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

Pompe disease in a Brazilian series: clinical and molecular analyses with identification of nine new mutations

Pompe disease (glycogen storage disease type II or acid maltase deficiency) is an inherited autosomal recessive deficiency of acid alpha-glucosidase (GAA), with predominant manifestations of skeletal muscle weakness. A broad range of studies have been published focusing on Pompe patients from different countries, but none from Brazil. We investigated 41 patients with either infantile-onset (21 cases) or late-onset (20 cases) disease by muscle pathology, enzyme activity and GAA gene mutation screening. Molecular analyses identified 71 mutant alleles from the probands, nine of which are novel (five missense mutations c.136T > G, c.650C > T, c.1456G > C, c.1834C > T, and c.1905C > A, a splice-site mutation c.1195-2A > G, two deletions c.18_25del and c.2185delC, and one nonsense mutation c.643G > T). Interestingly, the c.1905C > A variant was detected in four unrelated patients and may represent a common Brazilian Pompe mutation. The c.2560C > T severe mutation was frequent in our population suggesting a high prevalence in Brazil. Also, eight out of the 21 infantile-onset patients have two truncating mutations predicted to abrogate protein expression. Of the ten late-onset patients who do not carry the common late-onset intronic mutation c.-32-13T > G, five (from three separate families) carry the recently described intronic mutation, c.-32-3C > A, and one sibpair carries the novel missense mutation c.1781G > C in combination with known severe mutation c.1941C > G. The association of these variants (c.1781G > C and c.-32-3C > A) with late-onset disease suggests that they allow for some residual activity in these patients. Our findings help to characterize Pompe disease in Brazil and support the need for additional studies to define the wide clinical and pathological spectrum observed in this disease.Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)[2001/00422-5]Genzyme Corporatio

Pregnancy and multiple sclerosis: the initial results from a Brazilian database Gravidez e esclerose múltipla: resultados preliminares de base de dados Brasileira

PURPOSE: Pregnancy management poses an extra challenge to physicians and their multiple sclerosis (MS) patients. There are few papers reporting databases on the subject. METHOD: Brazilian database from nine MS clinical and research units, with complete data on 47 pregnant women (49 pregnancies). RESULTS: Despite relatively high exposure to MS medications, no birth defects were reported. Low birth weight and prematurity were similar to those for developing countries. Three complications may have been associated with these medications, while three others were considered to be of purely obstetric nature. CONCLUSION: Our results confirm previous findings on lower relapse rate during pregnancy and add to the present literature informing on data related to drug exposure.<br>PROPÓSITO: O manejo da gravidez cria um desafio extra aos médicos e aos pacientes com esclerose múltipla (EM). Existem poucos trabalhos relatando bases de dados neste tema. MÉTODO: Base de dados brasileira de nove centros clínicos e de pesquisa na EM, com dados completos de 47 mulheres grávidas (49 gestações). RESULTADOS: Apesar da exposição a drogas para EM ter sido relativamente alta, não foram registradas malformações. Baixo peso e prematuridade foram semelhantes àqueles de países em desenvolvimento. Três complicações podem ter sido associadas a drogas, enquanto outras três foram consideradas como sendo de natureza puramente obstétrica. CONCLUSÃO: Nossos resultados confirmam os achados de menor taxa de surtos na gestação e adicionam dados relacionados a exposição a drogas, na literatura atual