In the last seven years a new computational approach has led to promising advances in the understanding of biological visual perception. The foundations of the approach are largely due to the work of a single man, David Marr at M.I.T. Now, after his death in Boston on November 17th 1980, research in vision will not be the same for the growing number of those who are following his lead.

Indivíduos não relacionados apresentam apenas 1% de diferenças entre seus genomas. Estas variações ocorrem na forma de substituições, inserções, deleções, rearranjos complexos ou até estruturais. Dentre essas variações, aquelas que apresentam uma frequência populacional acima de 1% são denominadas de polimorfismos. Tais variações são responsáveis por diferenças que vão desde a resposta imunológica até o tratamento com drogas, incluindo sensitividade das células tumorais, níveis de plasma, efeitos colaterais e toxicidade. A forma mais comum de polimorfismo genético entre humanos são os polimorfismo de base única ou Single Nucleotide Polymorphisms (SNPs), sendo mais de 47 milhões descritos no dbSNP, um banco de dados de pequenos polimorfismos do NCBI. No presente estudo, foi estabelecida uma abordagem computacional, com etapas de exclusão de regiões parálogas ou de baixa qualidade, com o objetivo de identificar variantes genéticas em sequências expressas gerados pelo método de Open Reading Frame ESTs (ORESTES) durante o Projeto Genoma Humano do Câncer. Diferentemente de outros softwares de detecção de polimorfismos, a abordagem computacional descrita neste estudo leva em consideração a informação a priori do número de bibliotecas distintas que reportaram a mesma variação. Foram identificadas 1900 mutações (853 sinônimas e 1047 não-sinônimas) presentes em duas ou mais bibliotecas distintas, que foram validados in-silico contra o dbSNP v130. O resultado da análise identificou 901 mutações já descritas no dbSNP (47,42%). Para confirmação da análise, foram selecionadas 10 mutações (6 novas e 4 já presentes no dbSNP) para validação pelo método de High Resolution Melt (HRM), seguido da caracterização por sequenciamento de DNA. Nesse caso, o resultado foi a validação de 50% das mutações selecionadas. A análise de interação protéica, Protein-Protein Interaction (PPI), realizada com as mutações não-sinônimas localizadas em domínios funcionais, revelou redes gênicas mais complexas em tecidos tumorais do que nos tecidos normais. Esta observação ratificou a literatura a respeito da transformação tumorigênica ser desencadeada pela combinação de mutações que ativam uma série de processos biológicos, para isso, afetando genes, vias gênicas e networks de vias gênicas relacionados. Em resumo, o presente estudo descreve uma abordagem computacional eficiente para identificação de mutações em dados de sequências expressas, além de avaliar o papel das mutações na tumorigênese. / Unrelated humans have only 1% of non-simularity in their genome. These variations occur as substitutions, insertions, deletions, or even complex structural rearrangements. Among these variations, those which show a population frequency above 1% are called polymorphisms. Such variations are responsible for differences ranging from the immune response to treatment with drugs, including sensitivity of tumor cells, plasma levels, toxicity and side effects. The most common form of genetic polymorphism among human are Single Nucleotide Polymorphisms (SNPs), with more than 47 million reported in dbSNP, a database of small polymorphisms from NCBI. In this study, we established a computational approach, with steps to exclude low quality and paralogous regions, aiming to identify genetic variants in expressed sequences generated by the method of Open Reading Frame ESTs (ORESTES) for the Human Cancer Genome Project. Unlike other polymorphisms detection softwares, the computational approach described in this study takes into account the a priori information about the number of different libraries that reported the same variation. We identified 1900 mutations (853 synonymous and 1047 nonsynonymous) present in two or more different libraries, these mutations were in-silico validated against the dbSNP V130. The analysis result showed 901 mutations already described in dbSNP (47.42%). To confirm the analysis, we selected 10 mutations (six new and four already present in dbSNP) for validation by the method of High Resolution Melt (HRM), followed by characterization by DNA sequencing. In this case, the result was the validation of 50 % of the selected mutations. The Protein-Protein Interaction analysis (PPI), performed with non-synonymous mutations located in functional domains, showed more complex gene networks in tumor tissues than in normal tissues. This observation confirmed the literature regarding the tumorigenic transformation is triggered by the combination of mutations that activate a number of biological processes, thereby, affecting genes, gene pathways and networks of related gene pathways. In summary, this study describes an efficient computational approach to identify mutations in expressed sequence data, besides to evaluate the role of mutations in tumorigenesis.

Indivíduos não relacionados apresentam apenas 1% de diferenças entre seus genomas. Estas variações ocorrem na forma de substituições, inserções, deleções, rearranjos complexos ou até estruturais. Dentre essas variações, aquelas que apresentam uma frequência populacional acima de 1% são denominadas de polimorfismos. Tais variações são responsáveis por diferenças que vão desde a resposta imunológica até o tratamento com drogas, incluindo sensitividade das células tumorais, níveis de plasma, efeitos colaterais e toxicidade. A forma mais comum de polimorfismo genético entre humanos são os polimorfismo de base única ou Single Nucleotide Polymorphisms (SNPs), sendo mais de 47 milhões descritos no dbSNP, um banco de dados de pequenos polimorfismos do NCBI. No presente estudo, foi estabelecida uma abordagem computacional, com etapas de exclusão de regiões parálogas ou de baixa qualidade, com o objetivo de identificar variantes genéticas em sequências expressas gerados pelo método de Open Reading Frame ESTs (ORESTES) durante o Projeto Genoma Humano do Câncer. Diferentemente de outros softwares de detecção de polimorfismos, a abordagem computacional descrita neste estudo leva em consideração a informação a priori do número de bibliotecas distintas que reportaram a mesma variação. Foram identificadas 1900 mutações (853 sinônimas e 1047 não-sinônimas) presentes em duas ou mais bibliotecas distintas, que foram validados in-silico contra o dbSNP v130. O resultado da análise identificou 901 mutações já descritas no dbSNP (47,42%). Para confirmação da análise, foram selecionadas 10 mutações (6 novas e 4 já presentes no dbSNP) para validação pelo método de High Resolution Melt (HRM), seguido da caracterização por sequenciamento de DNA. Nesse caso, o resultado foi a validação de 50% das mutações selecionadas. A análise de interação protéica, Protein-Protein Interaction (PPI), realizada com as mutações não-sinônimas localizadas em domínios funcionais, revelou redes gênicas mais complexas em tecidos tumorais do que nos tecidos normais. Esta observação ratificou a literatura a respeito da transformação tumorigênica ser desencadeada pela combinação de mutações que ativam uma série de processos biológicos, para isso, afetando genes, vias gênicas e networks de vias gênicas relacionados. Em resumo, o presente estudo descreve uma abordagem computacional eficiente para identificação de mutações em dados de sequências expressas, além de avaliar o papel das mutações na tumorigênese. / Unrelated humans have only 1% of non-simularity in their genome. These variations occur as substitutions, insertions, deletions, or even complex structural rearrangements. Among these variations, those which show a population frequency above 1% are called polymorphisms. Such variations are responsible for differences ranging from the immune response to treatment with drugs, including sensitivity of tumor cells, plasma levels, toxicity and side effects. The most common form of genetic polymorphism among human are Single Nucleotide Polymorphisms (SNPs), with more than 47 million reported in dbSNP, a database of small polymorphisms from NCBI. In this study, we established a computational approach, with steps to exclude low quality and paralogous regions, aiming to identify genetic variants in expressed sequences generated by the method of Open Reading Frame ESTs (ORESTES) for the Human Cancer Genome Project. Unlike other polymorphisms detection softwares, the computational approach described in this study takes into account the a priori information about the number of different libraries that reported the same variation. We identified 1900 mutations (853 synonymous and 1047 nonsynonymous) present in two or more different libraries, these mutations were in-silico validated against the dbSNP V130. The analysis result showed 901 mutations already described in dbSNP (47.42%). To confirm the analysis, we selected 10 mutations (six new and four already present in dbSNP) for validation by the method of High Resolution Melt (HRM), followed by characterization by DNA sequencing. In this case, the result was the validation of 50 % of the selected mutations. The Protein-Protein Interaction analysis (PPI), performed with non-synonymous mutations located in functional domains, showed more complex gene networks in tumor tissues than in normal tissues. This observation confirmed the literature regarding the tumorigenic transformation is triggered by the combination of mutations that activate a number of biological processes, thereby, affecting genes, gene pathways and networks of related gene pathways. In summary, this study describes an efficient computational approach to identify mutations in expressed sequence data, besides to evaluate the role of mutations in tumorigenesis.

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<p>Composite materials have been invented and used to make all kinds of industrial products, such as automobiles, aircraft, sports equipment etc., for many years. Excellent properties such as high specific stiffness and strength have been recognized and studied for decades, motivating the use of composite materials. However, the design of composite structures still remains a challenge. Existing design tools are not adequate to exploit the full benefits of composites. Many tools are still based on the traditional material selection paradigm created for isotropic homogeneous materials, separated from the shape design. This will lose the coupling effects between composite materials and the geometry and lead to less optimum design of the structure. Hence, due to heterogeneity and anisotropy inherent in composites, it is necessary to model composite parts with appropriate microstructures  instead of simplistically replacing composites as black aluminum and consider materials and geometry at the same time.</p> <p><br></p> <p>This work mainly focuses on the design problems of complex material-structural systems through computational analyses. Complex material-structural systems are structures made of materials that have microstructures smaller than the overall structural dimension but still obeying the continuum assumption, such as fiber reinforced laminates, sandwich structures, and meta-materials, to name a few. This work aims to propose a new design-by-analysis framework based on the mechanics of structure genome (MSG), because of its capability in accurate and efficient predictions of effective properties  for different solid/structural models and three-dimensional local fields (stresses, strains, failure status, etc). The main task is to implement the proposed framework by developing new tools and integrating these tools into a complete design toolkit. The main contribution of this work is a new efficient high-fidelity design-by-analysis framework for complex material-structural systems.</p> <p><br></p> <p>The proposed design framework contains the following components. 1) MSG and its companion code SwiftComp is the theoretical foundation for structural analysis in this design framework. This is used to model the complex details of the composite structures. This approach provides engineers the flexibility to use different multiscale modeling strategies. 2) Structure Gene (SG) builder creates finite element-based model inputs for SwiftComp using design parameters defining the structure. This helps designers deal with realistic and meaningful engineering parameters directly without expert knowledge of finite element analysis. 3) Interface is developed using Python for easy access to needed data such as structural properties and failure status. This is used as the integrator linking all components and/or other tools outside this framework. 4) Design optimization methods and iteration controller are used for conducting the actual design studies such as parametric study, optimization, surrogate modeling, and uncertainty quantification. This is achieved by integrating Dakota into this framework. 5) Structural analysis tool is used for  computing global structural responses. This is used if an integrated MSG-based global analysis process is needed.</p> <p><br></p> <p>Several realistic design problems of composite structures are used to demonstrate the capabilities of the proposed framework. Parameter study of a simple fiber reinforce laminated structure is carried out for investigating the following: comparing with traditional design-by-analysis approaches, whether the new approach can bring new understandings on parameter-response relations and because of new parameterization methods and more accurate analysis results. A realistic helicopter rotor blade is used to demonstrate the optimization capability of this framework. The geometry and material of composite rotor blades are optimized to reach desired structural performance. The rotor blade is also used to show the capability of strength-based design using surrogate models of sectional failure criteria. A thin-walled composite shell structure is used to demonstrate the capability of designing variable stiffness structures by steering in-plane orientations of fibers of the laminate. Finally, the tool is used to study and design auxetic laminated composite materials which have negative Poisson's ratios.</p>