Arboviroses notification in Brazil in the COVID 19 pandemic

Introduction: During tha Sars-Cov-2 pandemic the focus was the COVID-19, but what about other disease? In this way, our study shows what happen to the arbovirus’s notifications. Objective: Analyze the record of notifications of arboviruses during the Sars-Cov-2 pandemic in 2020 in Brazil and compared with notifications occurred in between 2015 to 2020. Methods: Through a descriptive and epidemiological study, the data was collected in DATASUS BRASIL, INMET and CPTEC databases. Results: The drop in the notification of arbovirus cases in 2020 was proven, linked to the predominance of compulsory notifications of Dengue in the first half of 2020; this data confirms the seasonality of the occurrence of dengue. Similarly, Zika virus cases had a higher percentage of notifications in the first half of the year, contrasting with previous data. In addition to the persistence of the highest incidence of cases in the early months of 2020, Chikungunya notifications showed a constant percentage incident from 2017 to 2019, data on rainfall from 2015 to 2019 indicated low levels in the month of January compared to previous years, while in the month of 2020 a considerable increase of precipitation anomalies. Conclusion: Compared to previous years, the data show a considerable drop in arbovirus reports during the period of the Sars-Cov-2 pandemic in 2020. To establish more clearly the correlation between low arbovirus reporting and the COVID pandemic -19 it is necessary to carry out further studies

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

Leishmania infantum Genetic Diversity and Lutzomyia longipalpis Mitochondrial Haplotypes in Brazil

Leishmania infantum is the etiological agent of visceral leishmaniasis (VL) in the Americas with domestic dogs being its major reservoir hosts. The main VL vector is the sandfly Lutzomyia longipalpis, while other Lutzomyia species may play a role in disease transmission. Although the genetic structure of L. infantum populations has been widely evaluated, only a few studies have addressed this subject coupled to the genetic structure of the respective sandfly vectors. In this study, we analyzed the population structure of L. infantum in three major VL endemic areas in Brazil and associated it with Lutzomyia longipalpis geographic structure

Rock n' Seeds: A database of seed functional traits and germination experiments from Brazilian rock outcrop vegetation

Advancing functional ecology depends fundamentally on the availability of data on reproductive traits, including those from tropical plants, which have been historically underrepresented in global trait databases. Although some valuable databases have been created recently, they are mainly restricted to temperate areas and vegetative traits such as leaf and wood traits. Here, we present Rock n' Seeds, a database of seed functional traits and germination experiments from Brazilian rock outcrop vegetation, recognized as outstanding centers of diversity and endemism. Data were compiled through a systematic literature search, resulting in 103 publications from which seed functional traits were extracted. The database includes information on 16 functional traits for 383 taxa from 148 genera, 50 families, and 25 orders. These 16 traits include two dispersal, six production, four morphological, two biophysical, and two germination traits-the major axes of the seed ecological spectrum. The database also provides raw data for 48 germination experiments, for a total of 10,187 records for 281 taxa. Germination experiments in the database assessed the effect of a wide range of abiotic and biotic factors on germination and different dormancy-breaking treatments. Notably, 8255 of these records include daily germination counts. This input will facilitate synthesizing germination data and using this database for a myriad of ecological questions. Given the variety of seed traits and the extensive germination information made available by this database, we expect it to be a valuable resource advancing comparative functional ecology and guiding seed-based restoration and biodiversity conservation in tropical megadiverse ecosystems. There are no copyright restrictions on the data; please cite this paper when using the current data in publications; also the authors would appreciate notification of how the data are used in publications

Resumo em andamento - Arquitetura e Urbanismo

Resumo em andamento - Arquitetura e Urbanism