Management of invasive candidiasis and candidemia in adult non-neutropenic intensive care unit patients: Part I. Epidemiology and diagnosis

Background Invasive candidiasis and candidemia are frequently encountered in the nosocomial setting, particularly in the intensive care unit (ICU)

Management of invasive candidiasis and candidemia in adult non-neutropenic intensive care unit patients: Part II. Treatment

Background Invasive candidiasis and candidemia are frequently encountered in the nosocomial setting particularly in the intensive care unit (ICU)

DINÂMICA DE USO DA TERRA, NO SETOR AGROPECUÁRIO, EM PARAGOMINAS – PA

ABSTRACT: Knowing land use is an essential step for strategic planning of the agricultural sector. Thus, this study evaluated the dynamics of land use in the city of Paragominas from 2008 to 2014 using geoprocessing and the shift-share model. The sources of the database acquired were TerraClass project, DNIT and IBGE, obtained in electronic addresses for free, which were used in the elaboration of georeferenced maps. The land uses were annual crops, shrubby pasture, herbaceous pasture, deforestation and reforestation, on the years of 2008 and 2014. The data in hectares from the five land use classes were also analyzed by the shift-share model through the effect area decomposed into the scale and substitution effects. The results revealed that land use in Paragominas presented expansion to the activities herbaceous pasture and annual crops in the study period resulting in positive values of the scale effect. Shrubby pasture and deforestation activities revealed a decrease in area utilization which resulted in negative values of the substitution effect. The class that presented higher values in hectares used and of substitution effect was the herbaceous pasture.KEYWORDS: Amazon, Geoprocessing, Shift-share Model.RESUMEN: Conocer el uso de la tierra se presenta como paso esencial para la planificación estratégica del sector agropecuario. De esta forma, este estudio evaluó la dinámica de uso de la tierra, del sector agropecuario, en el municipio de Paragominas en el período de 2008 a 2014 por medio de técnicas de geoprocesamiento y del modelo shift-share. Las fuentes de la base de datos adquiridos fueron del Proyecto TerraClass, DNIT e IBGE, obtenidos en sus direcciones electrónicas de forma gratuita, los cuales fueron utilizados en la elaboración de mapas georreferenciados. Las clases de usos de la tierra analizadas fueron agricultura anual, pasto sucio, pasto limpio, deforestación y reforestación en los años 2008 y 2014. Los datos en hectáreas provenientes de las cinco clases de uso de la tierra también fueron analizados por el modelo shift-share a través del efecto área descompuesto en los efectos de escala y sustitución. Los resultados revelaron que el uso de la tierra en Paragominas presentó expansión para las actividades pasto limpio y agricultura anual en el período de estudio resultando en valores positivos del efecto escala. Las actividades pasto sucio y deforestación revelaron una disminución de área utilizada que resultó en valores negativos del efecto sustitutivo. La clase que presentó mayores valores en hectáreas utilizadas y de efecto sustitutivo fue el pasto limpio.PALABRAS CLAVE: Amazonia, Geoprocesamiento, Modelo Shift-Share.RESUMO: Conhecer o uso da terra apresenta-se como passo essencial para planejamento estratégico do setor agropecuário. Dessa forma, este estudo avaliou a dinâmica de uso da terra, do setor agropecuário, no município de Paragominas – PA, no período de 2008 a 2014, por meio de técnicas de geoprocessamento e do modelo shift-share. As fontes da base de dados adquiridos foram do Projeto TerraClass, DNIT e IBGE, obtidos em seus endereços eletrônicos de forma gratuita, os quais foram utilizados na elaboração de mapas georreferenciados. As classes de usos da terra analisadas foram agricultura anual, pasto sujo, pasto limpo, desflorestamento e reflorestamento nos anos de 2008 e 2014. Os dados em hectares provenientes das cinco classes de uso da terra também foram analisados pelo modelo shift-share através do efeito área decomposto nos efeitos escala e substituição.  Os resultados revelaram que uso da terra em Paragominas apresentou expansão para as atividades pasto limpo e agricultura anual no período de estudo resultando em valores positivos do efeito escala. As atividades pasto sujo e desflorestamento revelaram uma diminuição de área utilizada o que resultou em valores negativos do efeito substituição. A classe que apresentou maiores valores em hectares utilizados e de efeito substituição foi o pasto limpo.PALAVRAS-CHAVE: Amazônia, Geoprocessamento, Modelo Shift-Share

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