Filosofia e filosofia política em Hannah Arendt

This paper presents the main motivations in the political thought of Hannah Arendt. It analyses the passion of understanding, taken as reconciliation of thought and experience. And finally, it states that the agent that enables that reconciliation are the members of society that judge the political events.O artigo aborda as motivações do pensamento político de Hannah Arendt. Analisa a paixão de compreender, percebendo-a como forma de reconciliação do pensamento e da experiência. Verifica, finalmente, que o agente que possibilita a reconciliação é o espectador que julga os acontecimentos políticos

Reproductive success or failure in four breed groups of beef bulls

The objective of this study was to determine the main causes of failure in bull breeding using a soundness evaluation in Rio Grande do Sul State/Brazil. We evaluated 19,836 bulls from 15 different breeds with ages ranging from two to eight years. The failures of bulls in each step were analyzed by logistic regression. The binary logistic regression was applied because the response variable had only two responses: Success (1) and Failure (0). Older bulls are more likely to be rejected than are younger bulls, regardless of their genetic group. Depending on the step of the assessment, one or another group is rejected. All steps of bull breeding soundness evaluation (BBSE) are important, with special attention to the failures of the behavioral evaluation (libido and physical ability). A BBSE performed before the breeding season reduces the risk of sub-fertile bulls in the herd

Erratum to: The study of cardiovascular risk in adolescents – ERICA: rationale, design and sample characteristics of a national survey examining cardiovascular risk factor profile in Brazilian adolescents

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6ª Diretriz de monitorização ambulatorial da pressão arterial e 4ª diretriz de monitorização residencial da pressão arterial

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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