Atypical presentation of pellucid marginal degeneration: case report

The authors report an unusual clinical presentation of pellucid marginal degeneration with 360º peripheral corneal thinning diagnosed in a younger male patient. We discuss the findings of topographic maps, ultrasound pachymetry and the proposed treatment.Os autores relatam o caso de paciente jovem do sexo masculino que desenvolveu quadro de degeneração marginal pelúcida bilateral atípica, com afinamento corneal periférico de 360º, abordando ainda os aspectos dos mapas topográficos e os resultados obtidos com a paquimetria ultra-sônica, além do tratamento adotado.Hospital Oftalmológico de SorocabaHospital Oftalmológico de Sorocaba Setor de CórneaHospital Oftalmológico de Sorocaba Setor de Córnea e Doenças ExternasPontifícia Universidade Católica de SorocabaUNIFESP Departamento de OftalmologiaUNIFESP, Depto. de OftalmologiaSciEL

Intracranial hypertension with ocular manifestation during the use of tetracycline: case report

The authors describe a young female patient with intracranial hypertension associated with ocular manifestations, during treatment with tetracycline. This is a rare adverse effect described in the medication warnings, and in a few reported cases in the scientific literature.Os autores relatam o caso de uma paciente jovem, do sexo feminino, que desenvolveu quadro de hipertensão intracraniana benigna com manifestações oculares em concomitância ao uso de tetraciclina para o tratamento de otite. Esta é uma reação adversa rara deste medicamento, descrito em bula e com alguns relatos de caso em literatura internacional.Hospital oftalmológico de SorocabaUniversidade Federal de São Paulo (UNIFESP)Pontifícia Universidade Católica de SorocabaUNIFESPSciEL

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

Updated cardiovascular prevention guideline of the Brazilian Society of Cardiology: 2019

Sem informação113478788

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

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at root(NN)-N-S=2.76 TeV

Peer reviewe