Relation entre le traitement maternel en bas âge et la régulation de la tristesse à huit ans : une étude de jumeaux

La présente étude porte sur les aspects Nature-Culture relatifs à l’émergence de variations interindividuelles quant à la capacité universelle de régulation d’une émotion primaire, la tristesse. Cette problématique représente un exemple du lien entre la conception évolutionniste d’une nature humaine universelle, innée et génétiquement prescrite, mais susceptible de variation dans son expression en fonction d’expériences individuelles liées aux processus de socialisation et d’enculturation. À l’aide du devis génétiquement informatif des jumeaux, nous nous sommes d’abord penchés sur l’étiologie gènes-environnement de la dépression à l’enfance, une dysfonction du système de régulation émotionnelle de la tristesse. Puis, nous nous sommes interrogés quant à l’influence du traitement et de l’état psychique maternels sur cet aspect du développement émotionnel de l’enfant. Nos analyses de la symptomatologie dépressive indiquent une absence d’influence génétique dans le développement de ce trouble de l’humeur. Les variations individuelles de la régulation de la tristesse reposent ainsi uniquement sur les effets de l’environnement. Nos résultats révèlent également l’existence d’une relation importante entre l’état psychique de la mère, évalué lorsque les jumeaux avaient cinq mois, et la présence de symptômes dépressifs chez ces derniers mesurés huit ans plus tard. L’état psychique de la mère est considéré comme l’un des meilleurs indicateurs de la qualité du traitement maternel en bas âge. Nos mesures directes des comportements maternels envers le nourrisson et le développement ultérieur du trouble de dépression indiquent également l’existence de tendances statistiques allant dans le sens de notre hypothèse d’un traitement maternel sous-optimal contribuant au développement de dysfonctions émotionnelles ultérieures.

Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fuzzy modeling to predict chicken egg hatchability in commercial hatchery

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

Comparison of a traditional and a multilevel Cox Proportional Hazards model

Hierarchically clustered populations are often encountered in public health research, but the traditional methods used in analyzing this type of data are not always adequate. In the case of survival time data, more appropriate methods have only begun to surface in the last couple of decades. Such methods include multilevel statistical techniques which, although more complicated to implement than traditional methods, are more appropriate. ^ One population that is known to exhibit a hierarchical structure is that of patients who utilize the health care system of the Department of Veterans Affairs where patients are grouped not only by hospital, but also by geographic network (VISN). This project analyzes survival time data sets housed at the Houston Veterans Affairs Medical Center Research Department using two different Cox Proportional Hazards regression models, a traditional model and a multilevel model. VISNs that exhibit significantly higher or lower survival rates than the rest are identified separately for each model. ^ In this particular case, although there are differences in the results of the two models, it is not enough to warrant using the more complex multilevel technique. This is shown by the small estimates of variance associated with levels two and three in the multilevel Cox analysis. Much of the differences that are exhibited in identification of VISNs with high or low survival rates is attributable to computer hardware difficulties rather than to any significant improvements in the model. ^

Statistical issues on studies of mixed longitudinal design

Mixed longitudinal designs are important study designs for many areas of medical research. Mixed longitudinal studies have several advantages over cross-sectional or pure longitudinal studies, including shorter study completion time and ability to separate time and age effects, thus are an attractive choice. Statistical methodology used in general longitudinal studies has been rapidly developing within the last few decades. Common approaches for statistical modeling in studies with mixed longitudinal designs have been the linear mixed-effects model incorporating an age or time effect. The general linear mixed-effects model is considered an appropriate choice to analyze repeated measurements data in longitudinal studies. However, common use of linear mixed-effects model on mixed longitudinal studies often incorporates age as the only random-effect but fails to take into consideration the cohort effect in conducting statistical inferences on age-related trajectories of outcome measurements. We believe special attention should be paid to cohort effects when analyzing data in mixed longitudinal designs with multiple overlapping cohorts. Thus, this has become an important statistical issue to address. ^ This research aims to address statistical issues related to mixed longitudinal studies. The proposed study examined the existing statistical analysis methods for the mixed longitudinal designs and developed an alternative analytic method to incorporate effects from multiple overlapping cohorts as well as from different aged subjects. The proposed study used simulation to evaluate the performance of the proposed analytic method by comparing it with the commonly-used model. Finally, the study applied the proposed analytic method to the data collected by an existing study Project HeartBeat!, which had been evaluated using traditional analytic techniques. Project HeartBeat! is a longitudinal study of cardiovascular disease (CVD) risk factors in childhood and adolescence using a mixed longitudinal design. The proposed model was used to evaluate four blood lipids adjusting for age, gender, race/ethnicity, and endocrine hormones. The result of this dissertation suggest the proposed analytic model could be a more flexible and reliable choice than the traditional model in terms of fitting data to provide more accurate estimates in mixed longitudinal studies. Conceptually, the proposed model described in this study has useful features, including consideration of effects from multiple overlapping cohorts, and is an attractive approach for analyzing data in mixed longitudinal design studies.^

Structural Time Series Modeling: an Application to Environmental Variables

A structural time series model is one which is set up in terms of components which have a direct interpretation. In this paper, the discussion focuses on the dynamic modeling procedure based on the state space approach (associated to the Kalman filter), in the context of surface water quality monitoring, in order to analyze and evaluate the temporal evolution of the environmental variables, and thus identify trends or possible changes in water quality (change point detection). The approach is applied to environmental time series: time series of surface water quality variables in a river basin. The statistical modeling procedure is applied to monthly values of physico- chemical variables measured in a network of 8 water monitoring sites over a 15-year period (1999-2014) in the River Ave hydrological basin located in the Northwest region of Portugal.

Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados

Water regimes in the Brazilian Cerrados are sensitive to climatological disturbances and human intervention. The risk that critical water-table levels are exceeded over long periods of time can be estimated by applying stochastic methods in modeling the dynamic relationship between water levels and driving forces such as precipitation and evapotranspiration. In this study, a transfer function-noise model, the so called PIRFICT-model, is applied to estimate the dynamic relationship between water-table depth and precipitation surplus/deficit in a watershed with a groundwater monitoring scheme in the Brazilian Cerrados. Critical limits were defined for a period in the Cerrados agricultural calendar, the end of the rainy season, when extremely shallow levels (< 0.5-m depth) can pose a risk to plant health and machinery before harvesting. By simulating time-series models, the risk of exceeding critical thresholds during a continuous period of time (e.g. 10 days) is described by probability levels. These simulated probabilities were interpolated spatially using universal kriging, incorporating information related to the drainage basin from a digital elevation model. The resulting map reduced model uncertainty. Three areas were defined as presenting potential risk at the end of the rainy season. These areas deserve attention with respect to water-management and land-use planning.

O modelamento estatístico de misturas: experimento tutorial usando voltametria de redissolução anódica

This work illustrates the modeling procedure for a solvent mixture using the simplex- centroid approach. The selected experiment was the optimization of the peak current observed in the direct determination of nickel by anodic stripping voltammetry (ASV) in a solvent mixture composed of N,N-dimethylformamide, ethanol and water. The text is presented in a tutorial way, showing in detail the several steps which must be followed in such a process. Since not all possible mixtures lead to a measurable instrumental response, pseudocomponents had to be used to rescale the experimental design. This also allows to show how to apply this tool, usually troublesome for non-specialists in mixture modeling procedures.

Causas do declínio da desnutrição infantil no Brasil, 1996-2007

OBJETIVO: Estabelecer a evolução da prevalência de desnutrição na população brasileira de crianças menores de cinco anos de idade entre 1996 e 2007 e identificar os principais fatores responsáveis por essa evolução.MÉTODOS: Os dados analisados procedem de inquéritos "Demographic Health Surveys" realizados no Brasil em 1996 e 2006/7 em amostras probabilísticas de cerca de 4 mil crianças menores de cinco anos. A identificação dos fatores responsáveis pela variação temporal da prevalência da desnutrição (altura-para-idade inferior a -2 escores z; padrão OMS 2006) considerou mudanças na distribuição de quatro determinantes potenciais do estado nutricional. Modelagem estatística da associação independente entre determinante e risco de desnutrição em cada inquérito e cálculo de frações atribuíveis parciais foram utilizados para avaliar a importância relativa de cada fator na evolução da desnutrição infantil. RESULTADOS: A prevalência da desnutrição foi reduzida em cerca de 50%: de 13,5% (IC 95%: 12,1%;14,8%) em 1996 para 6,8% (5,4%;8,3%) em 2006/7. Dois terços dessa redução poderiam ser atribuídos à evolução favorável dos quatro fatores estudados: 25,7% ao aumento da escolaridade materna; 21,7% ao crescimento do poder aquisitivo das famílias; 11,6% à expansão da assistência à saúde e 4,3% à melhoria nas condições de saneamento.CONCLUSÕES: A taxa anual de declínio de 6,3% na proporção de crianças com déficits de altura-para-idade indica que em cerca de mais dez anos a desnutrição infantil poderia deixar de ser um problema de saúde pública no Brasil. A conquista desse resultado dependerá da manutenção das políticas econômicas e sociais que têm favorecido o aumento do poder aquisitivo dos mais pobres e de investimentos públicos que permitam completar a universalização do acesso da população brasileira aos serviços essenciais de educação, saúde e saneamento

Causas do declínio acelerado da desnutrição infantil no Nordeste do Brasil (1986-1996-2006)

OBJETIVO: Descrever a variação temporal na prevalência de desnutrição infantil na região Nordeste do Brasil, em dois períodos sucessivos, identificando os principais fatores responsáveis pela evolução observada em cada período. MÉTODOS: Os dados analisados provêm de amostras probabilísticas da população de crianças menores de cinco anos estudadas por inquéritos domiciliares do programa Demographic Health Surveys realizados em 1986 (n=1.302), 1996 (n=1.108) e 2006 (n=950). A identificação dos fatores responsáveis pela variação na prevalência da desnutrição (altura para idade < -2 z) levou em conta mudanças na freqüência de cinco determinantes potenciais do estado nutricional, modelagens estatísticas da associação independente entre determinante e risco de desnutrição no início de cada período e cálculo de frações atribuíveis. RESULTADOS: A prevalência da desnutrição foi reduzida em um terço de 1986 a 1996 (de 33,9 por cento para 22,2 por cento ) e em quase três quartos de 1996 a 2006(de 22,2 por cento para 5,9 por cento ). Melhorias na escolaridade materna e na disponibilidade de serviços de saneamento foram particularmente importantes para o declínio da desnutrição no primeiro período, enquanto no segundo período foram decisivos o aumento do poder aquisitivo das famílias mais pobres e, novamente, a melhoria da escolaridade materna. CONCLUSÕES: A aceleração do declínio da desnutrição do primeiro para o segundo período foi consistente com a aceleração de melhorias em escolaridade materna, saneamento, assistência à saúde e antecedentes reprodutivos e, sobretudo, com o excepcional aumento do poder aquisitivo familiar, observado apenas no segundo período. Mantida a taxa de declínio observada entre 1996 e 2006, o problema da desnutrição infantil na região Nordeste poderia ser considerado controlado em menos de dez anos. ) Para se chegar a este resultado será preciso manter o aumento do poder aquisitivo dos mais pobres e assegurar investimentos públicos para completar a universalização do acesso a serviços essenciais de educação, saúde e saneamento

Role of the wider Azores region as a nursery ground for North Atlantic blue shark (Prionace glauca)

Tese de Doutoramento, Ciências do Mar (Ecologia Marinha)

Handling random errors and biases in methods used for short-term dietary assessment

Epidemiological studies have shown the effect of diet on the incidence of chronic diseases; however, proper planning, designing, and statistical modeling are necessary to obtain precise and accurate food consumption data. Evaluation methods used for short-term assessment of food consumption of a population, such as tracking of food intake over 24h or food diaries, can be affected by random errors or biases inherent to the method. Statistical modeling is used to handle random errors, whereas proper designing and sampling are essential for controlling biases. The present study aimed to analyze potential biases and random errors and determine how they affect the results. We also aimed to identify ways to prevent them and/or to use statistical approaches in epidemiological studies involving dietary assessments.

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Extreme value Birnbaum-Saunders regression models applied to environmental data

Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

O efeito da deposição salina nas características das escorrências rodoviárias em zonas costeiras

Tese de Doutoramento em Engenharia Civil.