Robustez das infraestruturas de transporte de países emergentes às mudanças do clima / The robustness of emerging countries' transport infrastructures to climate change

Os sistemas de transporte têm se tornado mais vulneráveis a incidentes, dado que a presença de congestionamentos tende a crescer conforme o processo de urbanização avança. Logo, torna-se mais difícil a recuperação perante incidentes, visto que a capacidade disponível para acomodar novas distribuições de fluxos é cada vez menor. Os métodos tradicionais para avaliação do risco de inundações, baseados em registros históricos, com frequência ignoram as tendências de longo prazo, tais como as decorrentes de mudanças climáticas oriundas de ações antropogênicas. O objetivo desse trabalho é avaliar a robustez de uma rede viária perante eventos extremos de inundações associados às mudanças climáticas por meio da proposição de um método. Para isso, conduziu-se uma revisão bibliográfica sistemática para identificar os estudos que relacionam transporte e robustez com eventos extremos. Os resultados mostram que é possível estabelecer um método de análise que permite a criação de um ranking de prioridades em função dos potenciais danos

Seleção de indicadores ambientais com foco em armazém verde: Selection of environmental indicators with focus on green warehouse

As emissões geradas por edifícios de logística, incluindo armazéns e instalações de triagem, são significativas, representando cerca de 11% das emissões da cadeia de suprimentos. No caso da operação de um armazém, a iluminação, refrigeração/aquecimento e equipamentos de carga são os principais responsáveis pelo consumo energético e emissão de Gases de Efeito Estufa (GEE). Entretanto, a norma ISO 14001, que trata da gestão ambiental, não define quais os principais indicadores a serem utilizados para avaliação e monitoramento da operação. Desta forma, foi identificada a necessidade de se adotar práticas verde na operação de armazém. A proposta deste artigo é apresentar um método para selecionar indicadores que permitam avaliar e monitorar práticas que tornem a operação do armazém, de fato, verde. Esta pesquisa analisa a confiabilidade dos indicadores escolhidos por meio do método alfa de Cronbach, seguido de sua validação. O resultado aponta 27 indicadores mais relevantes a serem considerados para armazém verde

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

Cryptococcus neoformans Secretes Small Molecules That Inhibit IL-1β Inflammasome-Dependent Secretion

Cryptococcus neoformans is an encapsulated yeast that causes disease mainly in immunosuppressed hosts. It is considered a facultative intracellular pathogen because of its capacity to survive and replicate inside phagocytes, especially macrophages. This ability is heavily dependent on various virulence factors, particularly the glucuronoxylomannan (GXM) component of the polysaccharide capsule. Inflammasome activation in phagocytes is usually protective against fungal infections, including cryptococcosis. Nevertheless, recognition of C. neoformans by inflammasome receptors requires specific changes in morphology or the opsonization of the yeast, impairing proper inflammasome function. In this context, we analyzed the impact of molecules secreted by C. neoformans B3501 strain and its acapsular mutant Δcap67 in inflammasome activation in an in vitro model. Our results showed that conditioned media derived from B3501 was capable of inhibiting inflammasome-dependent events (i.e., IL-1β secretion and LDH release via pyroptosis) more strongly than conditioned media from Δcap67, regardless of GXM presence. We also demonstrated that macrophages treated with conditioned media were less responsive against infection with the virulent strain H99, exhibiting lower rates of phagocytosis, increased fungal burdens, and enhanced vomocytosis. Moreover, we showed that the aromatic metabolite DL-Indole-3-lactic acid (ILA) and DL-p-Hydroxyphenyllactic acid (HPLA) were present in B3501’s conditioned media and that ILA alone or with HPLA is involved in the regulation of inflammasome activation by C. neoformans. These results were confirmed by in vivo experiments, where exposure to conditioned media led to higher fungal burdens in Acanthamoeba castellanii culture as well as in higher fungal loads in the lungs of infected mice. Overall, the results presented show that conditioned media from a wild-type strain can inhibit a vital recognition pathway and subsequent fungicidal functions of macrophages, contributing to fungal survival in vitro and in vivo and suggesting that secretion of aromatic metabolites, such as ILA, during cryptococcal infections fundamentally impacts pathogenesis