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The family Adenoviridae consists of five genera, including the genus Aviadenovirus, which infects avian species. The genus Aviadenovirus currently comprises five fowl (Fowl adenovirus A-E), one falcon (Falcon adenovirus A), and one goose (Goose adenovirus) adenovirus species. Fowl adenoviruses (FAdVs) have a worldwide distribution. Some are associated with diseases such as inclusion body hepatitis (IBH), while FAdV species C serotype 4 (FAdV-4) has been associated with hydropericardium-hepatitis syndrome (HHS). In this study, the complete nucleotide sequence of fowl adenovirus serotype 8 (FAdV-8) was determined. The full genome was 44,055 nucleotides (nt) in length, with an organization similar to that of the FAdV-1 and FAdV-9 genomes. No regions homologous to early regions E1, E3, and E4 of mastadenoviruses were recognized Pathogenicity of FAdV-8 and FAdV-4 were studied in specific-pathogen-free chickens following oral and intramuscular inoculations. Pathogenicity was determined on the basis of clinical signs and gross and histological lesions. Additionally, virus shedding and viral genome copy numbers in liver, cecal tonsil, and bursa of Fabricius were determined. The role of interleukins (IL) in the pathogenicity of and immune response to FAdVs is unknown. Therefore, in a chicken experiment, interferon-γ, IL-10, IL-18, and IL-8 gene expression was evaluated following FAdV-8 and FAdV-4 infection. Cytokine gene expression was examined in the liver, spleen, and cecal tonsils. This study explored the ability of fowl adenoviruses to subvert the host cell’s secretion of cytokines in response to infection as an important viral mechanism for immune evasion during infection. Variations in virulence of FAdVs are likely to be determined by the fiber alone as shown by Pallister et al. (1996). Therefore, we compared and analyzed the nt and amino acid (aa) sequences of the fiber gene of pathogenic and non-pathogenic FAdVs representing species groups D (FAdV-11) and E (FAdV-8). According to our data, virulence might not be associated only with sequence of the fiber gene. This work is a continuation of our efforts towards better understanding of the molecular biology of FAdVs and the pathogenesis of the disease, with an emphasis on the role of interleukins, an unknown area.

Mathematical and statistical modeling and analysis of biological growth using images collected over time are important for understanding of normal and abnormal development. In computational anatomy, changes in the shape of a growing anatomical structure have been modeled by means of diffeomorphic transformations in the background coordinate space. Various image and landmark matching algorithms have been developed for inference of large transformations that perform image registration consistent with the material properties of brain anatomy under study. However, from a biological perspective, it is not material constants that regulate growth, it is the genetic control system. A pattern theoretic model called the Growth as Random Iterated Diffeomorphisims (GRID) introduced by Ulf Grenander (Brown University) constructs growth-induced transformations according to fundamental biological principles of growth. They are governed by an underlying genetic control that is expressed in terms of probability laws governing the spatial-temporal patterns of elementary cell decisions (e.g., cell division/death). This thesis addresses computational and stochastic aspects of the GRID model and develops its application to image analysis of growth. The first part of the thesis introduces the original GRID view of growth-induced deformation on a fine time scale as a composition of several, elementary, local deformations each resulting from a random cell decision, a highly localized event in space-time called a seed. A formalization of the proposed model using theory of stochastic processes is presented, namely, an approximation of the GRID model by the diffusion process and the Fokker-Planck equation describing the evolution of the probability density of seed trajectories in space-time. Its time-dependent and stationary numerical solutions reveal bimodal distribution of a random seed trajectory in space-time. The second part of the thesis considers the growth pattern on a coarse time scale which underlies visible shape changes seen in images. It is shown that such a "macroscopic" growth pattern is a solution to a deterministic integro-differential equation in the form of a diffeomorphic flow dependent on the GRID growth variables such as the probability density of cell decisions and the rate of contraction/expansion. Since the GRID variables are unobserved, they have to be estimated from image data. Using the GRID macroscopic growth equation such an estimation problem is formulated as an optimal control problem. The estimated GRID variables are optimal controls that force the image of an initial organism to be continuously transformed into the image of a grown organism. The GRID-based inference method is implemented for inference of growth properties of the Drosophila wing disc directly from confocal micrographs of Wingless gene expression patterns.

Mathematical and statistical modeling and analysis of biological growth using images collected over time are important for understanding of normal and abnormal development. In computational anatomy, changes in the shape of a growing anatomical structure have been modeled by means of diffeomorphic transformations in the background coordinate space. Various image and landmark matching algorithms have been developed for inference of large transformations that perform image registration consistent with the material properties of brain anatomy under study. However, from a biological perspective, it is not material constants that regulate growth, it is the genetic control system. A pattern theoretic model called the Growth as Random Iterated Diffeomorphisims (GRID) introduced by Ulf Grenander (Brown University) constructs growth-induced transformations according to fundamental biological principles of growth. They are governed by an underlying genetic control that is expressed in terms of probability laws governing the spatial-temporal patterns of elementary cell decisions (e.g., cell division/death). This thesis addresses computational and stochastic aspects of the GRID model and develops its application to image analysis of growth. The first part of the thesis introduces the original GRID view of growth-induced deformation on a fine time scale as a composition of several, elementary, local deformations each resulting from a random cell decision, a highly localized event in space-time called a seed. A formalization of the proposed model using theory of stochastic processes is presented, namely, an approximation of the GRID model by the diffusion process and the Fokker-Planck equation describing the evolution of the probability density of seed trajectories in space-time. Its time-dependent and stationary numerical solutions reveal bimodal distribution of a random seed trajectory in space-time. The second part of the thesis considers the growth pattern on a coarse time scale which underlies visible shape changes seen in images. It is shown that such a "macroscopic" growth pattern is a solution to a deterministic integro-differential equation in the form of a diffeomorphic flow dependent on the GRID growth variables such as the probability density of cell decisions and the rate of contraction/expansion. Since the GRID variables are unobserved, they have to be estimated from image data. Using the GRID macroscopic growth equation such an estimation problem is formulated as an optimal control problem. The estimated GRID variables are optimal controls that force the image of an initial organism to be continuously transformed into the image of a grown organism. The GRID-based inference method is implemented for inference of growth properties of the Drosophila wing disc directly from confocal micrographs of Wingless gene expression patterns.

My dissertation hypothesis is that bovine trophoblast cells express cell-surface and secreted non-classical major histocompatibility complex class I (MHC-Ib) proteins which inhibit NK cells and other leukocytes by binding to inhibitory receptors (e.g., LILRB1, LILRB2, KIR2DL4, and/or CD94/NKG2A). Extremely polymorphic and ubiquitously expressed classical MHC class I (MHC-Ia) proteins, which present foreign antigenic peptides to CD8+ T lymphocytes, are involved in acceptance or rejection of tissue grafts. Non-classical MHC class I (MHC-Ib) glycoproteins, such as Human Leukocyte Antigen-G (HLA-G) and murine Qa-2, are important modulators of the maternal immune system during pregnancy. MHC-Ib proteins are: (a) oligomorphic or monomorphic, (b) expressed in specific tissues under specific condtions, and (c) produced as surface and/or soluble isoforms due to alternative splicing. Third trimester-bovine trophoblast cells express both MHC-Ia and MHC-Ib proteins. The MHC-Ib proteins expressed by trophoblast cells during the third trimester of pregnancy are encoded by four bovine leukocyte antigen (BoLA) loci: BoLA-NC1, BoLA-NC2, BoLA-NC3, and BoLA-NC4. Two MHC-Ia (N*01701 and N*01802) and three MHC-Ib (NC1*00501, NC3*00101 and NC4*00201) proteins showed cell-surface expression in transfection studies performed in murine P815 and human K562 cells. Two additional isoforms, NC1*00401 and NC2*00102, were not detected on the surface of these cells. Nevertheless, both class Ia proteins, N*01701 and N*01802, and five class Ib proteins, NC1*00401, NC1*00501, NC2*00102, NC3*00101, and NC4*00201, were detected in crude cell lysates on Western blots. Precipitation of proteins from culture supernatants showed that cell-surface MHC-Ia (N*01701 and N*01802) and MHC-Ib proteins (NC1*00501, NC3*00101, and NC4*00201) are shed from the surface of these cells into the media. The mechanism of shedding of these proteins is, however, not known. Monoclonal antibodies W6/32, IL-A88, H1A, H6A, H11A, H58A, and PT-85A recognized surface MHC-I isoforms with varying affinity. We were able to develop a sandwich enzyme-linked immunosorbent assay (ELISA) using either H1A or IL-A88 antibody as the capture antibody and the W6/32 antibody for detection. We produced monoclonal antibodies against cattle NC1*00501 and NC3*00101 proteins. One monoclonal antibody generated against BoLA-NC3*00101 was highly specific. Unfortunately, due to failure to clone the NC3*00101- hybridoma, we no longer have an infinite source of this monoclonal antibody for NC3*00101. We eluted peptides from NC3*00101-transfected MHC-null K562 cells and identified peptides using liquid chromatography-mass spectrum (LC-MS) analysis. Analysis of peptide binding data using the SAS Proc mixed statistical program, suggested that the peptide EVTNQLVVL is a potential peptide ligand, which can be used to make tetramers for enumeration of antigen-specific leukocytes.

A possibilidade de armazenamento de imagens no formato digital favoreceu a evolução de diversos ramos de atividades, especialmente as áreas de pesquisa e clínica médica. Ao mesmo tempo, o volume crescente de imagens armazenadas deu origem a um problema de relevância e complexidade consideráveis: a Recuperação de Imagens Baseada em Conteúdo, que, em outras palavras, diz respeito à capacidade de um sistema de armazenamento processar operações de consulta de imagens a partir de características visuais, extraídas automaticamente por meio de métodos computacionais. Das principais questões que constituem este problema, amplamente conhecido pelo termo CBIR - Content-Based Image Retrieval, fazem parte as seguintes: Como interpretar ou representar matematicamente o conteúdo de uma imagem? Quais medidas que podem caracterizar adequadamente este conteúdo? Como recuperar imagens de um grande repositório utilizando o conteúdo extraído? Como estabelecer um critério matemático de similaridade entre estas imagens? O trabalho desenvolvido e apresentado nesta tese busca, exatamente, responder perguntas deste tipo, especialmente para os domínios de imagens médicas e da biologia genética, onde a demanda por sistemas computacionais que incorporam técnicas CBIR é consideravelmente alta por diversos motivos. Motivos que vão desde a necessidade de se buscar informação visual que estava até então inacessível pela falta de anotações textuais, até o interesse em poder contar com auxílio computacional confiável para a importante tarefa de diagnóstico clínico. Neste trabalho são propostos métodos e soluções inovadoras para o problema de segmentação e extração de características de imagens médicas e imagens de padrões espaciais de expressão genética. A segmentação é o processo de delimitação automático de regiões de interesse da imagem que possibilita uma caracterização bem mais coerente do conteúdo visual, comparado com as tradicionais técnicas de caracterização global e direta da imagem. Partindo desta idéia, as técnicas de extração de características desenvolvidas neste trabalho empregam métodos adaptativos de segmentação de imagens e alcançam resultados excelentes na tarefa de recuperação baseada em conteúdo / Storing images in digital format has supported the evolution of several branches of activities, specially the research area and medical clinic. At the same time, the increasing volume of stored images has originated a topic of considerable relevance and complexity: the Content- Based Imagem Retrieval, which, in other works, is related to the ability of a computational system in processing image queries based on visual features automatically extracted by computational methods. Among the main questions that constitute this issue, widely known as CBIR, are these: How to mathematically express image content? What measures can suitably characterize this content? How to retrieve images from a large dataset employing the extracted content? How to establish a mathematical criterion of similarity among the imagens? The work developed and presented in this thesis aims at answering questions like those, especially for the medical images domain and genetical biology, where the demand for computational systems that embody CBIR techniques is considerably high for several reasons. Reasons that range from the need for retrieving visual information that was until then inaccessible due to the lack of textual annotations, until the interest in having liable computational support for the important task of clinical diagnosis. In this work are proposed innovative methods and solutions for the problem of image segmentation and feature extraction of medical images and images of gene expression patterns. Segmentation is the process that enables a more coherent representation of image?s visual content than that provided by traditional methods of global and direct representation. Grounded in such idea, the feature extraction techniques developed in this work employ adaptive image segmentation methods, and achieve excellent results on the task of Content-Based Image Retrieval

A possibilidade de armazenamento de imagens no formato digital favoreceu a evolução de diversos ramos de atividades, especialmente as áreas de pesquisa e clínica médica. Ao mesmo tempo, o volume crescente de imagens armazenadas deu origem a um problema de relevância e complexidade consideráveis: a Recuperação de Imagens Baseada em Conteúdo, que, em outras palavras, diz respeito à capacidade de um sistema de armazenamento processar operações de consulta de imagens a partir de características visuais, extraídas automaticamente por meio de métodos computacionais. Das principais questões que constituem este problema, amplamente conhecido pelo termo CBIR - Content-Based Image Retrieval, fazem parte as seguintes: Como interpretar ou representar matematicamente o conteúdo de uma imagem? Quais medidas que podem caracterizar adequadamente este conteúdo? Como recuperar imagens de um grande repositório utilizando o conteúdo extraído? Como estabelecer um critério matemático de similaridade entre estas imagens? O trabalho desenvolvido e apresentado nesta tese busca, exatamente, responder perguntas deste tipo, especialmente para os domínios de imagens médicas e da biologia genética, onde a demanda por sistemas computacionais que incorporam técnicas CBIR é consideravelmente alta por diversos motivos. Motivos que vão desde a necessidade de se buscar informação visual que estava até então inacessível pela falta de anotações textuais, até o interesse em poder contar com auxílio computacional confiável para a importante tarefa de diagnóstico clínico. Neste trabalho são propostos métodos e soluções inovadoras para o problema de segmentação e extração de características de imagens médicas e imagens de padrões espaciais de expressão genética. A segmentação é o processo de delimitação automático de regiões de interesse da imagem que possibilita uma caracterização bem mais coerente do conteúdo visual, comparado com as tradicionais técnicas de caracterização global e direta da imagem. Partindo desta idéia, as técnicas de extração de características desenvolvidas neste trabalho empregam métodos adaptativos de segmentação de imagens e alcançam resultados excelentes na tarefa de recuperação baseada em conteúdo / Storing images in digital format has supported the evolution of several branches of activities, specially the research area and medical clinic. At the same time, the increasing volume of stored images has originated a topic of considerable relevance and complexity: the Content- Based Imagem Retrieval, which, in other works, is related to the ability of a computational system in processing image queries based on visual features automatically extracted by computational methods. Among the main questions that constitute this issue, widely known as CBIR, are these: How to mathematically express image content? What measures can suitably characterize this content? How to retrieve images from a large dataset employing the extracted content? How to establish a mathematical criterion of similarity among the imagens? The work developed and presented in this thesis aims at answering questions like those, especially for the medical images domain and genetical biology, where the demand for computational systems that embody CBIR techniques is considerably high for several reasons. Reasons that range from the need for retrieving visual information that was until then inaccessible due to the lack of textual annotations, until the interest in having liable computational support for the important task of clinical diagnosis. In this work are proposed innovative methods and solutions for the problem of image segmentation and feature extraction of medical images and images of gene expression patterns. Segmentation is the process that enables a more coherent representation of image?s visual content than that provided by traditional methods of global and direct representation. Grounded in such idea, the feature extraction techniques developed in this work employ adaptive image segmentation methods, and achieve excellent results on the task of Content-Based Image Retrieval