PANTANAL VALLEY

Este artigo apresenta as principais etapas para criação do Pantanal Valley, um Laboratório de Inovação Interinstitucional com foco no município de Corumbá/MS e região fronteiriça. Primeiramente, apresentamos uma análise das mudanças impostas pela inovação e seus diferentes efeitos e o conceito de laboratório de inovação. Uma vez fundamentado o conceito de laboratório de inovação, mostramos as dimensões estratégica, serviços, estrutura, aprendizagem e comunicação, nas quais um laboratório de inovação necessita ser planejado. Finalmente, conclusões e trabalhos futuros são apresentados no final do artigo

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

Vatairea guianensis lectin stimulates changes in gene expression and release of TNF‐α from rat peritoneal macrophages via glycoconjugate binding

Using a rat model of peritonitis, we herein report the inflammatory effect induced by the lectin isolated from Vatairea guianensis (VGL) seeds in the context of interactions between VGL and both toll-like receptor 4 (TLR4) and tumor necrosis factor receptor 1 (TNFR1). Peritoneal macrophages were stimulated with VGL for dose-dependent gene expression and release of TNF-alpha. In vivo results showed that VGL (1 mg/kg; intraperitoneal) induced peritonitis in female Wistar rats. Leukocyte migration, macrophage activation, and protein leakage were measured 3 and 6 hours after induction. In vitro, peritoneal macrophages were stimulated with VGL for gene expression and TNF-alpha dosage (mean +/- SEM (n = 6), analysis of variance, and Bonferroni's test (P < .05)). In silico, VGL structure was applied in molecular docking with representative glycans. It was found that (a) VGL increases vascular permeability and stimulates leukocyte migration, both rolling and adhesion; (b) lectin-induced neutrophil migration occurs via macrophage stimulation, both in vitro and in vivo; (c) lectin interacts with TLR4 and TNFR1; and (d) stimulates TNF-alpha gene expression (RT-PCR) and release from peritoneal macrophages. Thus, upon lectin-glycan binding on the cell surface, our results suggest that VGL induces an acute inflammatory response, in turn activating the release of peritoneal macrophages via TNF-alpha and TLR and/or TNFR receptor pathways