18 research outputs found
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
Cirurgia dermatológica e procedimentos cosmiátricos na gestação – Revisão sistemática
Considerações especiais são necessárias antes de qualquer procedimento cirúrgico durante a gravidez. Os cirurgiões dermatológicos devem considerar a melhor abordagem paraminimizar os riscos e prestar o cuidado ideal para mãe e feto.Tratamentos não emergenciais devem ser adiados até o término da gestação. Quando a cirurgia for necessária, é prudente a utilização de drogas e técnicas bem documentadas na literatura especializada
Histopatologia do saco herniário das hérnias inguinais: a importância do conhecimento morfológico sacular
O conteĂşdo do saco herniário sempre foi motivo de preocupação por parte do cirurgiĂŁo, embora a estrutura de sua parede seja ainda pouco estudada e conhecida. O objetivo do trabalho Ă© avaliar a influĂŞncia de sexo, cor, idade, regiĂŁo do saco herniário, lado da hĂ©rnia, largura, comprimento e espessura da amostra peritoneal na presença de fibras musculares lisas (FML) na parede do saco herniário inguinal. Pretende-se tambĂ©m descrever a histologia dos sacos herniários e apresentar algumas teorias sobre a origem das FML, alĂ©m de destacar a importância do conhecimento da estrutura sacular na identificação de condições patolĂłgicas encobertas e certificar o uso do prĂłprio saco como instrumento de reforço nas correções cirĂşrgicas. Amostras de 252 sacos herniários obtidos no tratamento operatĂłrio de hĂ©rnias inguinais indiretas, diretas, recidivadas e encarceradas foram encaminhadas para o estudo histopatolĂłgico, e foram coradas por Hematoxilina-Eosina (HE) e tricrĂ´mico de Gomori para a identificação de FML. Estas estiveram presentes em 67,9% das amostras, e ocorreram de modo significativo nas hĂ©rnias indiretas e recidivadas, quando comparadas com as diretas e encarceradas. Em relação Ă s variáveis estudadas, os pacientes que apresentaram FML nĂŁo diferiram significativamente daqueles em que as mesmas nĂŁo foram observadas. Quando presentes, as FML muitas vezes estavam associadas com vasos sangĂĽĂneos espessos, sugerindo a origem Ă partir da camada mĂ©dia do vaso e podem representar um reforço tecidual em resposta ao trauma mecânico ou a outros fatores da patogĂŞnese da hĂ©rnia. Foi observado tambĂ©m que o saco herniário pode sediar vários processos patolĂłgicos que atingem o peritĂ´nio parietal, como a endometriose, inflamações especĂficas e processos hiperplásicos ou mesmo neoplásicos, inclusive podendo constituir, em alguns casos, a primeira evidĂŞncia de neoplasias