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

Comparison of diagnostic performance of RT-qPCR, RT-LAMP and IgM/IgG rapid tests for detection of SARS-CoV-2 among healthcare workers in Brazil

Background: COVID-19 has become a major public health problem after the outbreak caused by SARS-CoV-2 virus. Great efforts to contain COVID-19 transmission have been applied worldwide. In this context, accurate and fast diagnosis is essential. Methods: In this prospective study, we evaluated the clinical performance of three different RNA-based molecular tests – RT-qPCR (Charité protocol), RT-qPCR (CDC (USA) protocol) and RT-LAMP – and one rapid test for detecting anti-SARS-CoV-2 IgM and IgG antibodies. Results: Our results demonstrate that RT-qPCR using the CDC (USA) protocol is the most accurate diagnostic test among those evaluated, while oro-nasopharyngeal swabs are the most appropriate biological sample. RT-LAMP was the RNA-based molecular test with lowest sensitivity while the serological test presented the lowest sensitivity among all evaluated tests, indicating that the latter test is not a good predictor of disease in the first days after symptoms onset. Additionally, we observed higher viral load in individuals who reported more than 3 symptoms at the baseline. Nevertheless, viral load had not impacted the probability of testing positive for SARS-CoV-2. Conclusion: Our data indicates that RT-qPCR using the CDC (USA) protocol in oro-nasopharyngeal swabs samples should be the method of choice to diagnosis COVID-19