Simulation study under a Semi-parametric Model for censored gap times

We consider a comparison between the Kaplan-Meier and the semiparametric estimators for a censorship models. The observations are assumed to be generated under a semi-parametric random censorship, this mean that a random censorship model where de conditional expectation of censoring indicator given the observations belongs to a parametric family. The semi-parametric estimator of the survival function was de ned in de U~na-Alvarez and Amorim (2011). An asymptotic representation of a general empirical integral as a sum of independent and identically distributed (i.i.d.) random variables under the proposed model was obtained in Amorim (2012). The performance of the corresponding asymptotic con dence intervals (a.c.i.) relative to that of a nonparametric method, de U~na-Alvarez and Meira-Machado (2008), is investigated through simulations Dikta et al. (2005).Project UID/MAT/00013/2013 - FCT - Fundação para a Ciência e a Tecnologi

Multi-level modelling of longitudinal child growth data: a comparison of growth models in the generation XXI birth cohort

Several methods have been used by different authors to estimate growth curves. In this paper, five growth models are compared using weight and height data from 6668 children from the Generation XXI birth cohort. The goal was to determine the model(s) that better describe the growth pattern from birth to 10 years of age using mixed-effect modelling. The study compared the fitness of four structural (Jenss-Bayley, adapted Jenss-Bayley, Berkey-Reed 1st order and Berkey-Reed 2nd order) and one non structural (cubic spline based) model. The goodness of t of the models was examined using standard deviation of the residuals, Akaike Information Criterion and Bayesian Criterion. The adapted Jenss-Bayley and the spline based model had the better tting for weight while for height the better models were Berkey-Reed 2nd order and the spline

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

A multistate model for analyzing transitions between body mass index categories during childhood: the generation XXI birth cohort study

Prevalences of overweight and obesity in young children have risen dramatically in the last several decades in most developed countries. Childhood overweight and obesity are known to have immediate and long-term health consequences and are now recognized as important public health concerns. We used a Markov 4-state model with states defined by 4 body mass index (BMI; weight (kg)/height (m)2) categories (underweight (2 SDs of BMI z score)) to study the rates of transition to higher or lower BMI categories among children aged 4–10 years. We also used this model to study the relationships between explanatory variables and their transition rates. The participants consisted of 4,887 children from the Generation XXI Birth Cohort Study (Porto, Portugal; 2005–2017) who underwent anthropometric evaluation at age 4 years and in at least 1 of the subsequent follow-up waves (ages 7 and 10 years). Children who were normal weight weremore likely tomove to higher BMI categories than to lower categories, whereas overweight children had similar rates of transition to the 2 adjacent categories. We evaluated the associations of maternal age and education, type of delivery, sex, and birth weight with childhood overweight and obesity, but we observed statistically significant results only for sex and maternal education with regard to the progressive transitions.L.M.-M. received financial support from the Spanish Ministry of Economy and Competitiveness through project M2017-82379-R, funded by the Agencia Estatal de Investigación and the European Regional Development Fund. A.C.S. holds an FCT Investigator contract (contract IF/01060/2015) from the Fundação para a Ciência e Tecnologia (FCT). The Generation XXI Birth Cohort Study was funded by Programa Operacional de Saúde XXI, Quadro Comunitário de Apoio III, and the Administração Regional de Saúde Norte (a regional department of the Portuguese Ministry of Health). The current study was funded by the Fundo Europeu de Desenvolvimento Regional through the Operational Thematic Programme for Competitiveness and Internationalization (COMPETE 2020); by the FCT, Ministério Português da Ciência, Tecnologia e Ensino Superior (grant POCI-01-0145- FEDER-016837); by the project “PathMOB: Risco Cardiometabólico na Infância: Desde o Início da Vida ao Fim da Infância” (grant FCT PTDC/DTP-EPI/3306/2014); by the Unidade de Investigação em Epidemiologia (EPIUnit), Instituto de Saúde Pública da Universidade do Porto (grant POCI-01-0145-FEDER-006862); and by the Fundação Calouste Gulbenkian (Lisbon, Portugal). This study also resulted from the DOCnet Project (“Diabetes and Obesity at the Crossroads Between Oncological and Cardiovascular Diseases—A System Analysis Network Towards Precision Medicine”) (grant NORTE-01-0145- FEDER-000003), which is supported by the Programa Operacional da Região Norte (NORTE 2020) under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq