Notas sobre a pesquisa na comunidade negra chã

O seguinte texto reflete sobre a pesquisa em andamento que tem como objeto de investigação a Identidade étnico-racial da Comunidade Rural Negra Chã, em Teodoro Sampaio – BA. O estudo busca tensionar e problematizar, por meio das memórias orais dos moradores, a construção da identidade étnico-racial, observando continuidades e rupturas pertinentes ao movimento que entrelaça identidade, territorialidade e cultura desse lugar. O texto traz apresentação do contexto de pesquisa, percurso metodológico e, em certa medida, autores que promovem discussões sobre identidade

Histórias de vida como imagens de si

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Vozes negras femininas: reflexões no Centro de Giro Caboclo Boiadeiro

o presente texto é resultado de notas de um trabalho de campo. Os relatos trazidos fazem parte de uma pesquisa que teve como objetivo refletir acerca das identidades e trajetórias de mulheres negras do Centro de Giro Caboclo Boiadeiro (Candomblé Angola), em Teodoro Sampaio – Bahia. Neste sentido, uma questão fundamental foi refletir sobre de que modo a experiência religiosa, através de rituais, mitos e arquétipos fundamentam as identidades e trajetórias destas mulheres. A citada pesquisa é de natureza qualitativa com base na Crítica Cultural, seu aporte metodológico, se define pela interpretação e análise de histórias de vida e narrativas memorialísticas de mulheres negras, também construídas através de entrevistas, observação das mulheres dentro e fora do espaço religioso. Assim, observou-se um processo de movência identitária tanto de Mãe Raimunda quanto do Centro de Giro

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

COVID-19 pandemic: a cross-sectional design of cases

Quantificar a evolução dos casos de COVID-19 a partir da análise dos relatórios emitidos pela Organização Mundial de Saúde no período de 21 de janeiro a 31 de maio de 2020. Metodologia: Trata-se de um estudo epidemiológico descritivo, bibliográfico e documental, construído com dados secundários obtido dos relatórios da evolução mundialdoSARS-CoV-2, da Organização Mundial de Saúde, do período inicial do surto da doença até atingir o patamar de pandemia. Os dados foram analisados, categorizados e apresentados em 3 etapas: 1) Da identificação do novo SARS-CoV-2 ao epicentro chinês em declínio; 2) Novo epicentro e disseminação da COVID-19 por regiões; 3) Epicentro da Região das Américas. Resultados: Da identificação do novo SARS-CoV-2 ao epicentro chinês, identificou-se uma explosão de casos, inicialmente na China, com vários desfechos letais em relação aos casos confirmados. Em seguida, diversos países passaram a conviver com a realidade do COVID-19, sendo a doença declarada uma pandemia. Dos casos reportados, diversos deles progrediram para doenças graves incluindo pneumonia e insuficiência respiratória. Rapidamente, os números de casos globalmente atingiram marcas expressivas. Na Europa e América houve ascensão de registros tanto de casos novos como de óbitos, especialmente no Brasil, enquanto na China o número declinava. Conclusão: No presente estudo pôde-se depreender que se trata de uma doença grave, com demanda de controle sanitário rigoroso e permanente para a redução do potencial de contaminação. As análises demonstram uma propagação acelerada do vírus com desfecho negativo, situação confirmada pela rápida expansão da doença em todos os continentes.SimTo quantify the evolution of COVID-19 cases from the analysis of reports issued by the World Health Organization from January 21 to May 31, 2020. Methodology: This is a descriptive, bibliographic and documentary epidemiological study, built with secondary data obtained from the reports of the world evolution of SARS-CoV-2, from the World Health Organization, from the initial period of the disease outbreak until reaching the pandemic level. The data were analyzed, categorized and presented in 3 stages: 1) Identification of the new SARS-CoV-2 to the Chinese epicenter in decline; 2) New epicenter and dissemination of COVID-19 by regions; 3) Epicenter of the Region of the Americas. Results: From the identification of the new SARS-CoV-2 to the Chinese epicenter, an explosion of cases was identified, initially in China, with several lethal outcomes in relation to confirmed cases. Then, several countries started to live with the reality of COVID-19, and the disease was declared a pandemic. Of the reported cases, several have progressed to serious illnesses including pneumonia and respiratory failure. Rapidly, the numbers of cases globally reached expressive marks. In Europe and America there was an increase in both new cases and deaths, especially in Brazil, while in China the number declined. Conclusion: In the present study it was possible to conclude that it is a serious disease, with a demand for strict and permanent sanitary control to reduce the potential for contamination. The analyzes show an accelerated spread of the virus with a negative outcome, a situation confirmed by the rapid expansion of the disease on all continents