867 resultados para Nonparametric discriminant analysis


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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The relationship between mother and child in the context of oral health has traditionally been exposed by the scientific literature in microbiology, which lacks a broad and necessary discussion of health and illness seen as processes, both biological and social. Objective: Investigate the family social determinants associated with the caries history of children and the need for dental treatment (NDT) among their mothers was the objective of this study. Material and Methods: This research employed a cross-sectional study of mother-child pairs living in southern Brazil. Data collection occurred in public institutions of early childhood education. The instruments included a structured questionnaire administered to mothers and clinical oral examinations of the mothers and children. The social variables considered were marital status, maternal education, number of children, income, employment status, and frequency of visits to a dental professional. The measured outcomes were the maternal NDT and child caries history. Data were analyzed by the chi-square test (chi 2) and by discriminant analysis. Results: The final sample consisted of 272 mother-child pairs and it was found that the greatest need for treatment was among mothers with low educational level and low family income who rarely or never visited a dentist. Tooth decay was less frequent in only child, and most frequent in children of mothers with low educational attainment, and in children in lower income households who rarely or never visited the dentist. The social determinants of caries in children and of the maternal NDT were similar. It follows that the maternal NDT and caries history among children were strongly associated with maternal education (p<0.0001), household income (p<0.0001), and frequency of visits to a dental professional (0.0018). Caries history among children was also associated with number of children in the household (p<0.0001). Conclusions: The results suggest that the caries experience in children depended less on the family social variables than on the maternal NDT.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Pós-graduação em Engenharia de Produção - FEB

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Pós-graduação em Desenvolvimento Humano e Tecnologias - IBRC

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China is an important center of origin for the genus Citrus L. of the family Rutaceae and is rich in wild Citrus species. The taxonomy of Citrus has been a subject of controversy for more than a half century. We propose that the metabolite profiles of Chinese native Citrus species can be used for classification and understanding of the taxonomic relationships within the Citrus germplasm. In this study, triplicate gas chromatography-mass spectrometry (GC-MS) metabolite profiles of 20 Citrus species/varieties were acquired, including 10 native varieties originating in China. R-(+)-limonene, alpha-pinene, sabinene and alpha-terpinene were found to be major characteristic components of the essential oils analyzed in this study, and these compounds contributed greatly to the metabolic classification. The three basic species of the subgenus Eucitrus (Swingle's system), i.e., C. reticulata Blanco, C. medica L. and C. grandis Osb., were clearly differentiated based upon their metabolite profiles using hierarchical cluster analysis (HCA) and partial least square-discriminant analysis (PLS-DA). All the presumed hybrid genotypes, including sweet orange (C. sinensis Osb.), sour orange (C. aurantium L.), lemon (C. limon Burm.f.), rough lemon (C. jambhiri Lush.), rangpur lime (C. limonia Osb.) and grapefruit (C. paradisi Macf.), were grouped closely together with one of their suggested parent species in the HCA-dendrogram and the PLS-DA score plot. These results clearly demonstrated that the metabolite profiles of Citrus species could be utilized for the taxonomic classification of the genus and are complementary to the existing taxonomic evidence, especially for the identification and differentiation of hybrid species.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Re-establishing deforested ecosystems to pre-settlement vegetation is difficult, especially in ecotonal areas, due to lack of knowledge about the original physiognomy. Our objective was to use a soils database that included chemical and physical parameters to distinguish soil samples of forest from those of savannah sites in a municipality located in the southeastern Brazil region. Discriminant analysis (DA) was used to determine the original biome vegetation (forest or savannah) in ecotone regions that have been converted to pasture and are degraded. First, soils of pristine forest and savannah sites were tested, resulting in a reference database to compare to the degraded soils. Although the data presented, in general had a high level of similarity among the two biomes, some differences occurred that were sufficient for DA to distinguish the sites and classify the soil samples taken from grassy areas into forest or savannah. The soils from pastured areas presented quality worse than the soils of the pristine areas. Through DA analysis we observed that, from seven soil samples collected from grassy areas, five were most likely originally forest biome and two were savannah, ratified by a complementary cluster analysis carried out with the database of these samples. The model here proposed is pioneer. However, the users should keep in mind that using this technology, i.e., establishing a regional-level database of soil features, using soil samples collected both from pristine and degraded areas is critical for success of the project, especially because of the ecological and regional particularities of each biome.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)