Discriminating different classes of biological networks by analyzing the graphs spectra distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them on (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed

A model for the decision-maker preferences in a polymer extrusion process

The NN-DM is a method developed to find a mathematical model that represents the Decision-Maker (DM) by employing an artificial neural network (NN) in situations in which the preferences can be represented by a utility function. This paper presents further developments to the NN-DM method to find a model in a polymer extrusion process. The form of the DM's interaction, the domain assignment, the ranking process, and the performance assessment are adapted to a real context of a multi-objective optimization problem followed by a design decision. The DM is then requested to fill a matrix expressing his preferences considering pairwise comparisons expressing ordinal relations only. Two multi-objective optimization problems are tested, each one with three estimates of different Pareto-optimal fronts. The adapted NN-DM method is able to provide a model which sorts the available solutions from the best to the worst according to the DM's preferences.info:eu-repo/semantics/publishedVersio

O impacto das variações do programa de produção nos custos logísticos: um estudo de caso na FIAT Automóveis

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia de Produção.Resumo informativo referente a pesquisa efetuada sobre os impactos das variações do programa de produção nos custos logísticos relativos a transportes e estoques realizada na Fiat Automóveis. Descreve as mudanças provocadas pela globalização na economia mundial, a evolução dos sistemas de produção desde a época do fordismo até os conceitos modernos do consórcio modular, o desenvolvimento da função logística nas empresas e a gestão estratégica dos custos. Através de entrevistas realizadas com os departamentos da empresa, foram descritas as atividades de planejamento das previsões, carregamento de pedidos, programação de materiais, gestão dos transportes e planejamento dos estoques. Com os dados coletados nos relatórios e arquivos da empresa, estabeleceu-se uma comparação entre os volumes de produção previstos e os efetivamente realizados, permitindo demonstrar como os custos logísticos são influenciados por estas variações. A conclusão desta pesquisa demonstra a importância da qualidade das previsões de mercado para o dimensionamento da atividade da empresa, destacando a diferença entre a flexibilidade do ambiente comercial e a rigidez dos processos produtivos. Também ressalta a importância de se fazer previsões do nível de serviço logístico, considerado ponto inicial para avaliação da sua eficácia

Psoriasis in childhood and adolescence

A psoríase é doença inflamatória crônica, imunologicamente mediada, recorrente e de caráter universal. Aproximadamente um terço dos adultos acometidos refere início da doença antes dos 16 anos de idade. Quanto mais precoce, mais grave tende a ser a evolução do quadro. Em crianças, as lesões podem ser fisicamente desfigurantes, causando prejuízos psicológicos e evidente comprometimento da qualidade de vida. As medicações sistêmicas utilizadas na psoríase, bem como a fototerapia, têm indicação limitada na infância, devido aos efeitos cumulativos das drogas, à baixa aceitação e ao risco de teratogenicidade. Nesta seção, discutiremos as principais manifestações clínicas da psoríase na infância e na adolescência, bem como os diagnósticos diferenciais, opções terapêuticas e prognóstico.Psoriasis is a chronic, immunologically mediated, recurrent and universal inflammatory disorder. Approximately one third of adults refer onset before 16 years of age. The sooner the onset, the worse is the prognosis. In children, lesions may be physically disfiguring, leading to psychological impairment and evident loss of quality of life. Systemic therapy used in psoriasis, as well as phototherapy, has limited use in children due to accumulative effects of drugs, low acceptance, and risk of teratogenicity. In this section, we discuss the main clinical aspects of psoriasis in childhood and adolescence, differential diagnosis, therapeutic options, and prognosis

A revelação do diagnóstico de doença de Alzheimer: opiniões de cuidadores em uma amostra brasileira

BACKGROUND: Disclosure of the diagnosis of Alzheimer's disease (AD) remains a contentious issue, and has been little studied in developing countries. OBJECTIVE: To investigate the influence of socio-demographic factors and the experience of being a caregiver on opinion about disclosing AD diagnosis to the patient in a Brazilian sample. METHOD: Caregivers of 50 AD patients together with 50 control participants that did not have the experience of being a caregiver of AD patient were interviewed using a structured questionnaire. RESULTS: Most of the participants (73.0%) endorsed disclosure of the diagnosis, while caregivers were less prone to disclose (58.0%) than controls (88.0%; p=0.0007). Logistic regression confirmed that only the experience of being a caregiver was associated with a lesser tendency for disclosure endorsement. CONCLUSION: The majority of participants was in favor of disclosing the diagnosis, but caregivers were less willing to disclose the diagnosis to the AD patient.FUNDAMENTO: A revelação do diagnóstico de doença de Alzheimer (DA) tem sido tema polêmico e pouco estudado em países em desenvolvimento. OBJETIVO: Investigar a influência de fatores sócio-demográficos e a experiência de ter sido cuidador na opinião sobre a revelação do diagnóstico em uma amostra brasileira. MÉTODO: Cuidadores de 50 pacientes com DA e 50 indívíduos controle que não tinham tido experiência como cuidadores de pacientes com DA foram entrevistados com o uso de um questionário estruturado. RESULTADOS: A maioria dos participantes (73,0%) manifestou-se a favor da revelação diagnóstico aos pacientes, mas cuidadores foram menos favoráveis (58,0%) que controles (88,0%; p=0,0007). Regressão logística demonstrou que apenas a experiência como cuidador foi associada com menor tendência a apoiar a revelação do diagnóstico. CONCLUSÃO: A maioria dos participantes foi a favor da revelação do diagnóstico ao paciente, mas aqueles com experiência como cuidadores de pacientes com DA foram menos favoráveis

Measuring network's entropy in ADHD: A new approach to investigate neuropsychiatric disorders

The application of graph analysis methods to the topological organization of brain connectivity has been a useful tool in the characterization of brain related disorders. However, the availability of tools, which enable researchers to investigate functional brain networks, is still a major challenge. Most of the studies evaluating brain images are based on centrality and segregation measurements of complex networks. in this study, we applied the concept of graph spectral entropy (GSE) to quantify the complexity in the organization of brain networks. in addition, to enhance interpretability, we also combined graph spectral clustering to investigate the topological organization of sub-network's modules. We illustrate the usefulness of the proposed approach by comparing brain networks between attention deficit hyperactivity disorder (ADHD) patients and the brain networks of typical developing (TD) controls. the main findings highlighted that GSE involving sub-networks comprising the areas mostly bilateral pre and post central cortex, superior temporal gyrus, and inferior frontal gyri were statistically different (p-value = 0.002) between ADHD patients and TO controls. in the same conditions, the other conventional graph descriptors (betweenness centrality, clustering coefficient, and shortest path length) commonly used to identify connectivity abnormalities did not show statistical significant difference. We conclude that analysis of topological organization of brain sub-networks based on GSE can identify networks between brain regions previously unobserved to be in association with ADHD. (C) 2013 Elsevier Inc. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Pew Latin American FellowshipFed Univ ABC, Ctr Math Computat & Cognit, BR-09210170 Santo Andre, SP, BrazilPrinceton Univ, Dept Psychol, Princeton, NJ 08540 USAPrinceton Univ, Neurosci Inst, Princeton, NJ 08540 USAUniversidade Federal de São Paulo, Dept Psychiat, Lab Interdisciplinar Neurociencias Clin, São Paulo, BrazilUniv Estadual Campinas, Ctr Mol Biol & Genet Engn, BR-13083875 Campinas, SP, BrazilUniv São Paulo, Dept Comp Sci, Inst Math & Stat, BR-05508090 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychiat, Lab Interdisciplinar Neurociencias Clin, São Paulo, BrazilWeb of Scienc

Reducing Dimensionality to Improve Search in Semantic Genetic Programming

Genetic programming approaches are moving from analysing the syntax of individual solutions to look into their semantics. One of the common definitions of the semantic space in the context of symbolic regression is a n-dimensional space, where n corresponds to the number of training examples. In problems where this number is high, the search process can became harder as the number of dimensions increase. Geometric semantic genetic programming (GSGP) explores the semantic space by performing geometric semantic operations—the fitness landscape seen by GSGP is guaranteed to be conic by construction. Intuitively, a lower number of dimensions can make search more feasible in this scenario, decreasing the chances of data overfitting and reducing the number of evaluations required to find a suitable solution. This paper proposes two approaches for dimensionality reduction in GSGP: (i) to apply current instance selection methods as a pre-process step before training points are given to GSGP; (ii) to incorporate instance selection to the evolution of GSGP. Experiments in 15 datasets show that GSGP performance is improved by using instance reduction during the evolution