This thesis proposes a mechanism for the induction of peripheral tolerance to protein antigens. I have investigated the mechanism of tolerance induction to soluble protein antigens by targeting an antigen to small, resting B cells. For this purpose I have used a rabbit antibody directed at the IgD molecule found on the surface of most small, resting B cells but missing or lowered on activated B cells. Intravenous injection of normal mice with 100 μg of an ultracentrifuged Fab fragment of rabbit anti-mouse IgD (Fab anti-δ) makes these mice profoundly tolerant to challenge with nonimmune rabbit Fab (Fab NRG) fragments. This tolerance is antigen specific since treated mice make normal responses to an irrelevant antigen, chicken immunoglobulin (Ig). Fab fragments of rabbit Ig (rabbit Fab) not targeted to B cells do not induce tolerance as well as Fab anti-δ. Evidence suggests that the B cells must remain in a resting state for tolerance to be induced, since injection of F(ab)'2 anti-δ does not induce tolerance. Investigation of the mechanisms of the tolerance, by adoptive transfer, have shown that rabbit Fab specific B cell function has been impaired. The major effect however is in helper T cell function, as shown by adoptive transfer and lack of help for a hapten response. In vitro proliferation experiments show that the T cell response has not been shifted toward activation of different T cell subsets which do not help Ig production, nor is there any change in the Ig isotypes produced. Suppression does not appear to be the major cause of the helper T cell defect as shown by cell mixing experiments. This work shows that an antigen targeted to small B cells can induce tolerance to a soluble protein antigen, and suggests a role for small B cells in tolerance to self-proteins not presented in the thymus.

This thesis deals with various methods used for spam detection and identification. In the introduction various methods are described. Then Bayes' theorem and methods for spam detection that use this theorem are described in detail. This section also discusses biological and artificial immune systems and methods for spam detection based on artificial immune systems. Next sections contain the description of custom spam detection system design and implementation. Finally the system is tested and the results are evaluated.

The natural immune system embodies a wealth of information processing capabilities that can be exploited as a metaphor for the development of artificial immune systems. Chief among these features is the ability to recognize previously encountered substances and to generalize beyond recognition in order to provide appropriate responses to pathogens not seen before. This thesis presents a new supervised learning paradigm, resource limited artificial immune classifiers, inspired by mechanisms exhibited in natural and artificial immune systems. The key abstractions gleaned from these immune systems include resource competition, clonal selection, affinity maturation, and memory cell retention. A discussion of the progenitors of this work is offered. This work provides a thorough explication of a resource limited artifical immune classification algorithm, named AIRS (Artificial Immune Recognition System). Experimental results on both simulated data sets and real world machine learning benchmarks demonstrate the effectiveness of the AIRS algorithm as a classification technique.

Made available in DSpace on 2016-12-23T14:33:37Z (GMT). No. of bitstreams: 1 dissertacao.pdf: 1052399 bytes, checksum: b17ce224ca3822e277c997fd00bd2c67 (MD5) Previous issue date: 2006-11-29 / Este trabalho apresenta um Sistema Imune Artificial (SIA) para tratar problemas de escalonamento. O Sistema Imunológico Artificial desenvolvido neste projeto baseia-se na estrutura arquitetura e funcionamento dos Sistemas Imunes Biológicos ou Naturais. O uso de Algoritmo Genético (AG) fez-se necessário para gerar os indivíduos a serem escalonados, representando os antígenos e anticorpos do SIA. Cada indivíduo gerado pelo AG representa um conjunto de tarefas processadas em um conjunto de máquinas. Os indivíduos são avaliados por uma função de aptidão que representa o processo de seleção natural. A evolução dos indivíduos e consequentemente das populações são obtidas aplicando-se os operadores genéticos de crossover e mutação. As tarefas e as máquinas, utilizadas para o escalonamento, representa o problema de Job Shop Scheduling (JSS). Ao problema, foram aplicados alguns testes clássicos da literatura, onde se verificou a viabilidade dos SIA para tratamento de problemas de escalonamento. Ainda com os testes, pode-se observar o comportamento do sistema durante toda a execução, possibilitando assim, uma análise criteriosa das funcionalidades do sistema e dos resultados gerados pela massa de teste, observados durante um período de tempo. A representação dos sistemas imunológicos naturais através de algoritmos computacionais tem inspirado pesquisadores de todo o mundo, a motivação é que os sistemas imunológicos possuem características de paralelismo adaptabilidade e aprendizagem, além da possibilidade de serem aplicados em diversos problemas das mais diversas áreas, devido sua portabilid ade. / This work presents an Artificial Immune System (AIS) to deal with problems scheduling. The Artificial Immunologic System developed in this project was based on the structure, architecture and functioning of the Biological or Natural Immune Systems. The use of Genetic Algorithm (GA) became necessary to represent the antibodies and antigens of the AIS. Each individual generated for the GA represented a processed task set library in a set of machines. The evaluation of each individual was given by a fitness function that represents the process of natural selection. The evolution of the individuals, and population as a consequence was obtained by applying the genetic operators of crossover e mutation. The machines and the tasks used for the scheduling represent the problem of Job Shop Scheduling (JSS). Some classic tests of the literature where applied to the problem in order to verify the viability of the AIS on the treatment of task of scheduling problems. Those tests also demonstrated the system s behavior its entire execution, therefore, allowing for a detailed analysis of the system s functionalities sets for certain time period. The representation of the natural immunologic systems through computational algorithms inspires from all over world researchers. The motivation is that the immunologic systems possess parallelism characteristics adaptability and learning, which can be applied in several problems found in many areas, had its portability.

Made available in DSpace on 2016-12-23T13:44:55Z (GMT). No. of bitstreams: 1 dissertacao.pdf: 1052399 bytes, checksum: b17ce224ca3822e277c997fd00bd2c67 (MD5) Previous issue date: 2007-03-23 / This work presents an Artificial Immune System (AIS) to deal with problems scheduling. The Artificial Immunologic System developed in this project was based on the structure,architecture and functioning of the Biological or Natural Immune Systems. The use of Genetic Algorithm (GA) became necessary to represent the antibodies and antigens of the AIS. Each individual generated for the GA represented a processed task set library in a set of machines. The evaluation of each individual was given by a fitness function that represents the process of natural selection. The evolution of the individuals, and population as a consequence was obtained by applying the genetic operators of crossover e mutation. The machines and the tasks used for the scheduling represent the problem of Job Shop Scheduling (JSS). Some classic tests of the literature where applied to the problem in order to verify the viability of the AIS on the treatment of task of scheduling problems. Those tests also demonstrated the system s behavior its entire execution, therefore, allowing for a detailed analysis of the system s functionalities sets for certain time period. The representation of the natural immunologic systems through computational algorithms inspires from all over world researchers. The motivation is that the immunologic systems possess parallelism characteristics adaptability and learning, which can be applied in several problems found in many areas, had its portability. / Este trabalho apresenta um Sistema Imune Artificial (SIA) para tratar problemas de escalonamento. O Sistema Imunológico Artificial desenvolvido neste projeto baseia-se na estrutura arquitetura e funcionamento dos Sistemas Imunes Biológicos ou Naturais. O uso de Algoritmo Genético (AG) fez-se necessário para gerar os indivíduos a serem escalonados, representando os antígenos e anticorpos do SIA. Cada indivíduo gerado pelo AG representa um conjunto de tarefas processadas em um conjunto de máquinas. Os indivíduos são avaliados por uma função de aptidão que representa o processo de seleção natural. A evolução dos indivíduos e consequentemente das populações são obtidas aplicando-se os operadores genéticos de crossover e mutação. As tarefas e as máquinas, utilizadas para o escalonamento, representa o problema de Job Shop Scheduling (JSS). Ao problema, foram aplicados alguns testes clássicos da literatura, onde se verificou a viabilidade dos SIA para tratamento de problemas de escalonamento. Ainda com os testes, pode-se observar o comportamento do sistema durante toda a execução, possibilitando assim, uma análise criteriosa das funcionalidades do sistema e dos resultados gerados pela massa de teste, observados durante um período de tempo. A representação dos sistemas imunológicos naturais através de algoritmos computacionais tem inspirado pesquisadores de todo o mundo, a motivação é que os sistemas imunológicos possuem características de paralelismo adaptabilidade e aprendizagem, além da possibilidade de serem aplicados em diversos problemas das mais diversas áreas, devido sua portabilidade.

A geração de grades horárias de qualidade é um fator crítico em qualquer instituição de ensino, tanto em escolas de ensino fundamental/médio como em universidades. Este problema é considerado complexo, pois devem ser relacionados e otimizados diversos recursos, tais como horários, disciplinas, professores e alunos. Em grande parte das instituições de ensino, a geração de grades horárias é realizada manualmente, o que vem a tornar este processo custoso e sujeito a falhas. Diversas abordagens são também encontradas na literatura para resolução deste problema, nas quais foram aplicados métodos de busca estocástica, devido à sua inerente complexidade. As estratégias de busca formuladas e comparadas no presente trabalho foram baseadas no uso de algoritmos genéticos e de sistemas imunológicos artificiais. Tais técnicas foram capazes de fornecer soluções de qualidade para o problema de geração automática de grades horárias. Neste trabalho foram desenvolvidos dois sistemas de apoio à decisão, nos quais foram combinadas técnicas heurísticas aos algoritmos genéticos e ao algoritmo de seleção clonal. O propósito desta investigação é realizar uma análise comparativa entre as duas técnicas a fim de verificar qual delas apresenta resultados mais promissores para a resolução do problema de geração automática de grades horárias. / The generation of timetables with good quality is a critical factor in any educational institution. This is considered a complex problem because it involves several types of information, such as schedules, course subjects, teachers and students. Several search strategies have been applied to solve timetabling problems, whose constraints may vary from one educational institution to another. Most educational institutions still prepare their timetables manually, which is a highly time-consuming process and subjected to errors. Several approaches to solve this problem are also found in technical studies, which use stochastic search methods due to the problems complexity. The search optimization methods used in this work to solve the timetabling problem are genetic algorithms and the clonal selection algorithm, whose satisfactory results when applied to optimization problems are reported in the literature. Two decision support systems were developed in this work, combining heuristic techniques with the genetic algorithms and the clonal selection algorithm. The purpose of this research is to make a comparative analysis of these two techniques in order to determine which one offers the most promising results for solving the timetabling problem.

A geração de grades horárias de qualidade é um fator crítico em qualquer instituição de ensino, tanto em escolas de ensino fundamental/médio como em universidades. Este problema é considerado complexo, pois devem ser relacionados e otimizados diversos recursos, tais como horários, disciplinas, professores e alunos. Em grande parte das instituições de ensino, a geração de grades horárias é realizada manualmente, o que vem a tornar este processo custoso e sujeito a falhas. Diversas abordagens são também encontradas na literatura para resolução deste problema, nas quais foram aplicados métodos de busca estocástica, devido à sua inerente complexidade. As estratégias de busca formuladas e comparadas no presente trabalho foram baseadas no uso de algoritmos genéticos e de sistemas imunológicos artificiais. Tais técnicas foram capazes de fornecer soluções de qualidade para o problema de geração automática de grades horárias. Neste trabalho foram desenvolvidos dois sistemas de apoio à decisão, nos quais foram combinadas técnicas heurísticas aos algoritmos genéticos e ao algoritmo de seleção clonal. O propósito desta investigação é realizar uma análise comparativa entre as duas técnicas a fim de verificar qual delas apresenta resultados mais promissores para a resolução do problema de geração automática de grades horárias. / The generation of timetables with good quality is a critical factor in any educational institution. This is considered a complex problem because it involves several types of information, such as schedules, course subjects, teachers and students. Several search strategies have been applied to solve timetabling problems, whose constraints may vary from one educational institution to another. Most educational institutions still prepare their timetables manually, which is a highly time-consuming process and subjected to errors. Several approaches to solve this problem are also found in technical studies, which use stochastic search methods due to the problems complexity. The search optimization methods used in this work to solve the timetabling problem are genetic algorithms and the clonal selection algorithm, whose satisfactory results when applied to optimization problems are reported in the literature. Two decision support systems were developed in this work, combining heuristic techniques with the genetic algorithms and the clonal selection algorithm. The purpose of this research is to make a comparative analysis of these two techniques in order to determine which one offers the most promising results for solving the timetabling problem.

The immune system is a complex network of tissues, cells and proteins which protects us against infections and invading pathogens we encounter every day. Immunosenescence refers to age-related impairments in immune function which may contribute to increased prevalence and severity of infectious disease in the elderly. How and why ageing affects the immune system is not fully understood. Using a naturally aged mouse model, work in this thesis shows that the abundance of a rare type of lymphocyte, known as NKT cells, increased across multiple immune organs. Additionally, macrophage abundance was also altered in the lymph nodes of aged mice. Invariant NKT (iNKT) cells express an invariant T cell receptor (TCR) which recognises lipids presented on the CD1d molecule. iNKT cells can be activated and respond to invading pathogens either by recognition of antigens through TCR-CD1d interactions or cytokine-dependent means. Less is known about NKT-like cells, which also express NK cell-associated surface markers, such as CD49b, but lack an invariant TCR. Data within this thesis show that both iNKT and NKT-like cell populations are abundant in the spleen and liver of aged mice. iNKT and NKT-like cells can be divided into subpopulations based on their expression of surface markers or transcription factors, and data suggests that not all subpopulations of these cells are affected by age equally. For instance, flow cytometry showed that while spleen-derived iNKT cells are significantly increased in aged mice, within the iNKT cell population the percentage representation of CD4+ cells are significantly reduced with age. Additionally, data indicates that both iNKT and NKT-like cells from aged mice show compromised responses to in vitro stimulation compared to young controls. Using bone marrow chimeras, where either young cells are reconstituted within an aged mouse or old cells are reconstituted within a young mouse, provided the opportunity to determine whether the aged environment contributes to this diminished response. Data demonstrates that the aged environment plays at least a partial role in these age-related changes to response to stimulation, however the young environment seems unable to reverse these changes. Macrophages are phagocytes which are found within all organs of the body. Studies in this thesis show that CD169+ macrophages have diminished numbers in the lymph nodes of aged mice, but this did not seem to affect the capture of the model antigen, dextran. Further studies revealed ageing affects macrophage populations differently in the different tissues within the body. For example, macrophage numbers remain constant in the spleen with ageing, but appear to increase in density in the lungs. To conclude, ageing can cause dramatic changes to the numbers and function of different cells of the immune system across multiple organs. Furthering our understanding of the ageing immune system and the underlying mechanisms which cause age-related decline in immune function is important to design strategies to improve the quality of the lives of the elderly.

O presente trabalho investiga um método de detecção de anomalias baseado em sistemas imunológicos artificiais, especificamente em uma técnica de reconhecimento próprio/não-próprio chamada algoritmo de seleção negativa (NSA). Foi utilizado um esquema de representação baseado em hiperesferas com centros e raios variáveis e um modelo capaz de gerar detectores, com esta representação, de forma eficiente. Tal modelo utiliza algoritmos genéticos onde cada gene do cromossomo contém um índice para um ponto de uma distribuição quasi-aleatória que servirá como centro do detector e uma função decodificadora responsável por determinar os raios apropriados. A aptidão do cromossomo é dada por uma estimativa do volume coberto através uma integral de Monte Carlo. Este algoritmo teve seu desempenho verificado em diferentes dimensões e suas limitações levantadas. Com isso, pode-se focar as melhorias no algoritmo, feitas através da implementação de operadores genéticos mais adequados para a representação utilizada, de técnicas de redução do número de pontos do conjunto próprio e de um método de pré-processamento baseado em bitmaps de séries temporais. Avaliações com dados sintéticos e experimentos com dados reais demonstram o bom desempenho do algoritmo proposto e a diminuição do tempo de execução. / This work investigates a novel detection method based on Artificial Immune Systems, specifically on a self/non-self recognition technique called negative selection algorithm (NSA). A representation scheme based on hyperspheres with variable center and radius and a model that is able to generate detectors, based on that representation scheme, have been used. This model employs Genetic Algorithms where each chromosome gene represents an index to a point in a quasi-random distribution, which serves as a detector center, and a decoder function that determines the appropriate radius. The chromosome fitness is given by an estimation of the covered volume, which is calculated through a Monte Carlo integral. This algorithm had its performance evaluated for different dimensions, and more suitable genetic operators for the used representation, techniques of reducing self-points number and a preprocessing method based on bitmap time series have been therefore implemented. Evaluations with synthetic data and experiments with real data demonstrate the performance of the proposed algorithm and the decrease in execution time.

Neonatal mice have immature immune systems with defects in several components of inflammatory, innate, and specific immune responses and develop a preferential T helper type 2 (Th2) response following immunization with many vaccine antigens. Although maternal antibody is the major form of protection from disease in early life when the neonatal immune system is still immature, the presence of maternal antibody also interferes with active immunization, placing infants at risk for severe bacterial and viral infection. Recent studies have suggested that immunizing with DNA plasmids encoding the vaccine antigen of interest is highly efficacious in a variety of adult animal models. However, similar extensive studies have not been conducted in infants. In this dissertation, we examine both the quantitative and qualitative differences between neonatal and adult humoral and cell-mediated immune responses in the presence or absence of maternal antibody. First, we wished to determine if one-day-old neonatal mice immunized with plasmid DNA expressing influenza A/PR/8/34 hemagglutinin (HA) by either intramuscular (i.m.) or gene gun (g.g.) inoculation were capable of generating humoral responses comparable to those in mice immunized as adults. We found that newborn mice developed stable, long-lived, protective anti-HA-specific IgG responses similar in titer to those of adult DNA-immunized mice. However, unlike the adult i.m. and g.g. DNA immunizations, which develop polarized IgG2a and IgG1 responses, respectively, mice immunized as neonates developed a variety of IgG1-, IgG2a-, and mixed IgG1/IgG2a responses regardless of the inoculation method. Boosting increased, but did not change these antibody profiles. We also found that, in contrast to the DNA immunizations, inoculations of newborn mice with an A/PR/8/34 viral protein subunit preparation failed to elicit an antibody response. Further, temporal studies revealed that both responsiveness to protein vaccination and development of polarized patterns of T help following DNA immunization appeared by 2 weeks of age. To determine if the disparity of polarized IgG responses between neonatal and adult DNA vaccinated mice was due to deficiencies in Th1 promoting cytokines, we addressed the ability of DNA encoding Th1 cytokines to bias the isotype of antibody raised by neonatal DNA immunization. We found that neonatal mice coimmunized with HA and either IL-12 or IFNγ-expressing DNAs developed IgG2a-biased immune responses, regardless of inoculation method, whereas these DNAs had no effect on IgG subtype patterns in adult DNA immunized mice. Consistent with the Th1-promoting effects of these cytokines, we also observed that codelivery of IL-12 or IFNγ DNAs raised T helper responses toward Th1 in mice immunized both as neonates or adults. Thus, codelivery of cytokine DNAs may be effective at tailoring immune responses depending on the required correlates of protection for a given pathogen. Finally, we addressed the effect of maternal antibody on the elicitation of humoral and cell-mediated immune responses. We tested the ability of i.m. and g.g. immunization with DNA expressing influenza HA and/or nucleoprotein (NP) to raise protective humoral and cellular responses in the presence and absence of maternal antibody. We found that neonatal mice born to influenza-immune mothers raised full antibody responses to NP but failed to generate antibody responses to HA. In contrast, the presence of maternal antibody did not affect the generation of long-lived CD4+ and CD8+ T cell responses to both HA and NP. Thus, maternal antibody did not affect cell-mediated responses, but rather it limited humoral responses, with the ability to limit the antibody response correlating with whether the DNA-expressed immunogen was localized in the plasma membrane or within the cell. We further observed that protection from influenza virus challenge was dependent on the presence of anti-HA IgG and was independent of the presence T cell responses. Taken together with other published studies, the data presented in this dissertation help better characterize the responses elicited by DNA vaccines at birth. This dissertation presents several novel observations including the temporal development of polarized IgG subtype responses, the ability of codelivered Th1 cytokine DNA to affect both antibody and T cell responses in the neonate, and the ability to generate humoral responses to intracellular, but not plasma membrane proteins, in the presence of maternal antibody. Furthermore, the data provides rationale for further development of DNA vaccines in the neonate.