Sexual dysfunction in adultwomen attended in the gynecology service of university hospital / Disfunção sexual em mulheres adultas atendidas no serviço de ginecologia do hospital universitário

Objetivos: descrever o perfil sóciodemográfico, sexual e reprodutivo, e a prevalência da disfunção sexual em mulheres adultas atendidas do Hospital Universitário. Método: estudo quantitativo, descritivo e transversal. Avaliaram-se 267 mulheres adultas entre 25 e 49 anos com pelo menos uma relação sexual na vida. Resultados: constatou-se associação significativa das disfunções sexuais femininas com coitarca menor que 15 anos, frequência de uma relação sexual mensal ou menos e lactação. A prevalência de dispaurenia foi encontrada em 30,3% das entrevistadas e vaginismo em 26,2 %. Conclusão: percebe-se que medidas preventivas minimizam a ocorrência das disfunções como: facilitar o acesso à informação, promoção e prevenção de saúde, e programas de capacitação e educação permanente. É importante construir uma abordagem holística e esforço multidisciplinar, visto que a disfunção sexual feminina constitui um largo espectro de dificuldades.

Sexual dysfunction in adultwomen attended in the gynecology service of university hospital / Disfunção sexual em mulheres adultas atendidas no serviço de ginecologia do hospital universitário

Objetivos: descrever o perfil sóciodemográfico, sexual e reprodutivo, e a prevalência da disfunção sexual em mulheres adultas atendidas do Hospital Universitário. Método: estudo quantitativo, descritivo e transversal. Avaliaram-se 267 mulheres adultas entre 25 e 49 anos com pelo menos uma relação sexual na vida. Resultados: constatou-se associação significativa das disfunções sexuais femininas com coitarca menor que 15 anos, frequência de uma relação sexual mensal ou menos e lactação. A prevalência de dispaurenia foi encontrada em 30,3% das entrevistadas e vaginismo em 26,2 %. Conclusão: percebe-se que medidas preventivas minimizam a ocorrência das disfunções como: facilitar o acesso à informação, promoção e prevenção de saúde, e programas de capacitação e educação permanente. É importante construir uma abordagem holística e esforço multidisciplinar, visto que a disfunção sexual feminina constitui um largo espectro de dificuldades.

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

Rarity of monodominance in hyperdiverse Amazonian forests.

Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such "monodominant" forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors

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