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

Hair Casts or Pseudonits

Hair casts or pseudonits are thin, elongated, cylindrical concretions that encircle the hair shaft and can be easily dislodged. A case of pseudonits in a 9-year-old girl is reported. Though not unusual, false diagnoses are common

Auricular Chromoblastomycosis: A Case Report and Review of Published Literature

Subcutaneous chromoblastomycosis is an infection commonly seen in tropical and subtropical climates, usually caused by trauma with vegetables and often affects the host's lower limbs. We report a case of auricular chromoblastomycosis in a 67-year-old man and discuss the rarity of this clinical manifestation of chromoblastomycosis in the medical literature. in the present case, the etiologic agent was Fonsecaea pedrosoi, the most common agent found in Brazil.Fundacao Tecn Educ Souza Marques Pele Saudavel, Curso Especializacao Dermatol, BR-01326000 São Paulo, BrazilUniversidade Federal de São Paulo, Disciplina Infectol, São Paulo, BrazilUniversidade Federal de São Paulo, Disciplina Infectol, São Paulo, BrazilWeb of Scienc

Photoprotection and skin self-examination in primary attention users : The impact of smartphone as a tool for education

Introduction: “mobile health” consists in using electronic devices to support health. Objectives: observe photoprotection, skin self-examination and compare the impact of multimedia presentation to Whatsapp messages on these habits in primary attention. Methodology: experimental study, envolving 300 users. First moment: SEPI (Sun Exposure and Protection Index) and SSEAS (Self Skin-Examination Attitudes Scale) were applied and a presentation was performed. Second moment (8 weeks):150 users received photoprotection and self-examination messages. Third moment:300 re-answered questionnaires. Differences were analysed using t test andlinear regression. Results: 39.66% used sunscreen always/frequently and, in intervention, ascended to 47.91% (p=0.0014) without change in control. Self-examination was a priority for 48.67% and increased to 73.91% in control (p=0.0179) and 69.8% (p=0.0001) in intervention, without difference. Conclusion: photoprotection remained low and self-examination became priority for twothirds (without difference between groups)