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

Políticas de saúde no Brasil nos anos 2000: a agenda federal de prioridades Health policies in Brazil in the 2000s: the national priority agenda

O artigo analisa as prioridades da política nacional da saúde no período de 2003 a 2008, correspondente ao Governo Lula. A pesquisa envolveu revisão bibliográfica, análise documental, análise de dados e entrevistas com dirigentes federais. Foram identificadas quatro prioridades na agenda federal da saúde: a Estratégia Saúde da Família, o Brasil Sorridente, os Serviços de Atendimento Móvel de Urgência e o programa Farmácia Popular. A primeira configura uma política de alta densidade institucional, iniciada no governo anterior, constituindo um exemplo de "dependência da trajetória". As demais foram adotadas como marcos de governo e trouxeram inovações em áreas em que havia fragilidades da atuação federal. As quatro políticas prioritárias analisadas se voltam para problemas relevantes do sistema de saúde brasileiro, porém apresentam diferenças quanto à sua trajetória, base de apoio e implicações para os princípios do Sistema Único de Saúde. Apesar de mudanças incrementais, observou-se a predominância de elementos de continuidade na política nacional de saúde no período.<br>This article analyzes Brazilian national health priorities from 2003 to 2008 under the Lula Administration. The study included a literature review, document analysis, and interviews with Federal health administrators. Four priorities were identified on the national health agenda: the Family Health Program, Smiling Brazil, Mobile Emergency Services, and the Popular Pharmacy Program. The first is a policy with high institutional density launched by the previous Administration, constituting an example of path dependence. The other three are innovations in areas where there had been weaknesses in Federal government action. The four policy priorities are strategies focused on solving key problems in the Brazilian health system. However, they display important differences in their historical development, political and institutional base, inclusion on the Federal agenda, and implications for the principles of the Unified National Health System. Although incremental changes have been introduced, national health policy has been characterized predominantly by continuity

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

Núcleos de Ensino da Unesp: artigos 2014: volume 1: processos de ensino e de aprendizagem dos conteúdos escolares

Conselho Nacional de Desenvolvimento Científco e Tecnológico (CNPq