COMPARATIVE STUDY EVALUATION OF PHYSICAL ACTIVITY LEVELS AND FUNCTIONAL AUTONOMY IN ELDERLY PRACTITIONERS OF PHYSICAL ACTIVITIES

Objetivo: Avaliar e comparar os níveis de atividade física e autonomia funcional em idosas praticantes de atividades. Métodos: Para determinar o nível de atividade física, utilizou-se a versão do Questionário Baecke Modificado para Idosos e a capacidade funcional foi avaliada pelos testes do GDLAM. Os dados foram analisados através do SPSS, versão 16.0. O nível de significância e erro estatístico considerados foi de 5%. Resultados: Verifica-se que as idosas participantes de atividades físicas formais obtiveram os melhores resultados, havendo diferença estatística significante (p 27,42).Conclusão: As idosas praticantes de atividades físicas formais apresentaram melhores resultados para os níveis de atividade física e autonomia funcional. Descritores: Atividade física, Capacidade funcional, Idoso.

COMPARATIVE STUDY EVALUATION OF PHYSICAL ACTIVITY LEVELS AND FUNCTIONAL AUTONOMY IN ELDERLY PRACTITIONERS OF PHYSICAL ACTIVITIES

Objetivo: Avaliar e comparar os níveis de atividade física e autonomia funcional em idosas praticantes de atividades. Métodos: Para determinar o nível de atividade física, utilizou-se a versão do Questionário Baecke Modificado para Idosos e a capacidade funcional foi avaliada pelos testes do GDLAM. Os dados foram analisados através do SPSS, versão 16.0. O nível de significância e erro estatístico considerados foi de 5%. Resultados: Verifica-se que as idosas participantes de atividades físicas formais obtiveram os melhores resultados, havendo diferença estatística significante (p 27,42). Conclusão: As idosas praticantes de atividades físicas formais apresentaram melhores resultados para os níveis de atividade física e autonomia funcional. Descritores: Atividade física, Capacidade funcional, Idoso.

A computational literature review of football performance analysis through probabilistic topic modeling

Principe, V. A., de Souza Vale, R. G., de Castro, J. B. P., Carvano, L. M., Henriques, R. A. P., Lobo, V. J. D. A. E. S., & de Alkmim Moreira Nunes, R. (2022). A computational literature review of football performance analysis through probabilistic topic modeling. Artificial Intelligence Review, 55(2). [Advanced online publication on 4 April 2021]. https://doi.org/10.1007/s10462-021-09998-8This research aims to illustrate the potential use of concepts, techniques, and mining process tools to improve the systematic review process. Thus, a review was performed on two online databases (Scopus and ISI Web of Science) from 2012 to 2019. A total of 9649 studies were identified, which were analyzed using probabilistic topic modeling procedures within a machine learning approach. The Latent Dirichlet Allocation method, chosen for modeling, required the following stages: 1) data cleansing, and 2) data modeling into topics for coherence and perplexity analysis. All research was conducted according to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses in a fully computerized way. The computational literature review is an integral part of a broader literature review process. The results presented met three criteria: (1) literature review for a research area, (2) analysis and classification of journals, and (3) analysis and classification of academic and individual research teams. The contribution of the article is to demonstrate how the publication network is formed in this particular field of research, and how the content of abstracts can be automatically analyzed to provide a set of research topics for quick understanding and application in future projects.authorsversionpublishe

Variáveis hidroclimáticas associadas com eventos de El-Niño e La-Niña no reservatório hidrelétrico de Curuá-Una, Amazônia Central

The anomalies of Sea Surface Temperature (SST) influence rainfall and therefore the regime of the rise and fall in the level of the rivers in the Amazon region. The aim of this study was to investigate the influence of the El Nino-Southern Oscillation (ENSO) on hydroclimatic variables and identify the existence of trends on these variables in the Curuá-Una hydroelectric reservoir in the West of the State of Pará. It was used 27 years of monthly precipitation and water flow data to identify possible trends using a non-parametric test (Mann Kendall, p<0.05), and the standardized precipitation index (SPI) was also calculated. The results indicate a positive tendency of the influence of the ENSO on hydroclimatic variables, although it was observed that the rainfall did not increase over the period of 1977 to 2004. The SPI indicates that extreme events of precipitation are related to El Nino and La Nina and that lower precipitation periods were more intense in the decades of the 80´s and 90’s. The results show that El Nino events can directly affect the water balance at the micro-watershed of Curuá-Una, as was observed in 2015. © 2016, Instituto Nacional de Pesquisas da Amazonia. All rights reserved

Potencial de indivíduos arbóreos como facilitadoras da formação de banco de sementes em um ecótono de Cerrado e Caatinga

This study verified the differences in richness, density and species composition of the seed bank under the canopy s tree species and the adjacent field area and the relationship of total stem height and crown area to density and richness bank seeds. The seed bank was collected under the canopy of 10 individuals of each species (Curatella americana L., Luetzelburgia auriculata (Allemão) Duckee, Copernicia prunifera (Mill.) H.E.Moore)), and at 10 points in an adjacent field area. The density and richness of the seed bank was larger under the crowns of the species. The total stem height showed a positive relationship with richness and density of the seed bank, and a canopy area negative relationship. The tree species studied enhance the generation of seed bank, increasing its richness and density. They also influence species distribution and local diversity.Este estudo verificou as diferenças na riqueza, densidade e composição de espécies do banco de sementes sob a copa es espécies arbóreas e a área de campo adjacentes e a relação entre altura total do caule e área da copa com a densidade e riqueza do banco de sementes. Foram coletados o banco de sementes sob a copa de 10 indivíduos de cada espécies (Curatella americana L., Luetzelburgia auriculata (Allemão) Duckee, Copernicia prunifera (Mill.) H.E.Moore)), e em 10 pontos em área de campo adjacente. A densidade e riqueza do banco de sementes foram maiores sob as copas das espécies. A altura do caule apresentou relação positiva com riqueza e densidade do banco de sementes e, área da copa relação negativa. As espécies arbóreas estudadas potencializam a formação de banco de sementes, aumentando a riqueza e densidade. Também influenciam na distribuição das espécies e na diversidade local

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

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI &lt;18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For school&#x2;aged children and adolescents, we report thinness (BMI &lt;2 SD below the median of the WHO growth reference) and obesity (BMI &gt;2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesit