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Os dados do Sistema de Avaliação da Educação Básica do Brasil resultam de uma política pública relevante, porém dispendiosa, ao passo que são pouco utilizados pela gestão escolar como ferramenta de diagnóstico da aprendizagem. Apesar disso, se adequadamente tratados eles possibilitam extrair informações importantes para que a gestão possa intervir de forma focalizada na aplicação de recursos, e de uma maneira mais ampla, nortear políticas públicas para melhoria do processo de ensino-aprendizagem. Esta pesquisa buscou identificar fatores que influenciaram na nota do Ideb na avaliação aplicada no ano de 2017, para o 9º ano/8ª série do ensino fundamental entre escolas da rede estadual do Tocantins. Estes fatores foram identificados a partir da análise das notas do Ideb, associados aos microdados oriundos dos questionários do Saeb. Para isso foi feita uma análise comparativa do desempenho no Ideb de 2017. A referida análise teve um viés quantitativo a partir dos dados dos questionários do diretor, do professor e da escola, sendo selecionadas algumas questões destes questionários agrupando-as em algumas esferas que compõem a gestão escolar, como a participação da comunidade, a gestão escolar, a interferência externa, o apoio financeiro, o fator socioeconômico e a atuação e qualificação do corpo docente. Com isso, a partir de uma análise fatorial multivariada exploratória, pôde-se identificar dentre estes fatores, quais estão relacionados a um maior ou menor desempenho na avaliação do Ideb. A análise fatorial multivariada pautou-se tanto na extração fatorial por meio do método dos componentes principais a partir da correlação de Pearson quanto na obtida por meio da correlação tetracórica. Da análise fatorial, pôde-se destacar que os maiores índices de desempenho se correlacionaram com variáveis relacionadas à infraestrutura, como: salas de aulas em bom estado de conservação, internet de boa qualidade para alunos e professores e laboratórios de informática e de ciências; escolas que receberam apoio financeiro regularmente, tanto do estado quanto da União e escolas situadas em zona urbana também se correlacionaram positivamente com as escolas que tiverem maiores notas. Além disso, verificou-se que o índice socioeconômico de médio a alto correlacionou-se com as escolas que obtiveram maior índice de desempenho no Ideb. A formação docente situou-se na região de interseção, não sendo fator preponderante para melhoria nas notas, não se correlacionando exclusivamente com um ou outro grupo, e a maior parte das variáveis referentes a formação docente não apresentou carga fatorial significativa. Porém, pela análise das matrizes de correlação, constatou-se que a formação docente contribuiu para dirimir os problemas inerentes ao alto índice de faltas dos professores, a alta rotatividade do corpo docente e a ausência de professores para disciplinas específicas. Concluiu-se que aplicação do método irá auxiliar o gestor em compreender os índices levantados sobre sua escola, correlacionando-a com as demais e traçando uma perspectiva com pontos específicos onde se deve melhorar, lembrando que os dados devem ser trabalhados e aplicados considerando as particularidades regionais e da escola especificamente. The data from the Brazilian Basic Education Assessment System are the result of a relevant but costly public policy, while at the same time they are little used by school management as a learning diagnostic tool. However, if properly treated, they allow extracting important information so that management can intervene in a focused way in the application of resources and, more broadly, guide public policies to improve the teaching-learning process. This research sought to identify the factors that influenced the Basic Education Development Index (Ideb) score in the assessment applied in 2017, for the 9th grade/8th grade of elementary school among schools in the state network of Tocantins. These factors were identified from the analysis of the Ideb grade, associated with the microdata from the Basic Education Assessment System (Saeb) questionnaires. For this, a comparative analysis of the performance in Ideb 2017 was carried out. This analysis had a quantitative bias based on data from the principal, teacher, and school questionnaires, with some questions being selected from these questionnaires, grouping them into some spheres that make up school management, such as community participation, school management, external interference, support, the socioeconomic factor, the performance, and qualifications of the teaching staff. Thus, from an exploratory multivariate factor analysis, it was possible to identify, among these factors, which ones are related to a greater or lower performance in the Ideb evaluation. Multivariate factor analysis was based both on factor extraction using Pearson's principal components method and on the one obtained by tetrachoric correlation. From the factor analysis, it was possible to highlight that the highest performance rates were correlated with variables related to infrastructure, such as: classrooms in good condition, good quality internet for students and teachers, and computer and science laboratories; schools that received regular financial support, both from the State Government and the Federated Government, and schools located in urban areas, were also positively correlated with schools with higher scores. In addition, it was found that the medium to high socioeconomic index correlated with the schools that presented the highest performance index in the Ideb. Teacher training was in the intersection region, not being a preponderant factor for grade improvement, not being correlated exclusively with one or another group, and most of the variables referring to teacher training did not present a significant factor loading. Although, by analyzing the correlation matrices, it was found that teacher training contributed to solving the problems inherent to the high rate of teacher shortages, the high turnover of the teaching staff and the absence of teachers for specific subjects. It was concluded that the application of the method will help the manager to understand the indexes raised about his school, correlating it with the others and tracing a perspective with specific points where it must be improved, remembering that the data must be worked and applied considering the regional particularities and the school specifically.

Agropastoral societies in Ethiopia and elsewhere in Africa are facing challenges in their land use. Land shifts toward large-scale land investments have exacerbated the scarcity of pastures, thus affecting the resilience of pastoral systems. In this study, we assessed how large-scale land investments affect household resilience using data from the Living Standards Measurement Survey in Ethiopia. We estimated household resilience capacity by a multivariate two-step factor analysis and welfare resilience from net changes in welfare outcomes between two survey intervals. We also assessed the effect of large-scale land investment on household resilience using random effects ordered logistic regression model. Factors that enhanced the resilience capacities of households included access to livestock markets, social safety nets, extension, mobility, and social services. Approximately one-third of the study population had a low resilience capacity, and more than half had low welfare resilience. Moreover, proximity to a large-scale land investment significantly reduced households' likelihood of having high resilience capacity. Therefore, future resilience programs in agropastoral areas should mitigate the adverse effect of large-scale land investments by enhancing livelihood diversification and households' access to communal pastures.

Genomic selection (GS) reports on milk fatty acid (FA) profiles have been published quite recently and are still few despite this trait represents the most important aspect of milk nutritional and sensory quality. Reasons for this can be found in the high costs of phenotype recording but also in issues related to its nature of complex trait constituted by multiple genetically correlated variables with low heritabilities. One possible strategy to deal with such constraint is represented by the use of dimension reduction methods. We analysed 40 individual FAs from Italian Brown Swiss, Holstein and Simmental milk through multivariate factor analysis (MFA) to study the genetics of milk FA-related latent variables (factors) and assess their potential use in breeding. A total of nine factors were obtained, and their genetic parameters were inferred under a Bayesian framework using two statistical approaches: the classical pedigree best linear unbiased prediction (ABLUP) and the single-step genomic BLUP (ssGBLUP). The resulting factorial solutions were able to represent groups of FAs with common origin and function and can be considered concise pathway-level phenotypes. The heritability (h2 ) values showed relevant variations across different factors in each breed (0.03 ≤ h2 ≤ 0.38). The accuracies of breeding values predicted were low to high, ranging from 0.13 to 0.72 and from 0.18 to 0.74 considering the pedigree and the genomic model, respectively. The gain in accuracy in genetic prediction due to the addition of genomic information was ~30% and ~5% in validation and training groups respectively, confirming the contribution of genomic information in yielding more accurate predictions compared to the traditional ABLUP methodology. Our results suggest that MFA in combination with GS can be a valuable tool in dairy cattle breeding and deserves to be further investigated for use in future breeding programs to improve cow's milk FA-related traits.

Background: Multivariate factor analysis (MFA) could be used for analyzing the complex pattern of correlations among fatty acids (FAs) in the rumen. Objective: To investigate the potential use of MFA to extract information on unbranched FAs metabolism in bovine rumen fluid. Methods: A dataset containing 107 individual records of 26 unbranched FAs from two in vitro ruminal incubation studies was constructed. The MFA was performed using the SPSS (Statistical Package for the Social Sciences) software. Results: The MFA extracted four latent factors, accounting for 86.7% of the total variance. The first factor was positively associated with short (6:0), medium (8:0, 10:0, 12:0, 14:0, and 16:0), and long (17:0, 20:0, and 23:0) saturated FAs (SFAs), as well as with cis and trans monounsaturated FAs (MUFAs) from 14 to 16 carbon atoms (14:1- t5, 14:1-c9, 15:1-t10, 16:1-c9, and 16:1-t9). The second factor was positively correlated with 18:0 and the majority of cis and trans MUFAs of 18 carbon atoms (18:1-t9, 18:1-t11, 18:1-c6, 18:1-c9, and 18:1-c11). The third factor was positively related to 18:3-c9,c12,c15 and 18:2-t11,c15, and negatively to 18:2-c9,t11. The fourth factor was positively correlated with 18:1-t6 and 19:0; however, 19:0 was also negatively associated with the second factor. The 18:2-c9,c12 was negatively correlated with the second and third factors. Conclusion: Multivariate factor analysis (MFA) allowed the reduction of a large number of fatty acids in bovine ruminal fluid to a few latent factors with biological meaning. Resumen Antecedentes: El análisis multivariado factorial (MFA) podría usarse para evaluar el complejo patrón de correlaciones entre ácidos grasos (FAs) en el rumen. Objetivo: Investigar el uso potencial del AMF para extraer información sobre el metabolismo de FAs no ramificados en fluido ruminal de bovinos. Métodos: Se construyó una base de datos conformada por 107 registros individuales de 26 FAs no ramificados, provenientes de dos estudios de incubación in vitro. El MFA se desarrolló usando el programa SPSS (Statistical Package for the Social Sciences). Resultados: El MFA extrajo cuatro factores latentes, cuantificando el 86.7% de la varianza total. El primer factor se correlacionó positivamente con FAs saturados (SFAs) de cadena corta (6:0), media (8:0, 10:0, 12:0, 14:0 y 16:0) y larga (17:0, 20:0 y 23:0) como también con FAs monoinsaturados (MUFAs) de 14 a 16 átomos de carbono (14:1-t5, 14:1-c9, 15:1-t10, 16:1-c9 y 16:1-t9). El segundo factor se correlacionó positivamente con 18:0 y con la mayoría de MUFAs cis y trans de 18 átomos de carbono (18:1- t9, 18:1-t11, 18:1-c6, 18:1-c9 y 18:1-c11). El tercer factor se correlacionó positivamente con 18:3-c9,c12,c15 y 18:2-t11,c15 y negativamente con 18:2-c9,t11. El cuarto factor se correlacionó positivamente con 18:1-t6 y 19:0; sin embargo, 19:0 también se correlacionó negativamente con el segundo factor. El 18:2-c9,c12 se correlacionó negativamente con los factores segundo y tercero. Conclusión: El análisis multivariado factorial (MFA) permitió reducir el gran número de ácidos grasos del fluido ruminal a unos pocos factores latentes con significado biológico. Resumo Antecedentes: A análise multivariada fatorial (MFA) poderia ser aplicada na análise do padrão complexo de correlações entre ácidos graxos (FAs) no rúmen. Objetivo: Investigar o potencial de uso do MFA para extrair informação sobre o metabolismo de FAs não ramificados no fluido ruminal de bovinos. Métodos: Construiu-se um banco de dados formado por 107 registros individuais de 26 FAs não ramificados, oriundos de dois estudos de incubação in vitro. A MFA foi desenvolvida usando o programa SPSS (Statistical Package for the Social Sciences). Resultados: A MFA extraiu quatro fatores latentes, quantificando 86.7% da variância total. O primeiro fator correlacionou-se positivamente com FAs saturados (SFAs) de cadeia curta (6:0), meia (8:0, 10:0, 12:0, 14:0 e 16:0) e longa (17:0, 20:0 e 23:0) bem como com os FAs monoinsaturados (MUFAs) de 14 a 16 átomos de carbono (14:1-t5, 14:1-c9, 15:1-t10, 16:1-c9 y 16:1-t9). O segundo fator correlacionou-se positivamente com 18:0 e com a maioria de MUFAs cis y trans de 18 átomos de carbono (18:1-t9, 18:1-t11, 18:1- c6, 18:1-c9 e 18:1-c11). O terceiro fator correlacionou-se positivamente com 18:3-c9,c12,c15 e 18:2-t11,c15 e negativamente com 18:2-c9,t11. O quarto fator correlacionou-se positivamente com 18:1-t6 e 19:0; porém, 19:0 também se correlacionou negativamente com o segundo fator. O 18:2-c9,c12 se correlacionou negativamente com os fatores segundo e terceiro. Conclusão: A análise multivariada fatorial (MFA) permitiu reduzir um número grande de ácidos graxos no fluido ruminal a poucos fatores latentes com significado biológico.

Urban sprawl is now a common and threatening phenomenon in Europe, severely affecting environmental and economic sustainability. An analytical characterization and measurement of urban sprawl are required to gain a better understanding of the phenomenon and to propose the possible solutions. Traditional factor analysis techniques, especially Principal Component Analysis and Factor Analysis, have been commonly used. In this paper, we additionally test Independent Component Analysis with the aim to obtain a multidimensional characterization of the sprawl phenomenon. We also use Bayesian Factor Analysis to obtain a single (unidimensional) measuring index of sprawl, which also allows us to obtain the uncertainty of the inferred index, in contrast to traditional approaches. All these techniques have been applied to study the phenomenon of urban sprawl at the municipality level in Valencia, Spain using a wide set of variables related to the characteristics and patterns of urban land use.

We examined the latent structure of 26 cheese related phenotypes in dairy cattle. Traits related to milk yield and quality (8 traits), milk protein fractions (8 traits), coagulation and curd firmness indicators (CF, 5 traits) and cheese-making phenotypes (cheese yields (%CY) and nutrient recoveries in the curd (REC), 5 traits) were analysed through multivariate factor analysis (MFA) using a varimax rotation. All phenotypes were measured in 1264 Brown Swiss cows. Ten mutual orthogonal, latent variables (factors; Fs) were obtained explaining 74% of the original variability. These Fs captured basic concepts of the cheese-making process. More precisely, the first 4 Fs, sorted by variance explained, were able to capture the underlying structure of the CY percentage (F1: %CY), the CF process with time (F2: CFt), the milk and solids yield (F3: Yield) and the presence of nitrogen (N) in the cheese (F4: Cheese N). Moreover, 4 Fs (F5: as1-β-CN, F7: κ-β-CN, F8: as2-CN and F9: as1-CN-Ph) were related to the basic milk caseins and 1 factor was associated with the α-LA whey protein (F10: α-LA). A factor describing udder health status (F6: Udder health), mainly loaded on lactose, other nitrogen compounds in the milk and SCS, was also obtained. Further, we inferred the effects of some potential sources of variation (e.g. stage of lactation and parity) including feeding and management systems. Stage of lactation had a significant effect for 7 of the 10 Fs, followed by parity of the cow (3 Fs), dairy system and feeding (3 Fs). Our work demonstrates the usefulness of MFA in reducing a large number of variables to a few latent factors with biological meaning and representing groups of traits that describe a complex process like cheese-making. Such an approach would be a valuable tool for studying the influence of different production environments and individual animal factors on protein composition and cheese-making related traits.

Adaptive immune maturation in children is likely the result of a complex interplay between both intrinsic and environmental factors, but surprisingly little is known about how early life immune maturation is related to immune responses and subsequent development of allergic disease. The aim of the FARMFLORA birth-cohort study, including farmers’ and non-farmers’ children, was to visualize longitudinal patterns of adaptive immune maturation in relation to allergic sensitization and disease, vaccine-induced antibody responses, as well as to certain environmental factors in childhood. By the use of multi¬variate factor analyses, we show that higher proportions of circulating neonatal regulatory T cells was strongly associated with sensitization in early childhood, and that a sustained higher fraction of these cells related to allergic disease at school age. Allergic disease at this age was also associated with higher proportions of naïve CD45RA+ T cells in infancy and with higher proportions of immature/naïve CD5+ B cells from birth to 8 years of age. These results indicate that allergic disease in childhood is preceded by a heightened immaturity in the adaptive immune system. Further, growing up on a dairy farm was associated with a higher degree of adaptive immune maturation, which may in part explain the lower incidence of allergic disease among farmers’ children. We further found that higher antibody levels induced by the non-live vaccine against diphtheria, tetanus and pertussis was associated with increased baseline immune maturation prior to vaccination. In contrast, higher antibody levels induced by the live attenuated vaccine against measles, mumps and rubella were generally associated with a lower degree of baseline adaptive immune maturation. Differences in the formulations of these vaccines and their respective way to induce immune responses in the host may be a possible explanation for these diverging association patterns.

We studied the genetics of cheese-related latent variables (factors; Fs) for application in dairy cattle breeding. In total, 26 traits, recorded in 1264 Brown Swiss cows, were analyzed through multivariate factor analysis (MFA). Traits analyzed were descriptors of milk quality and yield (including protein fractions) and measures of coagulation, curd firmness (CF), cheese yields (%CY) and nutrient recoveries in the curd (REC). A total of 10 Fs (mutual orthogonal with a varimax rotation) were obtained. To assess the practical use of the Fs into breeding, we inferred their genetic parameters using single and bivariate animal models under a Bayesian framework. Heritability estimates (intra-herd) varied between 0.11 and 0.72 (F3: Yield and F7: κ-β-CN, respectively). The Fs underlined basic characteristics of the cheese-making process, milk components and udder health, while retaining 74% of the original variability. The first two Fs were indicators of the CY percentage (F1: %CY) and the CF process (F2: CF t ), and presented similar heritability estimates: 0.268 and 0.295, respectively. The third factor was associated with the yield of milk and solids (F3: Yield) characterized by a low heritability (0.108) and the fourth with the cheese nitrogen (N) (F4: Cheese N) that conversely appeared to be characterized by a high heritability (0.618). Three Fs were associated with the proportion of the basic milk caseins on total milk protein (F5: as1-β-CN, F7: κ-β-CN, F8: as2-CN), also highly heritable (0.565, 0.723 and 0.397, respectively) and 1 factor with the phosphorylated form of the as1-CN (F9: as1-CN-Ph; 0.318). Moreover, 1 factor was linked to the whey protein α-LA (F10: α-LA; 0.147). An indicator factor of a cow's udder health (F6: Udder health) was also obtained and showed a moderate heritability (0.204). Although the Fs were phenotypically uncorrelated, considerable additive genetic correlations existed among them, with highest values observed between F10: α-LA and F6: Udder health (-0.67) as well as between F9: as1-CN-Ph and F3: Yield (-0.60). Our results show the usefulness of MFA in dairy cattle breeding. The ability to replace a large number of variables with a few latent indicators of the same biological meaning marks MFA as a valuable tool for developing breeding strategies to improve cow's cheese-related traits.

Background In omics data integration studies, it is common, for a variety of reasons, for some individuals to not be present in all data tables. Missing row values are challenging to deal with because most statistical methods cannot be directly applied to incomplete datasets. To overcome this issue, we propose a multiple imputation (MI) approach in a multivariate framework. In this study, we focus on multiple factor analysis (MFA) as a tool to compare and integrate multiple layers of information. MI involves filling the missing rows with plausible values, resulting in M completed datasets. MFA is then applied to each completed dataset to produce M different configurations (the matrices of coordinates of individuals). Finally, the M configurations are combined to yield a single consensus solution. Results We assessed the performance of our method, named MI-MFA, on two real omics datasets. Incomplete artificial datasets with different patterns of missingness were created from these data. The MI-MFA results were compared with two other approaches i.e., regularized iterative MFA (RI-MFA) and mean variable imputation (MVI-MFA). For each configuration resulting from these three strategies, the suitability of the solution was determined against the true MFA configuration obtained from the original data and a comprehensive graphical comparison showing how the MI-, RI- or MVI-MFA configurations diverge from the true configuration was produced. Two approaches i.e., confidence ellipses and convex hulls, to visualize and assess the uncertainty due to missing values were also described. We showed how the areas of ellipses and convex hulls increased with the number of missing individuals. A free and easy-to-use code was proposed to implement the MI-MFA method in the R statistical environment. Conclusions We believe that MI-MFA provides a useful and attractive method for estimating the coordinates of individuals on the first MFA components despite missing rows. MI-MFA configurations were close to the true configuration even when many individuals were missing in several data tables. This method takes into account the uncertainty of MI-MFA configurations induced by the missing rows, thereby allowing the reliability of the results to be evaluated. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1273-5) contains supplementary material, which is available to authorized users.

The interpretation of mutual relationship among milk fatty acids (FA) is not easy due to the high number of FA contained in milk fat and to the high degree of correlation among them. Multivariate analysis includes different statistical approaches that could help explaining complex pattern of variables. In this study, Multivariate Factor Analysis (MFA) was used to decompose the correlation matrix of 47 FA and milk production traits (milk yield and protein and fat content) measured in 300 Italian Holstein Friesian cows reared in the North of Italy in 23 commercial dairy farms, representative of the intensive dairy system. MFA was able to extract seven latent factors with specific biologic meaning: secretion of Long Chain FA (K LCFA ), mammary FA de novo synthesis (K m ), rumen biohydrogenation (K bh ), synthesis of odd chain FA (K o ), synthesis of branched chain FA (K b ), mammary desaturation activity (K d ), milk yield (K my ). According to the pattern of communalities of the factor analysis, C18:3c9c12c15 was the only FA, along with C18:2t11c15, to be uncorrelated with the other variables and it seemed to be excluded by the metabolic pattern described by the seven factors. The desaturation products of the SCD enzyme were independently associated to three latent factors, suggesting new insights in the regulation of SCD activity. Factors were considered as new quantitative phenotypes related to prominent features of milk FA profile. With the aim of evaluating the feeding regimen and animal effects, latent factors were analysed with a mixed model, which considered the fixed effect of lactation stage, parity, some feeding regimen characteristics and the random effect of bull. Lactation stage significantly affected K m and K my factors. In perspective, the seven factors extracted by applying MFA analysis to milk FA composition could be considered as new and more informative traits to test the effect of endogenous and exogenous variation factors.

Розглянуто основні проблеми проведення оцінювання рівня управління корпоративною соціальною відповідальністю на національному та регіональному рівні. Обгрунтовано методичні засади та показники рівня управління соціальною відповідальністю бізнесу. Подано поетапний опис процесу розрахунку інтегрального індексу управління корпоративною соціальною відповідальністю. Визначено 11 показників, які мають вирішальне значення для оцінки рівня управління корпоративною соціальною відповідальністю. В основу методики оцінювання рівня управління соціальною відповідальністю бізнесу покладено багатомірний факторний аналіз та розрахунок інтегрального індексу. Результати розрахунків засвідчили кризовий стан управління корпоративною соціальною відповідальністю у Україні. Рассмотрены основные проблемы проведения оценивания состояния управления корпоративной социальной ответственностью на национальном и региональном уровнях. Обоснованы методический базис и показатели уровня управления социальной ответственностью бизнеса. Представлено поэтапное описание процесса рассчета интегрального индекса управления корпоративной социальной ответственностью. Определены 11 показателей, которые имеют решающее значение для оценки уровня управления социальной ответственностью бизнеса. В основу методики оценивания состояния управления корпоративной социальной ответственностью положены многомерный факторный анализ и рассчет интегрального индекса. Результаты рассчетов подтвердили кризисное состояние управления корпоративной социальной ответственностью в Украине. The main problems of assessing the state of corporate social responsibility management at the national and regional levels are examined. The methodical basis and indicators of the management level of business social responsibility are substantiated. A step-by-step description of the calculation process of cumulative index of corporate social responsibility management is presented. Eleven indicators of crucial importance for evaluation of the level of corporate social responsibility management are identified. The method of assessing the state of corporate social responsibility management is based on multidimensional factor analysis and the calculation of the integral index. The results of the calculations confirmed the crisis state management of corporate social responsibility in Ukraine.

Depth profiles potassium and its isotope ratio (K-40/K) were investigated in several forest soils from different geological and climatic backgrounds. The ratio was found not to be constant (0.0117%) but varied appreciably from 0.0085 to 0.0167% among different soils. Possible factors affecting variability of K-40/K values were considered by a multivariate factor analysis which suggested that aqueous parameters like humidity and pH might be a controlling factor in the surface soils. The amount (and probably chemical forms) of iron oxide may be another one influencing K-40/K values in mineral components at deeper portions.

O presente artigo possui como objetivo avaliar a distribuição espacial do babaçu na bacia do rio Cocal, estado de Tocantins. Os dados foram obtidos em levantamento de campo considerando 80 parcelas, considerando os diferentes ambientes presentes na área de estudo. O estudo considerou 16 variáveis referentes às características do babaçu (quantidade de indivíduos, quantidade de cachos, altura máxima do fuste e altura média do fuste) e do meio ambiente (altitude, declividade, área de contribuição, índice topográfico, relevo, distância do rio, distância das casas, distância da estrada, declividade do relevo no campo, solo, uso do solo e vegetação). A análise dos dados foi realizada utilizando técnicas de sistema de informações geográficas e análise multivariada. O emprego da análise fatorial múltipla permitiu classificar as variáveis ambientais de maior significância para a ocorrência do babaçu e da quantidade de cachos. O principal fator que controla a distribuição do babaçu é o manejo adotado pelos fazendeiros. This paper aims to evaluate the spatial distribution of the babassu palm in the Cocal River basin, Tocantins State. Data were obtained from field survey considering 80 plots, covering the different environments present in the study area. The research considered 16 variables about the babassu palm characteristics (number of individuals, number of babassu nut clusters, average stem height and maximum stem height) and environment features (elevation, slope, contributing area, topographic index, relief, distance from the river, distance from the homes, distance from the road, slope obtained in the field, soil, land use and vegetation). Data analysis was performed using techniques of geographic information system and multivariate analysis. The use of multiple factor analysis enabled to classify the environmental variables of greatest significance for the babassu occurrence. The main factor controlling the babassu distribution is the management adopted by farmers.

FOXP3+ and CTLA-4+ Tregs may modulate CD4+ T-cell activation and homing receptor expression in children., In adults, a majority of FOXP3+ Tregs expresses CTLA-4, and this costimulatory molecule is essential to control the expansion of other T cells. However, it remains to be investigated whether FOXP3+ and/or CTLA-4+ Tregs are associated with the expression of memory markers and homing receptors on CD4+ T cells. Thus, in a prospective newborn-infant cohort study, we examined the proportions of FOXP3+ and CTLA-4+ Tregs within the CD4+CD25+ T cell population and the fractions of CD4+ T cells that expressed CD45RA, CD45RO, HLA-DR, α4β7, CD62L, and CCR4 at several time-points during the first 3 years of life using flow cytometry. With the use of multivariate factor analysis, we found that a high proportion of FOXP3+ or CTLA-4+ Tregs during the first 18 months of life was associated positively with the fraction of T cells that expressed a naïve phenotype (CD45RA and α4β7) and inversely related to the fraction of T cells that expressed a memory phenotype (CD45RO and CCR4) later in childhood. In conclusion, FOXP3+ or CTLA-4+ Tregs may modulate CD4+ T cell activation and homing receptor expression in children.