Estudos quimiométricos em amostras de arroz nacional: caracterização do perfil de aminoácidos e sua correlação com o teor de arsénio

O arroz é um dos alimentos básicos mais importantes para a população mundial, sendo um dos cereais mais consumidos em todo o mundo. Possui um alto teor em hidratos de carbono devido à alta concentração de amido, contém ainda proteínas, vitaminas, minerais e poucas gorduras. A quantidade de proteína a ingerir é requisito para uma dieta adequada (0,75g/kg/dia), devido ao desempenho vital que esta tem na saúde humana. O arroz pelo seu papel determinante na alimentação mundial faz com que os aminoácidos, constituintes das proteínas, mereçam o foco deste estudo. Por outro lado, o arroz pelo seu tipo de cultivo é uma das maiores fontes de ingestão de arsénio para o Homem, um importante agente cancerígeno e contaminante da cadeia alimentar. Isto faz com que este elemento seja igualmente merecedor de análise no presente estudo. Neste estudo foram analisadas, ao nível dos diferentes aminoácidos e do arsénio, 39 amostras de diferentes tipos e regiões de arroz nacional que foram remetidas para uma análise multivariada. Foi feita uma caracterização e posterior comparação entre tipos/variedades/região de arroz, que demonstra para ambos os tipos de estatística (ANOVA e Kruskal-Wallis), diferenças entre variedades, arroz integral e arroz branco. Verifica-se que ao analisar pelas várias características do arroz, não existem diferenças ao nível do arsénio e que, através da correlação de Spearman, este se correlaciona positivamente com arroz integral e negativamente com arroz branco. Na análise de clusters, os aminoácidos (variáveis) foram 3 conjuntos: baixa, média e alta concentração. Por sua vez, as amostras dividem-se pela variedade, formando ainda um cluster em que existe uma fusão de variedades. Para classificação de arroz no futuro, com base no perfil de aminoácidos, foi possível a criação de um modelo k-NN cujo erro de classificação fosse nulo.

Dependência e heterogeneidade geográfica da Diabetes Mellitus Tipo2 na região de Lisboa: análise de sensibilidade e robustez das estatísticas espaciais

A Diabetes Mellitus Tipo2 é uma doença crónica que afecta sobretudo a população adulta e é responsável por mais de 90% dos casos de diabetes. A sua prevalência tem aumentado rapidamente, implicando elevados custos em saúde. Está normalmente associada a várias co-morbilidades e complicações, constituindo-se uma das principais causas de morbilidade e mortalidade no mundo. Em Portugal, dados dos Observatório Nacional da Diabetes revelam que, em 2012, cerca de 13% da população adulta sofria de diabetes (aproximadamente um milhão de pessoas), sendo a taxa de incidência anual de 500 novos casos por cada 100 000 habitantes. A amostra do estudo incluiu os doentes com DM2 com mais de 20 anos, num total de 205068 utentes registados nos centros de cuidados de saúde primários da ARSLVT e que residem na área de Lisboa e Vale do Tejo. O enfoque desta dissertação não é somente a exploração dos padrões geográficos da DM Tipo2 mas, sobretudo, a análise de sensibilidade e robustez das estatísticas espaciais utilizadas. Os objectivos são fundamentalmente metodológicos e passam pela aplicação de estatísticas espaciais, em ambiente ArcGIS®, GeoDaTM e linguagem de computação estatística R; pela reflexão em torno das medidas de dependência e de heterogeneidade geográfica e ainda pela análise quantitativa da irregularidade da distribuição espacial da DM Tipo2 na região de Lisboa, baseada em decisões decorrentes do estudo da sensibilidade e da robustez das estatísticas espaciais. A estrutura espacial dos dados foi estudada segundo matrizes de vizinhos mais próximos, fazendo variar o número de vizinhos (1 a 20). Uma vez definida a estrutura de vizinhança procurou-se traduzir o grau de similaridade espacial que existe entre áreas que são próximas, utilizando como medida o Índice Global de Moran. A identificação dos clusters espaciais foi feita através da aplicação das estatísticas de Anselin Local Moran´s I e Getis-Ord Gi*. Após aplicação das estatísticas referidas procurou-se avaliar, ao longo dos testes realizados, a percentagem de permanência das freguesias num cluster espacial. Da análise dos resultados, e tendo em conta os objectivos propostos, concluiu-se que o mapeamento de padrões espaciais é pouco sensível à variação dos parâmetros utilizados. As duas ferramentas de análise espacial utilizadas (análise de cluster e outlier - Anselin´s Local Moran´s I e análises de Hot spot - Getis-Ord Gi*), embora muito distintas, geraram resultados muito similares em termos de identificação da localização geográfica dos clusters para todas as variáveis. Desta forma, foi possível identificar alguns clusters, ainda que de um modo geral exista uma aleatoriedade espacial nos dados.

Development of principle culture medium formulations for Saccharomyces cerevisiae

Saccharomyces cerevisiae as well as other microorganisms are frequently used in industry with the purpose of obtain different kind of products that can be applied in several areas (research investigation, pharmaceutical compounds, etc.). In order to obtain high yields for the desired product, it is necessary to make an adequate medium supplementation during the growth of the microorganisms. The higher yields are typically reached by using complex media, however the exact formulation of these media is not known. Moreover, it is difficult to control the exact composition of complex media, leading to batch-to-batch variations. So, to overcome this problem, some industries choose to use defined media, with a defined and known chemical composition. However these kind of media, many times, do not reach the same high yields that are obtained by using complex media. In order to obtain similar yield with defined media the addition of many different compounds has to be tested experimentally. Therefore, the industries use a set of empirical methods with which it is tried to formulate defined media that can reach the same high yields as complex media. In this thesis, a defined medium for Saccharomyces cerevisiae was developed using a rational design approach. In this approach a given metabolic network of Saccharomyces cerevisiae is divided into a several unique and not further decomposable sub networks of metabolic reactions that work coherently in steady state, so called elementary flux modes. The EFMtool algorithm was used in order to calculate the EFM’s for two Saccharomyces cerevisiae metabolic networks (amino acids supplemented metabolic network; amino acids non-supplemented metabolic network). For the supplemented metabolic network 1352172 EFM’s were calculated and then divided into: 1306854 EFM’s producing biomass, and 18582 EFM’s exclusively producing CO2 (cellular respiration). For the non-supplemented network 635 EFM’s were calculated and then divided into: 215 EFM’s producing biomass; 420 EFM’s producing exclusively CO2. The EFM’s of each group were normalized by the respective glucose consumption value. After that, the EFMs’ of the supplemented network were grouped again into: 30 clusters for the 1306854 EFMs producing biomass and, 20 clusters for the 18582 EFM’s producing CO2. For the non-supplemented metabolic network the respective EFM’s of each metabolic function were grouped into 10 clusters. After the clustering step, the concentrations of the other medium compounds were calculated by considering a reasonable glucose amount and by accounting for the proportionality between the compounds concentrations and the glucose ratios. The approach adopted/developed in this thesis may allow a faster and more economical way for media development.

State detection in a financial portfolio: a self-organizing maps approach for financial time series

This study analyses financial data using the result characterization of a self-organized neural network model. The goal was prototyping a tool that may help an economist or a market analyst to analyse stock market series. To reach this goal, the tool shows economic dependencies and statistics measures over stock market series. The neural network SOM (self-organizing maps) model was used to ex-tract behavioural patterns of the data analysed. Based on this model, it was de-veloped an application to analyse financial data. This application uses a portfo-lio of correlated markets or inverse-correlated markets as input. After the anal-ysis with SOM, the result is represented by micro clusters that are organized by its behaviour tendency. During the study appeared the need of a better analysis for SOM algo-rithm results. This problem was solved with a cluster solution technique, which groups the micro clusters from SOM U-Matrix analyses. The study showed that the correlation and inverse-correlation markets projects multiple clusters of data. These clusters represent multiple trend states that may be useful for technical professionals.

Botnet cluster identification

Botnets are a group of computers infected with a specific sub-set of a malware family and controlled by one individual, called botmaster. This kind of networks are used not only, but also for virtual extorsion, spam campaigns and identity theft. They implement different types of evasion techniques that make it harder for one to group and detect botnet traffic. This thesis introduces one methodology, called CONDENSER, that outputs clusters through a self-organizing map and that identify domain names generated by an unknown pseudo-random seed that is known by the botnet herder(s). Aditionally DNS Crawler is proposed, this system saves historic DNS data for fast-flux and double fastflux detection, and is used to identify live C&Cs IPs used by real botnets. A program, called CHEWER, was developed to automate the calculation of the SVM parameters and features that better perform against the available domain names associated with DGAs. CONDENSER and DNS Crawler were developed with scalability in mind so the detection of fast-flux and double fast-flux networks become faster. We used a SVM for the DGA classififer, selecting a total of 11 attributes and achieving a Precision of 77,9% and a F-Measure of 83,2%. The feature selection method identified the 3 most significant attributes of the total set of attributes. For clustering, a Self-Organizing Map was used on a total of 81 attributes. The conclusions of this thesis were accepted in Botconf through a submited article. Botconf is known conferênce for research, mitigation and discovery of botnets tailled for the industry, where is presented current work and research. This conference is known for having security and anti-virus companies, law enforcement agencies and researchers.

Genetic characterization of rabies virus isolated from bovines and equines between 2007 and 2008, in the States of São Paulo and Minas Gerais

INTRODUCTION: Rabies is an acute disease of the central nervous system and is responsible for the deaths of thousands of humans, wild animals and livestock, particularly cattle, as well as causing major economic losses. This study describes the genetic characterization of rabies virus variants that circulate in Desmodus rotundus populations and are transmitted to herbivores. METHODS: Fifty rabies virus isolates from bovines and equines in the States of São Paulo and Minas Gerais, Brazil, were genetically characterized and compared with sequences retrieved from GenBank. RESULTS: Two clusters (I and II) with mean nucleotide identities of 99.1 and 97.6% were found. The first of these contained nearly all the samples analyzed. Lineages from other Brazilian states grouped in cluster II. CONCLUSIONS: Analysis of the amino acid sequences of the N proteins revealed the existence of genetic markers that may indicate possible variations between geographic regions, although the biologically active regions are conserved within the species over space and time.

Molecular characterization of rabies virus isolated from non-haematophagous bats in Brazil

INTRODUCTION: Rabies is an important zoonosis that causes thousands of deaths worldwide each year. Although the terrestrial cycle, mainly transmitted by dogs, is controlled in Brazil, the aerial cycle remains a serious public health issue, besides the economic problem. In the aerial cycle, the haematophagous bat Desmodus rotundus is the main source of infection, where several different species of non-haematophagous bats can be infected and can transmit the virus. METHODS: The aim of this work was to study the epidemiological pattern of rabies using antigenic characterization with monoclonal antibodies and genetic characterization by reverse-transcriptase polymerase chain reaction followed by sequencing and phylogenetic analysis of non-haematophagous bats' and herbivorous animals' central nervous system samples from the western region of the State of São Paulo, Brazil. RESULTS: From 27 samples, 3 antigenic variants were identified: AgV-3, AgV-4, and AgV-6; and from 29 samples, 5 different clusters were identified, all belonging to the rabies virus species. CONCLUSIONS: Although only non-haematophagous bats were evaluated in the studied region, the majority of samples were from antigenic and genetic variants related to haematophagous bats Desmodus rotundus. Samples from the same antigenic variant were segregated in more than one genetic cluster. This study demonstrated the diversity of rabies virus genetic lineages presented and circulating in non-haematophagous bats in the studied region.