62 research outputs found
Validação do painel de sequenciamento de nova geração para osteogênese imperfeita : análise preliminar de mutações nos genes COL1A1 e COL1A2
FrequĂŞncia de variantes patogĂŞnicas nos genes COL1A1 e COL1A2 em pacientes cadastrados na Rede Brasileira de OsteogĂŞnese Imperfeita (REBOI)
PrevalĂŞncia de variantes patogĂŞnicas nos genes COL1A1 e COL1A2 em OsteogĂŞnese imperfeita tipo III e IV
Identificação de mutações nos genes envolvidos nas modificações pós-traducionais dos procolágenos α1 e α2 em formas moderada a grave de Osteogênese imperfeita
Análise de mutações nos genes GJB2 e GJB6 relacionadas com a perda auditiva não-sindrômica
Análise da mutação 35delG no gene GJB2 e deleção del(GJB6-D13S1830) no gene GJB6 em surdez não sindrômica, remover a informação da primeira linha
Quinze anos de experiĂŞncia do centro de referencia para tratamento de osteogĂŞnese imperfeita no Hospital de Clinicas de Porto Alegre
TGFA/Taq I polymorphism and environmental factors in non-syndromic oral clefts in Southern Brazil
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
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