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

Cystic fibrosis mutations R1162X and 2183AA<FONT FACE=Symbol>&reg;</FONT>G in two southern Brasilian states

We screened 79 southern Brazilian patients with cystic fibrosis for the rare cystic fibrosis mutations R1162X and 2183AA<FONT FACE="Symbol">&reg;</FONT>G. Forty-nine patients were born in the State of Paraná (PR) and 30 in the State of Santa Catarina (SC). Two 2183AA<FONT FACE="Symbol">&reg;</FONT>G alleles were found among the SC patients and one among the PR patients. Six R1162X alleles were found among the SC patients and one among the PR patients. Fourteen percent of the alleles found among patients of Italian origin were R1162X, and 7% were 2183AA<FONT FACE="Symbol">&reg;</FONT>G mutations. These mutations, together with <FONT FACE="Symbol">D</FONT>F508, were also studied in a sample of 270 normal non-related subjects of Italian origin who have been born in PR. In this sample we found two <FONT FACE="Symbol">D</FONT>F508 alleles and one 2183AA<FONT FACE="Symbol">&reg;</FONT>G allele. <FONT FACE="Symbol">D</FONT>F508, R1162X and 2183AA<FONT FACE="Symbol">&reg;</FONT>G frequencies were not statistically different from those observed in Italy. Our results demonstrate that it is important to include these mutations in southern Brazilian surveys of cystic fibrosis patients, especially when they are of Italian descent.<br>Realizou-se a análise de 79 pacientes provenientes do Sul do Brasil para duas mutações raras da fibrose cística (CF), R1162X e 2183AA<FONT FACE="Symbol">&reg;</FONT>G; dentre estes pacientes, 49 eram nascidos no Estado do Paraná (PR) e 30 eram nascidos no Estado de Santa Catarina (SC). Para a mutação 2183AA<FONT FACE="Symbol">&reg;</FONT>G, dois alelos foram detectados entre os pacientes de SC e um alelo nos pacientes de PR. Para a mutação R1162X, seis alelos foram detectados entre os pacientes de SC e um alelo entre os pacientes do PR. Quando estes pacientes foram classificados de acordo com a origem étnica, 14% dos alelos detectados entre os pacientes de origem italiana eram portadores da mutação R1162X e 7% da mutação 2183AA<FONT FACE="Symbol">&reg;</FONT>G. Estas mutações, juntamente com a mutação <FONT FACE="Symbol">D</FONT>F508, também foram analisadas em uma amostra de 270 indivíduos normais de origem italiana não-consangüíneos, os quais eram nascidos no Estado do PR. Nessa amostra foram detectados dois alelos <FONT FACE="Symbol">D</FONT>F508 e um alelo 2183AA<FONT FACE="Symbol">&reg;</FONT>G. As freqüências das mutações <FONT FACE="Symbol">D</FONT>F508, R1162X e 2183AA<FONT FACE="Symbol">&reg;</FONT>G não mostraram desvio estatístico significativo daquelas freqüências observadas no norte da Itália. Nossos resultados demonstram que é importante incluir estas mutações no conjunto de mutações a serem pesquisadas nos pacientes com FC do sul do Brasil, especialmente quando estes pacientes tiverem origem italiana

cystic fibrosis mutations r1162x and 2183aa g in two southern brasilian states

We screened 79 southern Brazilian patients with cystic fibrosis for the rare cystic fibrosis mutations R1162X and 2183AA®G. Forty-nine patients were born in the State of Paraná (PR) and 30 in the State of Santa Catarina (SC). Two 2183AA®G alleles were found among the SC patients and one among the PR patients. Six R1162X alleles were found among the SC patients and one among the PR patients. Fourteen percent of the alleles found among patients of Italian origin were R1162X, and 7% were 2183AA®G mutations. These mutations, together with DF508, were also studied in a sample of 270 normal non-related subjects of Italian origin who have been born in PR. In this sample we found two DF508 alleles and one 2183AA®G allele. DF508, R1162X and 2183AA®G frequencies were not statistically different from those observed in Italy. Our results demonstrate that it is important to include these mutations in southern Brazilian surveys of cystic fibrosis patients, especially when they are of Italian descent