Assessing the Feasibility of Implementing Membrane Distillation at the Beal Mountain Mine

Research Objectives: Verify the viability of membrane distillation applied to the Beal Mt. Mine Characterize treatment capacity and effectiveness. Design a full scale treatment facility with a 16 million gallons per year capacit

Prevalência de sarcopenia em pacientes que fazem hemodiálise em Araranguá-SC

TCC (graduação) - Universidade Federal de Santa Catarina, Campus Araranguá, Fisioterapia.Introdução: A sarcopenia é uma doença caracterizada pela perda de força e massa muscular que aumenta com o envelhecimento e se agrava por algumas doenças crônicas como a doença renal crônica (DRC). O objetivo deste estudo foi investigar a prevalência da sarcopenia em pacientes que fazem hemodiálise em Araranguá-SC. Método: Estudo transversal, com pacientes adultos que realizam hemodiálise na Clínica de Nefrologia de Araranguá-SC. Incluiu-se pacientes em hemodiálise ≥ 3 meses. A força muscular foi avaliada pela força de preensão palmar, massa muscular pela circunferência da panturrilha e o desempenho físico pelo teste de velocidade de marcha de 4 metros. A sarcopenia foi definida de acordo com o consenso europeu (EWGSOP2) como provável sarcopenia, sarcopenia confirmada e sarcopenia grave. Comparou-se a prevalência de sarcopenia entre os sexos e entre adultos < 60 anos e ≥ 60 anos por meio dos testes de Pearson ou Exato de Fischer. Resultados: Foram incluídos 45 pacientes (53 ± 17 anos, 73,3% homens). A prevalência de provável sarcopenia foi 17,8% (n=8), sarcopenia confirmada 22,2% (n=10) e sarcopenia grave 2,2% (n=1). Não houve diferença significativa entre os sexos. Os idosos apresentaram prevalência maior de provável sarcopenia (30,0% vs 8,0%; p = 0,06), mas não de sarcopenia confirmada (25,0%; vs 20,0; p = 0,68) e de sarcopenia grave (5,0 vs. 0,0; p = 0,44). Conclusão: A prevalência de sarcopenia confirmada (baixa força + baixa massa)foi a mais identificada nestes pacientes. Em pacientes idosos, houve prevalência maior de provável sarcopenia comparado aos mais jovens

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