Efeitos térmicos em meteoritos primitivos

Neste projeto estudamos a modelagem termodinâmica de meteoritos condritos, em específico os carbonáceos CO e CV, com o objetivo de simular seu aquecimento e eventual derretimento. Condritos CO e CV são geralmente associados a asteroides localizados na parte externa do cinturão principal, onde recentemente foram encontrados indícios de corpos com composições característica de um processo de aquecimento, como é o caso da família de Eos. Na tentativa de modelar a composição destes objetos, nossos resultados mostram que um derretimento parcial de ~50-60% de um corpo CO ou CV produziria um resíduo com a composição esperada para a olivina, sem entretanto reproduzir a presença do ortopiroxênio. Futuramente prosseguiremos com a modelagem utilizando outras composições condríticas e devidas condições de oxidação

Programa multidisciplinar para acompanhamento dos pacientes adolescentes e adultos com fibrose cística do Hospital de Clínicas de Porto Alegre : 5 anos de experiência

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Carta à Comunidade Científica: o Brasil frente às novas variantes de SARSCoV- 2

This letter discusses the epidemic situation of Covid-19 in Brazil, in the face of the emergence of a new strain, called P1, more transmissible and with possible associated reinfection.  Given the collapse of hospital care in Manaus in January 2021 and the results of three recent preprints, all of which found increased transmissibility of the P.1 variant, we propose some urgent actions: the establishment of genomic surveillance based on multi-step diagnostics, starting with RT-PCR type tests  to sequencing; an immediate effort to identify reinfections associated with the new variant, updating its definition protocols; and studies on the efficacy of currently available vaccines in Brazil in respect to the new variant.  We also propose the improvement of the Brazilian health surveillance system, which should be articulated with genomic surveillance, in order to respond more timely to future emergencies. We call on the public agents involved in health surveillance to share data and information regarding the epidemic in a clear, fast and transparent way. Finally, we propose a greater engagement in inter-institutional cooperation of all those involved in the response and production of knowledge about the pandemic in our country.Esta carta discute a situação epidêmica da Covid-19 no Brasil frente aoaparecimento de uma nova linhagem, chamada P1, mais transmissível e compossível re-infecção associada. Tendo em vista o colapso do atendimentohospitalar em Manaus em janeiro de 2021 e os resultados de três preprintsrecentes, todos encontrando maior transmissibilidade da variante P.1, propomos algumas ações urgentes: o estabelecimento de uma vigilância genômica baseada em diagnóstico em múltiplos passos, iniciando com os testes do tipo RT-PCR até o sequenciamento; um esforço imediato na identificação de re-infecções associadas à nova variante, atualizando os seus protocolos de definição; e estudos sobre a eficácia das vacinas atualmente disponíveis no Brasil na vigência da nova variante. Propomos, ademais, o aprimoramento do sistema de vigilância em saúde brasileiro, que seja articulado com a vigilância genômica, de forma a responder mais oportunamente a emergências futuras. Chamamos os agentes públicos implicados na vigilância em saúde para que compartilhem dados e informações referentes à epidemia de forma clara, rápida e transparente. Finalmente propomos um maior engajamento na cooperação inter-institucional de todos os envolvidos na resposta e produção de conhecimento sobre a pandemia em nosso país

Brazil in the face of new SARS-CoV-2 variantsemergencies and challenges in public health

Peer reviewedPublisher PD

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