OS CONTOS DE CYRO MARTINS NA REVISTA DO GLOBO

RESUMOEste artigo aborda um conjunto de nove contos publicados pelo psicanalista e escritor gaúcho Cyro Martins (Quaraí, 1908-Porto Alegre, 1995) na Revista do Globo nos anos de 1933, 1935, 1942, 1946, 1947 e 1949. Além de mostrar um autor integrado à dinâmica da Livraria do Globo, as narrativas indicam dois momentos da contística de Cyro Martins: as histórias da campanha vinculadas aos temas regionalistas e as histórias nas quais interessava mais o aspecto psicológico dos personagens. Para o desenvolvimento do estudo, afora as contribuições do escritor para a Revista do Globo e sua fortuna crítica, foram utilizados trabalhos sobre o periódico – Moreira (1999), Rüdiger (2003), Hohlfeldt (2021) – e breves conceitos relacionados a sistema literário – Candido (2010), Even-Zohar (2013) – e a contos – Gotlib (1995).ABSTRACTThis article approaches a set of nine short stories published by the psychoanalyst and writer from Rio Grande do Sul Cyro Martins (Quaraí, 1908-Porto Alegre, 1995) in Revista do Globo in the years 1933, 1935, 1942, 1946, 1947 and 1949. In addition to showing an author integrated to the dynamics of Livraria do Globo, the narratives indicate two moments in Cyro Martins’ short story writing: the campaign stories linked to regionalist themes and the stories in which the psychological aspect of the characters was more interested. For the development of the study, in addition to the writer’s contributions to Revista do Globo and his critical fortune, works on the periodical were used – Moreira (1999), Rüdiger (2003), Hohlfeldt (2021) – and brief concepts related to the literary system – Candido (2010), Even-Zohar (2013) – and related short stories – Gotlib (1995)

COVID-19 outcomes in people living with HIV: Peering through the waves

Objective: To evaluate clinical characteristics and outcomes of COVID-19 patients infected with HIV, and to compare with a paired sample without HIV infection. Methods: This is a substudy of a Brazilian multicentric cohort that comprised two periods (2020 and 2021). Data was obtained through the retrospective review of medical records. Primary outcomes were admission to the intensive care unit, invasive mechanical ventilation, and death. Patients with HIV and controls were matched for age, sex, number of comorbidities, and hospital of origin using the technique of propensity score matching (up to 4:1). They were compared using the Chi-Square or Fisher's Exact tests for categorical variables and the Wilcoxon for numerical variables. Results: Throughout the study, 17,101 COVID-19 patients were hospitalized, and 130 (0.76%) of those were infected with HIV. The median age was 54 (IQR: 43.0;64.0) years in 2020 and 53 (IQR: 46.0;63.5) years in 2021, with a predominance of females in both periods. People Living with HIV (PLHIV) and their controls showed similar prevalence for admission to the ICU and invasive mechanical ventilation requirement in the two periods, with no significant differences. In 2020, in-hospital mortality was higher in the PLHIV compared to the controls (27.9% vs. 17.7%; p = 0.049), but there was no difference in mortality between groups in 2021 (25.0% vs. 25.1%; p > 0.999). Conclusions: Our results reiterate that PLHIV were at higher risk of COVID-19 mortality in the early stages of the pandemic, however, this finding did not sustain in 2021, when the mortality rate is similar to the control group

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