as a potential IDDM preventive drug

A Game-theoretic Framework for Revenue Sharing in Edge-Cloud Computing System

We introduce a game-theoretic framework to ex- plore revenue sharing in an Edge-Cloud computing system, in which computing service providers at the edge of the Internet (edge providers) and computing service providers at the cloud (cloud providers) co-exist and collectively provide computing resources to clients (e.g., end users or applications) at the edge. Different from traditional cloud computing, the providers in an Edge-Cloud system are independent and self-interested. To achieve high system-level efficiency, the manager of the system adopts a task distribution mechanism to maximize the total revenue received from clients and also adopts a revenue sharing mechanism to split the received revenue among computing servers (and hence service providers). Under those system-level mechanisms, service providers attempt to game with the system in order to maximize their own utilities, by strategically allocating their resources (e.g., computing servers). Our framework models the competition among the providers in an Edge-Cloud system as a non-cooperative game. Our simulations and experiments on an emulation system have shown the existence of Nash equilibrium in such a game. We find that revenue sharing mechanisms have a significant impact on the system-level efficiency at Nash equilibria, and surprisingly the revenue sharing mechanism based directly on actual contributions can result in significantly worse system efficiency than Shapley value sharing mechanism and Ortmann proportional sharing mechanism. Our framework provides an effective economics approach to understanding and designing efficient Edge-Cloud computing systems

A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video

Video post-processing methods can improve the quality of compressed videos at the decoder side. Most of the existing methods need to train corresponding models for compressed videos with different quantization parameters to improve the quality of compressed videos. However, in most cases, the quantization parameters of the decoded video are unknown. This makes existing methods have their limitations in improving video quality. To tackle this problem, this work proposes a diffusion model based post-processing method for compressed videos. The proposed method first estimates the feature vectors of the compressed video and then uses the estimated feature vectors as the prior information for the quality enhancement model to adaptively enhance the quality of compressed video with different quantization parameters. Experimental results show that the quality enhancement results of our proposed method on mixed datasets are superior to existing methods.Comment: 10 pages, conferenc

Deep Learning with Long Short-Term Memory for Time Series Prediction

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a specific kind of scheme in deep learning, promise to effectively overcome the problem. In this article, we first give a brief introduction to the structure and forward propagation mechanism of the LSTM model. Then, aiming at reducing the considerable computing cost of LSTM, we put forward the Random Connectivity LSTM (RCLSTM) model and test it by predicting traffic and user mobility in telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic connectivity between neurons, which achieves a significant breakthrough in the architecture formation of neural networks. In this way, the RCLSTM model exhibits a certain level of sparsity, which leads to an appealing decrease in the computational complexity and makes the RCLSTM model become more applicable in latency-stringent application scenarios. In the field of telecommunication networks, the prediction of traffic series and mobility traces could directly benefit from this improvement as we further demonstrate that the prediction accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference

Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data. However, LSTM has considerable computational cost, which can not be tolerated in tasks with stringent latency requirement. In this paper, we propose a deep learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected. So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost. We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows a satisfactory performance. When we gradually add training samples, the performance of RCLSTM becomes increasingly closer to the baseline LSTM. Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure

R2P: A Deep Learning Model from mmWave Radar to Point Cloud

Recent research has shown the effectiveness of mmWave radar sensing for object detection in low visibility environments, which makes it an ideal technique in autonomous navigation systems. In this paper, we introduce Radar to Point Cloud (R2P), a deep learning model that generates smooth, dense, and highly accurate point cloud representation of a 3D object with fine geometry details, based on rough and sparse point clouds with incorrect points obtained from mmWave radar. These input point clouds are converted from the 2D depth images that are generated from raw mmWave radar sensor data, characterized by inconsistency, and orientation and shape errors. R2P utilizes an architecture of two sequential deep learning encoder-decoder blocks to extract the essential features of those radar-based input point clouds of an object when observed from multiple viewpoints, and to ensure the internal consistency of a generated output point cloud and its accurate and detailed shape reconstruction of the original object. We implement R2P to replace Stage 2 of our recently proposed 3DRIMR (3D Reconstruction and Imaging via mmWave Radar) system. Our experiments demonstrate the significant performance improvement of R2P over the popular existing methods such as PointNet, PCN, and the original 3DRIMR design.Comment: arXiv admin note: text overlap with arXiv:2109.0918