O ensino de funções de 1º grau com a utilização do GeoGebra na educação (Matemática) do campo: uma abordagem exploratório-investigativa

O presente trabalho teve como objetivo investigar as potencialidades e limitações no uso do software Geogebra no ensino de funções afins de 1º grau, utilizando a abordagem exploratório-investigativa. os participantes consistiram em uma turma de 1° ano do ensino médio de uma escola pública situada em município da região norte do estado de minas gerais e que atende a diferentes comunidades, dentre elas populações de assentados e ribeirinhos, bem como residentes no perímetro urbano. Para a construção dos dados foram ministradas aulas sobre o referido conteúdo utilizando tecnologias digitais e, posteriormente, sistematização com exercícios que abordavam a temática. os resultados foram interpretados à luz dos obstáculos epistemológicos no ensino de funções e da metodologia de análise de erros. concluímos que a utilização de metodologias diferenciadas, que incluem o Geogebra, permite que seja despertado maior interesse dos estudantes pelos conteúdos, o que contribui para o entendimento e, consequentemente, potencializa o aprendizado

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

ATLANTIC BIRDS: a data set of bird species from the Brazilian Atlantic Forest

South America holds 30% of the world's avifauna, with the Atlantic Forest representing one of the richest regions of the Neotropics. Here we have compiled a data set on Brazilian Atlantic Forest bird occurrence (150,423) and abundance samples (N = 832 bird species; 33,119 bird individuals) using multiple methods, including qualitative surveys, mist nets, point counts, and line transects). We used four main sources of data: museum collections, on-line databases, literature sources, and unpublished reports. The data set comprises 4,122 localities and data from 1815 to 2017. Most studies were conducted in the Florestas de Interior (1,510 localities) and Serra do Mar (1,280 localities) biogeographic sub-regions. Considering the three main quantitative methods (mist net, point count, and line transect), we compiled abundance data for 745 species in 576 communities. In the data set, the most frequent species were Basileuterus culicivorus, Cyclaris gujanensis, and Conophaga lineata. There were 71 singletons, such as Lipaugus conditus and Calyptura cristata. We suggest that this small number of records reinforces the critical situation of these taxa in the Atlantic Forest. The information provided in this data set can be used for macroecological studies and to foster conservation strategies in this biodiversity hotspot. No copyright restrictions are associated with the data set. Please cite this Data Paper if data are used in publications and teaching events. © 2017 by the Ecological Society of Americ

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

ATLANTIC BIRDS: a data set of bird species from the Brazilian Atlantic Forest

South America holds 30% of the world's avifauna, with the Atlantic Forest representing one of the richest regions of the Neotropics. Here we have compiled a data set on Brazilian Atlantic Forest bird occurrence (150,423) and abundance samples (N = 832 bird species; 33,119 bird individuals) using multiple methods, including qualitative surveys, mist nets, point counts, and line transects). We used four main sources of data: museum collections, on-line databases, literature sources, and unpublished reports. The data set comprises 4,122 localities and data from 1815 to 2017. Most studies were conducted in the Florestas de Interior (1,510 localities) and Serra do Mar (1,280 localities) biogeographic sub-regions. Considering the three main quantitative methods (mist net, point count, and line transect), we compiled abundance data for 745 species in 576 communities. In the data set, the most frequent species were Basileuterus culicivorus, Cyclaris gujanensis, and Conophaga lineata. There were 71 singletons, such as Lipaugus conditus and Calyptura cristata. We suggest that this small number of records reinforces the critical situation of these taxa in the Atlantic Forest. The information provided in this data set can be used for macroecological studies and to foster conservation strategies in this biodiversity hotspot. No copyright restrictions are associated with the data set. Please cite this Data Paper if data are used in publications and teaching events. © 2017 by the Ecological Society of Americ

Duration of post-vaccination immunity against yellow fever in adults

Submitted by Nuzia Santos ([email protected]) on 2015-06-22T17:37:43Z No. of bitstreams: 1 2014_152.pdf: 756403 bytes, checksum: c18d98237e29e19e785cf895a2a68ddc (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2015-06-22T17:37:52Z (GMT) No. of bitstreams: 1 2014_152.pdf: 756403 bytes, checksum: c18d98237e29e19e785cf895a2a68ddc (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2015-06-22T17:58:36Z (GMT) No. of bitstreams: 1 2014_152.pdf: 756403 bytes, checksum: c18d98237e29e19e785cf895a2a68ddc (MD5)Made available in DSpace on 2015-06-22T17:58:36Z (GMT). No. of bitstreams: 1 2014_152.pdf: 756403 bytes, checksum: c18d98237e29e19e785cf895a2a68ddc (MD5) Previous issue date: 2014Fundação Oswaldo Cruz. Brasilia, DF, BrasilFundação Oswaldo Cruz. Escola Nacional de Saúde Pública. Rio de Janeiro, RJ, BrazilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Biomarcadores Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicosde Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos de Bio-Manguinhos. Rio de Janeiro, RJ, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Biomarcadores. Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Biomarcadores. Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Imunopatologia .Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Esquistossomose. Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Biomarcadores. Belo Horizonte, MG, BrasilFundação Oswaldo Cruz. Centro de Pesquisa Rene Rachou. Laboratório de Biomarcadores. Belo Horizonte, MG, BrasilFood and Drug Administration Center for Biologics Evaluation and Research. Bethesda, USA.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratorio de Fla-vivirus. Rio de JaneiroFundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratorio de Fla-vivirus. Rio de JaneiroFundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratorio de Fla-vivirus. Rio de JaneiroInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilInstituto de Biologia do Exército. Rio de Janeiro, RJ, BrasilMinas Gerais. Secretaria Estadual de Saude. Belo Horizonte, MG, BrasilMinas Gerais. Secretaria Estadual de Saude. Belo Horizonte, MG, BrasilMinas Gerais. Secretaria Estadual de Saude. Belo Horizonte, MG, BrasilMinas Gerais. Secretaria Estadual de Saude. Belo Horizonte, MG, BrasilUniversidade Federal de Alfenas. Alfenas, MG, BrasilUniversidade de Brasília. Faculdade de Medicina. Brasilia, DF, BrasilFundação Oswaldo Cruz. Instituto Evandro Chagas. Ananindeua, PA, BrasilINTRODUCTION: Available scientific evidence to recommend or to advise against booster doses of yellow fever vaccine (YFV) is inconclusive. A study to estimate the seropositivity rate and geometric mean titres (GMT) of adults with varied times of vaccination was aimed to provide elements to revise the need and the timing of revaccination. METHODS: Adults from the cities of Rio de Janeiro and Alfenas located in non-endemic areas in the Southeast of Brazil, who had one dose of YFV, were tested for YF neutralising antibodies and dengue IgG. Time (in years) since vaccination was based on immunisation cards and other reliable records. RESULTS: From 2011 to 2012 we recruited 691 subjects (73% males), aged 18-83 years. Time since vaccination ranged from 30 days to 18 years. Seropositivity rates (95%C.I.) and GMT (International Units/mL; 95%C.I.) decreased with time since vaccination: 93% (88-96%), 8.8 (7.0-10.9) IU/mL for newly vaccinated; 94% (88-97), 3.0 (2.5-3.6) IU/mL after 1-4 years; 83% (74-90), 2.2 (1.7-2.8) IU/mL after 5-9 years; 76% (68-83), 1.7 (1.4-2.0) IU/mL after 10-11 years; and 85% (80-90), 2.1 (1.7-2.5) IU/mL after 12 years or more. YF seropositivity rates were not affected by previous dengue infection. CONCLUSIONS:Eventhough serological correlates of protection for yellow fever are unknown, seronegativity in vaccinated subjects may indicate primary immunisation failure, or waning of immunity to levels below the protection threshold. Immunogenicity of YFV under routine conditions of immunisation services is likely to be lower than in controlled studies. Moreover, infants and toddlers, who comprise the main target group in YF endemic regions, and populations with high HIV infection rates, respond to YFV with lower antibody levels. In those settings one booster dose, preferably sooner than currently recommended, seems to be necessary to ensure longer protection for all vaccinee