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

Achados histopatológicos em 431 córneas de receptores de transplantes no Rio de Janeiro

Foram examinadas 431 córneas de receptores de transplantes no Rio de Janeiro recebidas em sua maioria do Banco de Olhos associado à Sociedade Brasileira de Oftalmologia (SBO) e, as últimas, do Rio-transplante, após o fechamento temporário do Banco de Olhos. É notável a diferença entre os percentuais e o ordenamento das principais causas de transplante achadas neste levantamento, todos por comprovação histopatológica dos diagnósticos, e as citadas em bibliografia encontradas em Sorocaba, Porto Alegre, Florianópolis, Manaus e Recife baseadas apenas no levantamento dos prontuários clínicos

Characteristic aspects of alveolar proteinosis diagnosis Aspectos característicos do diagnóstico da proteinose alveolar

Alveolar proteinosis is an uncommon pulmonary disease characterized by an accumulation of surfactant in terminal airway and alveoli, thereby impairing gas exchange and engendering respiratory insufficiency in some cases. Three clinically and etiologically distinct forms of pulmonary alveolar proteinosis are recognized: congenital, secondary and idiopathic, the latter corresponding to 90% of the cases. In this case report we present a young male patient that was diagnosed with alveolar proteinosis. Computed tomography of the thorax, bronchoscopy and transbronchial biopsy were performed. The histopathologic aspect was characteristic. The patient was discharged in good health conditions and remains asymptomatic to date.<br>Proteinose alveolar é uma doença pulmonar incomum caracterizada pelo acúmulo de surfactante nas vias aéreas terminais e nos alvéolos, alterando a troca gasosa e, em alguns casos, promovendo insuficiência respiratória. Três formas clínicas e etiologicamente distintas de proteinose alveolar são reconhecidas: congênitas, secundárias e idiopáticas (mais de 90% dos casos são de etiologia idiopática). Neste relato, apresentamos um homem jovem que foi diagnosticado com proteinose pulmonar. Tomografia computadorizada de tórax, broncoscopia e biópsia transbrônquica foram realizadas. O aspecto histopatológico foi característico. O paciente teve alta, com boas condições de saúde, e encontra-se assintomático nos dias de hoje

Eosinophilic granulomatosis with polyangiitis (formerly known as Churg-Strauss syndrome) as a differential diagnosis of hypereosinophilic syndromes

Eosinophilic granulomatosis with polyangiitis (EGPA), formerly known as Churg-Strauss syndrome, is a rare systemic disease situated between primary small vessel vasculitides associated with antineutrophil cytoplasmic antibodies (ANCAs) and hypereosinophilic syndromes (HES). Here, we present a case of EGPA in a 38-year-old male, with a previous diagnosis of asthma, who presented with fever, migratory lung infiltrates and systemic eosinophilia that was refractory to previous courses of antibiotics. This case highlights the importance of the primary care physician understanding the differential diagnosis of pulmonary eosinophilic syndromes

Evaluation of grinding process using simultaneously MQL technique and cleaning jet on grinding wheel surface

In the last years, many researchers have proposed improvements to the minimum quantity lubrication (MQL) technique, in order to increase its efficiency and make it a viable alternative to the conventional application of cutting fluids for a cleaner machining. Besides the lubri-cooling effect inherent to the MQL technique, the cleaning of grinding wheel is pursued, since its surface undergoes the clogging phenomenon resulting in decreasing of the process performance. The main objective of this study is to assess the influence of the auxiliary wheel cleaning jet (WCJ) in an attempt to reduce the wheel clogging phenomenon and increase lubri-cooling and machining efficiencies in the cylindrical external grinding of AISI 4340 steel under the application of the MQL technique using aluminum oxide grinding wheel. The MQL combined with cleaning jet was compared to both flood application (conventional) and MQL without WCJ (traditional). MQL grinding employed oil flow rates of 30, 60 and 120 mL/h in order to evaluate the effects of amount of grinding fluid in the assessment. The conventional and MQL + WCJ methods produced lower surface roughness and roundness deviation compared to the other methods used due to the fact that they kept the wheel sharpness longer. Moreover, conventional and MQL + WCJ methods increased G ratio and reduced tangential grinding forces and specific energy in comparison to traditional MQL. The MQL + WCJ method enabled the removal of part of chips adhered on the grinding wheel active surface (GWAS) when compared to the traditional MQL, reducing the wheel clogging, which is one of the reasons for loss of wheel sharpness. Tubular chip form occurred not only in MQL + WCJ grinding but also in traditional MQL grinding, although it is not mentioned in literature271357367COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPNão tem2015/09197-