TLR1/2 Activation during Heterologous Prime-Boost Vaccination (DNA-MVA) Enhances CD8+ T Cell Responses Providing Protection against Leishmania (Viannia)

Leishmania (Viannia) are the predominant agents of leishmaniasis in Latin America. Given the fact that leishmaniasis is a zoonosis, eradication is unlikely; a vaccine could provide effective prevention of disease. However, these parasites present a challenge and we do not fully understand what elements of the host immune defense prevent disease. We examined the ability of vaccination to protect against L. (Viannia) infection using the highly immunogenic heterologous prime-boost (DNA-modified vaccinia virus) modality and a single Leishmania antigen (TRYP). Although this mode of vaccination can induce protection against other leishmaniases (cutaneous, visceral), no protection was observed against L. (V.) panamensis. However, we found that if the vaccination was modified and the innate immune response was activated through Toll-like receptor1/2(TLR1/2) during the DNA priming, vaccinated mice were protected. Protection was dependent on CD8 T cells. Vaccinated mice had higher CD8 T cell responses and decreased levels of cytokines known to promote infection. Given the long-term persistence of CD8 T cell memory, these findings are encouraging for vaccine development. Further, these results suggest that modulation of TLR1/2 signaling could improve the efficacy of DNA-based vaccines, especially where CD8 T cell activation is critical, thereby contributing to effective and affordable anti parasitic vaccines

Abundância de gavião-real e gavião-real falso numa área sob impacto de reservatório no Baixo e Médio rio Xingu

In the Brazilian Amazon, two monospecific genera, the Harpy Eagle and Crested Eagle have low densities and are classified by IUCN as Near Threatened due to habitat loss, deforestation, habitat degradation and hunting. In this study, we evaluate occurrence of these large raptors using the environmental surveys database from Belo Monte Hydroelectric Power Plant. Integrating the dataset from two methods, we plotted a distribution map along the Xingu River, including records over a 276-km stretch of river. Terrestrial surveys (RAPELD method) were more efficient for detecting large raptors than standardized aquatic surveys, although the latter were complementary in areas without modules. About 53% of the records were obtained during activities of wildlife rescue/flushing, vegetation suppression or in transit. Between 2012 and 2014, four Harpy Eagles were removed from the wild; two shooting victims, one injured by collision with power lines and one hit by a vehicle. Also, seven nests were mapped. The mean distance between Harpy Eagle records was 15 km along the river channel, with a mean of 20 km between nests near the channel, which allowed us to estimate 20 possible pairs using the alluvial forest, riverine forest and forest fragments. Territories of another ten pairs will probably be affected by inundation of the Volta Grande channel, which is far from the main river. The average distance between Crested Eagle records was 16 km along the river channel. The only nest found was 1.3 km away from a Harpy Eagle nest. The remnant forests are under threat of being replaced by cattle pastures, so we recommend that permanently protected riparian vegetation borders (APP) be guaranteed, and that forest fragments within 5 km of the river be conserved to maintain eagle populations. © 2015, Instituto Internacional de Ecologia. All rights reserved

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