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

Estudo da camada de fibras nervosas da retina em pacientes com esquistossomose mansônica: análise com GDxTM Study of retinal nerve fiber layer in patients with schistosomiasis mansoni: analysis with GDX TM

OBJETIVO: Estudar a camada de fibras nervosas da retina de portadores de esquistossomose mansônica hepatoesplênica. MÉTODOS: Foram submetidos ao exame com o GDx Scanning Laser System, 23 portadores de esquistossomose na forma hepatoesplênica que tinham sido submetidos, quando crianças, a esplenectomia, ligadura da veia gástrica esquerda e auto-implante de tecido esplênico no omento maior. Todos apresentaram pressão intra-ocular menor que 21 mmHg. No grupo controle foram estudados 23 indivíduos com idade e condição socioeconômico-geográfica similar, sem esquistossomose. RESULTADOS: Em apenas um paciente do grupo portador de esquistossomose foram observadas alterações em quatro parâmetros: superior nasal, média superior, média da espessura e número de fibra. Todos os indivíduos do grupo controle apresentaram GDx com parâmetros dentro da normalidade. CONCLUSÃO: No estudo realizado não foi encontrado diferença significante entre os dois grupos. Apenas um paciente mostrou redução da camada de fibras nervosas.<br>PURPOSE: To study the retinal nerve fiber layer in young patients suffering from hepatosplenic schistosomiasis mansoni. METHODS: Twenty-three patients with hepatosplenic schistosomiasis mansoni who were submitted, when children, to splenectomy, ligature of the left gastric vein and auto-implantation of spleen tissue in the major omentum underwent GDx Scanning Laser System evaluations. All patients presented with intraocular pressure below 21 mmHg. RESULTS: Only one patient suffering from hepatosplenic schistosomiasis mansoni showed abnormalities on the GDx examination. There were no abnormalities on GDx examination in the control group. CONCLUSION: There was no significant difference between the two groups of this study. Only one patient showed retinal nerve fiber layer reduction