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

Teoria geral do processo [31.ed.]

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II brazilian consensus on antinuclear antibodies in HEp-2 cells : definitions for standardizartion of autoantibody testing against the nucleus (ANA HEp-2), nucleolus, cytoplasm and mitotic apparatus, as wel as its clinical associations

Objetivo: O Segundo Consenso Brasileiro de Fator Antinuclear (FAN) em Células HEp-2 ratificou os algoritmos de decisão para leitura dos padrões do FAN na imunofluorescência indireta vistos na primeira edição do Consenso Brasileiro, adicionando ainda um novo algoritmo relacionado com os padrões mistos. Métodos: Tendo em vista a habilidade do teste em detectar autoantígenos nos distintos compartimentos celulares, e não apenas no núcleo, propõe-se novas denominações para este exame laboratorial. Resultados e Conclusões: Como novas denominações algumas sugestões foram igualmente aceitas, dentro do tema “pesquisa de auto-anticorpos contra constituintes do núcleo (FAN HEp-2), nucléolo, citoplasma e aparelho mitótico”. Foram abordadas as principais relevâncias clínicas com os padrões de FAN descritos, facilitando o melhor uso do ensaio pelo médico.Objective: The Second Brazilian Consensus on Antinuclear Antibodies (ANA) in HEp-2 Cells approved and extended the decision trees developed during the First Brazilian Consensus in order to also offer information about mixed patterns of fluorescence. Methods: Since this test elicits reactions not only to nuclear autoimmune antigens but also to different cell compartments, new denominations for the test were approved. Results and Conclusions: These new denominations encompass variations on the “autoantibody testing against the nucleus (ANA HEp-2), nucleolus, cytoplasm, and mitotic apparatus” issue. Furthermore, major clinical associations were described for each immunofluorescent pattern, facilitating the interpretation of laboratory results in the clinical practice