MONITORAMENTO DE PARÂMETROS DE QUALIDADE DA ÁGUA DO RIO PARAÍBA DO SUL EM CAMPOS DOS GOYTACAZES – RJ

A demanda atual por mecanismos de gestão de recursos hídricos que considerem a quantidade e qualidade das águas tem sido prioridade, já que se trata de um recurso de extrema importância à manutenção da vida e à continuidade das atividades econômicas. A Bacia Hidrográfica do Rio Paraíba do Sul é responsável pelo abastecimento de milhões de pessoas e indústrias ao longo dos estados de Minas Gerais, São Paulo e Rio de Janeiro. Devido a sua importância, é necessário o monitoramento contínuo visando verificar se suas águas estão adequadas para seus múltiplos usos e para manter a sua qualidade ecossistêmica. Nesse contexto, o objetivo principal do presente trabalho foi analisar alguns parâmetros de qualidade de água em um ponto do rio localizado no Baixo Paraíba do Sul, na localidade de Martins Lage, município de Campos dos Goytacazes-RJ, de forma a verificar se a qualidade da água do referido ponto está de acordo com o enquadramento concedido ao trecho do rio em questão. Para isso, foram realizadas coletas ao longo de todo o ano, entre o período de 2015 a 2018. As análises foram realizadas no Laboratório de Análise e Monitoramento das Águas, do Polo de Inovação Campos dos Goytacazes. Com os resultados obtidos, foi possível observar variações ao longo dos anos em todos os parâmetros, porém, em sua maioria, eles se encontram dentro da classificação do trecho (água doce/classe III) segundo legislação vigente

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

Autoexame da cavidade bucal

O câncer bucal está classificado entre os dez cânceres maisfrequentes. É uma neoplasia maligna localizada na gengiva, no soalho da boca ou palato, e em outras partes não específicas da boca. Este trabalho busca selecionar os oito passos do autoexame para que o cirurgião-dentista possa orientar, dialogar e educar o paciente quanto ao câncer de boca, além de o identificar e reconhecer precocemente, atuando diretamente na prevenção docâncer bucal