Structural changes and compressive behavior of a dystrophic clayey Red Latosol under different use and management systems

Uma das principais conseqüências do manejo inadequado do solo é a compactação, que leva à perda da sua sustentabilidade e à redução da produtividade. O objetivo deste estudo foi avaliar as alterações estruturais e o comportamento compressivo de um Latossolo Vermelho distrófico sob Cerrado (C), plantio direto (PD) e preparo com arado de discos (AD), após duas décadas de uso e manejo. Nas profundidades de 0-5 cm e 20-30 cm coletaram-se amostras indeformadas para medir a pressão de preconsolidação, densidade do solo e a sua porosidade, e amostras deformadas para a caracterização física e química do solo. A densidade do solo variou na seguinte ordem: PD = AD > C (0-5 cm) e AD > PD > C (20-30 cm). Os dados de matéria orgânica mostraram que C = PD > AD (0-5 cm) e C = PD = AD (20-30 cm), demonstrando a capacidade de incremento da matéria orgânica pelo PD. A pressão de preconsolidação variou na seguinte ordem: PD = C > AD (0-5 cm; tensão de -1.500 kPa), e AD > C = PD (20-30 cm; tensão de -1.500 kPa).One of the consequences of inadequate soil management is its compaction that causes the loss of its sustainability and reduction of its productivity. The objective of this study was to evaluate the structural changes and the compressive behavior of a Latosol (Oxisol) under native vegetation, "Cerrado" (C), no-till (NT) and conventional till (CT) system, after two decades of use. The undisturbed soil samples were used to characterize the soil bulk density, the porous space and to perform the uniaxial compression test. The disturbed soil samples were used for soil physical and chemical characterization analyses in the analyzed depth. The bulk density varied in the following order: NT = CT > C (0 to 5 cm) and CT > NT > C (20 to 30 cm). Organic matter data showed that in the C = NT > CT (0 to 5 cm) and C = NT = CT (20 to 30 cm), showing the NT capacity of increasing organic matter. The preconsolidation pressure changed in the following order: NT = C > CT (0 to 5 cm; tension -1,500 kPa), and CT > C = NT (20 to 30 cm; tension -1,500 kPa)

Repercussões e manejo relacionados a Distúrbios Hidroeletroliticos nos pacientes graves: uma revisão sistemática com metanálise

Os distúrbios hidroeletrolíticos são eventos comumente observados na prática médica, inclusive em situações de emergência, podendo representar risco de vida ou possibilidade de sequelas para o paciente a depender da magnitude do caso. Independentemente da etiologia, a desidratação tem sua importância definida pela intensidade das perdas líquidas e pela proporção de perdas salinas em relação à perda de água. Isto evidencia a importância de se avaliar corretamente o quadro para se desenvolver um tratamento adequado. Este estudo tem como objetivo explorar o tema das repercussões e manejo de distúrbios hidroeletrolíticos nos pacientes graves a partir de uma revisão sistemática com meta análise com o emprego das palavras chave “unidade de terapia intensiva”, “gerenciamento hidroeletrolítico”, “distúrbios hidroeletrolíticos” e “controle de líquidos e eletrólitos” nos bancos de dados PubMed, BVS, Lilacs, Medline e Scielo objetivando acessar artigos publicados entre 2015 e 2022. A equipe de enfermagem está diretamente responsável pelo manejo de pacientes de alta complexidade, o que pode envolver casos que exigem o gerenciamento hidroeletrolítico, isto requer um conhecimento aprofundado dos mecanismos envolvidos no metabolismo da água e dos eletrólitos. O monitoramento diário da função renal pela equipe de enfermagem é um cuidado importante para se evitar o quadro de insuficiência renal aguda

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