Produção de mudas de manjericão sob diferentes concentrações e tipos de substrato

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Agronomia e Medicina Veterinária, 2016.O cultivo de plantas medicinais vem apresentando grande demanda mundial, utilizados por indústrias químicas, farmacêuticas, alimentícias e cosméticas. Estudos de técnicas de manejo do manjericão são requeridos devido as suas características, propriedades químicas, e utilizações. O presente estudo teve como objetivo verificar a interferência de diferentes substratos e suas diferentes concentrações, no desenvolvimento do manjericão roxo (Ocimum basilicum L). Experimento foi conduzido com delineamento em blocos casualizados, e com quatro repetições por cada tratamento. Foram testados quatro tratamentos sobre a cultura, T1: 100% Bioplant; T2: 25% Bioplant + 75% Vermiculita expandida; T3: 100% Vivatto Plus; T4: 25% Vivatto Plus + 75% Vermiculita expandida. Foram avaliadas as seguintes características: porcentagens de germinação, número de folhas, comprimento da parte aérea, comprimento de raízes, massa fresca da parte aérea, massa seca da parte aérea, massa fresca das raízes, massa seca das raízes, relação da massa fresca da parte aérea sobre a massa fresca da raiz e relação da massa seca da parte aérea sobre a massa seca da raiz. Diante dos resultados obtidos no experimento, foi possível verificar que o tratamento 4 proporcionou maior porcentagem de germinação nas sementes de manjericão roxo. O tratamento T3 apresentou maiores valores em todas as características avaliadas para desenvolvimento de mudas de manjericão roxo

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

Gasdermin-D activation by SARS-CoV-2 triggers NET and mediate COVID-19 immunopathology

Abstract: Background: The release of neutrophil extracellular traps (NETs) is associated with inflammation, coagulopathy, and organ damage found in severe cases of COVID-19. However, the molecular mechanisms underlying the release of NETs in COVID-19 remain unclear. Objectives: We aim to investigate the role of the Gasdermin-D (GSDMD) pathway on NETs release and the development of organ damage during COVID-19. Methods: We performed a single-cell transcriptome analysis in public data of bronchoalveolar lavage. Then, we enrolled 63 hospitalized patients with moderate and severe COVID-19. We analyze in blood and lung tissue samples the expression of GSDMD, presence of NETs, and signaling pathways upstreaming. Furthermore, we analyzed the treatment with disulfiram in a mouse model of SARS-CoV-2 infection. Results: We found that the SARS-CoV-2 virus directly activates the pore-forming protein GSDMD that triggers NET production and organ damage in COVID-19. Single-cell transcriptome analysis revealed that the expression of GSDMD and inflammasome-related genes were increased in COVID-19 patients. High expression of active GSDMD associated with NETs structures was found in the lung tissue of COVID-19 patients. Furthermore, we showed that activation of GSDMD in neutrophils requires active caspase1/4 and live SARS-CoV-2, which infects neutrophils. In a mouse model of SARS-CoV-2 infection, the treatment with disulfiram inhibited NETs release and reduced organ damage. Conclusion: These results demonstrated that GSDMD-dependent NETosis plays a critical role in COVID-19 immunopathology and suggests GSDMD as a novel potential target for improving the COVID-19 therapeutic strategy