Dental pain, use of dental services and oral health-related quality of life in southern Brazil

This study aimed at assessing the relationship between dental pain and the reason for using dental services and oral health quality of life in people aged 50 to 74 years in southern Brazil. A cross-sectional population-based study was conducted with 720 individuals aged 50 to 74 years, living in three health districts in the city of Porto Alegre. Dental impacts on daily life and sociodemographic data were assessed using structured interviews. The Oral Impacts on Daily Performance – OIDP instrument was used to measure oral impacts. The information was analyzed by Poisson regression with robust variance adjustment, taking into account cluster sampling. Dental pain was present in 32.5% of those reporting an oral impact on their daily activities. Dental pain most frequently affected talking (37.6%), cleaning teeth and gums (37.0%) and enjoying the companionship of people (36.5%). After adjustments to the multivariate analysis, the reason for dental visits due to dental pain was found to have a high impact on daily activities [RP 1.68 (1.11 - 2.54]

EXPECTATIVAS DOS ACADÊMICOS DE ODONTOLOGIA QUANTO A FORMAÇÃO E FUTURA PROFISSÃO

Considerando as mudanças que vêm ocorrendo no mercado de trabalho em Odontologia, principal-mente pelo crescimento do assalariamento da profissão, torna-se importante identificar as expecta-tivas dos estudantes de Odontologia quanto a sua formação e futura profissão. Foi aplicado umquestionário aos estudantes de três semestres distintos em duas universidades (PUC-RS e UFSM).As perguntas eram direcionadas para a caracterização sócio-demográfica, características do cursoescolhido, expectativas quanto ao exercício profissional e tendências para a pós-graduação. Verifi-cou-se que os estudantes são predominantemente solteiros e jovens, havendo diferenças quanto àclasse social e sexo entre as universidades. Os estudantes das duas instituições alegam que aescolha pelo curso de Odontologia foi motivada pelo desejo de trabalhar na área da saúde e espe-ram contar com professores capacitados durante a graduação. Quanto ao exercício profissional,esperam trabalhar em consultório privado e ter um emprego. A preferência pela especializaçãoreside nas áreas de Odontopediatria e Ortodontia. De acordo com a metodologia empregada e comos dados obtidos, conclui-se que os estudantes parecem reconhecer a tendência de assalariamentona profissão, mas mostram desejo de atuação na área privada. Dessa forma, as instituições deensino devem reconhecer as características do mercado de trabalho e adequar o ensino-aprendiza-gem de acordo com a realidade dos serviços

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

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

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics