EFFECTS OF LOW-DOSE-GAMMA RAYS ON THE IMMUNE SYSTEM OF DIFFERENT ANIMAL MODELS OF DISEASE

We reviewed the beneficial or harmful effects of low-dose ionizing radiation on several diseases based on a search of the literature. The attenuation of autoimmune manifestations in animal disease models irradiated with low-dose γ-rays was previously reported by several research groups, whereas the exacerbation of allergic manifestations was described by others. Based on a detailed examination of the literature, we divided animal disease models into two groups: one group consisting of collagen-induced arthritis (CIA), experimental encephalomyelitis (EAE), and systemic lupus erythematosus, the pathologies of which were attenuated by low-dose irradiation, and another group consisting of atopic dermatitis, asthma, and Hashimoto’s thyroiditis, the pathologies of which were exacerbated by low-dose irradiation. The same biological indicators, such as cytokine levels and Tcell subpopulations, were examined in these studies. Low-dose irradiation reduced interferon (IFN)-gamma (γ) and interleukin (IL)-6 levels and increased IL-5 levels and the percentage of CD4+CD25+Foxp3+Treg cells in almost all immunological disease cases examined. Variations in these biological indicators were attributed to the attenuation or exacerbation of the disease’s manifestation. We concluded that autoimmune diseases caused by autoantibodies were attenuated by low-dose irradiation, whereas diseases caused by antibodies against external antigens, such as atopic dermatitis, were exacerbated

Embedded Vision Systems: A Review of the Literature

Over the past two decades, the use of low power Field Programmable Gate Arrays (FPGA) for the acceleration of various vision systems mainly on embedded devices have become widespread. The reconfigurable and parallel nature of the FPGA opens up new opportunities to speed-up computationally intensive vision and neural algorithms on embedded and portable devices. This paper presents a comprehensive review of embedded vision algorithms and applications over the past decade. The review will discuss vision based systems and approaches, and how they have been implemented on embedded devices. Topics covered include image acquisition, preprocessing, object detection and tracking, recognition as well as high-level classification. This is followed by an outline of the advantages and disadvantages of the various embedded implementations. Finally, an overview of the challenges in the field and future research trends are presented. This review is expected to serve as a tutorial and reference source for embedded computer vision systems

Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states

<p>Abstract</p> <p>Background</p> <p>Computational analysis of tissue structure reveals sub-visual differences in tissue functional states by extracting quantitative signature features that establish a diagnostic profile. Incomplete and/or inaccurate profiles contribute to misdiagnosis.</p> <p>Methods</p> <p>In order to create more complete tissue structure profiles, we adapted our cell-graph method for extracting quantitative features from histopathology images to now capture temporospatial traits of three-dimensional collagen hydrogel cell cultures. Cell-graphs were proposed to characterize the spatial organization between the cells in tissues by exploiting graph theory wherein the nuclei of the cells constitute the <it>nodes </it>and the approximate adjacency of cells are represented with <it>edges</it>. We chose 11 different cell types representing non-tumorigenic, pre-cancerous, and malignant states from multiple tissue origins.</p> <p>Results</p> <p>We built cell-graphs from the cellular hydrogel images and computed a large set of features describing the structural characteristics captured by the graphs over time. Using three-mode tensor analysis, we identified the five most significant features (metrics) that capture the compactness, clustering, and spatial uniformity of the 3D architectural changes for each cell type throughout the time course. Importantly, four of these metrics are also the discriminative features for our histopathology data from our previous studies.</p> <p>Conclusions</p> <p>Together, these descriptive metrics provide rigorous quantitative representations of image information that other image analysis methods do not. Examining the changes in these five metrics allowed us to easily discriminate between all 11 cell types, whereas differences from visual examination of the images are not as apparent. These results demonstrate that application of the cell-graph technique to 3D image data yields discriminative metrics that have the potential to improve the accuracy of image-based tissue profiles, and thus improve the detection and diagnosis of disease.</p