A imagem na indústria fonográfica: como o k-pop conquistou o mercado da música ocidental

Este artigo se propõe a relacionar teorias e conceitos da Comunicação e dos estudos da imagem com a ascensão da música pop da Coreia do Sul, o k-pop, no mercado da indústria fonográfica mundial. Por meio de uma observação espontânea dos efeitos desse fenômeno, pretende-se compreender de que forma esse gênero musical, construído dentro do ideal de uma sociedade espetacularizada, se apoiou na imagem de seus artistas para conquistar destaque para além das fronteiras sul-coreanas. Além disso, busca-se relacionar o impacto das evoluções tecnológicas na indústria da música, em especial do cenário de propagabilidade proporcionado por ferramentas como o YouTube, como um dos principais difusores do k-pop

Estudo dos fatores de risco associados à infecção pelo HIV na população de idosos

Nas últimas quatro décadas verificou-se um avanço significativo da infecção pelo Vírus da Imunodeficiência Humana (HIV), sendo, portanto, considerado um verdadeiro problema de saúde pública no Brasil e no mundo. Ao longo do tempo a infecção por esse vírus vem atingindo pessoas dos mais variáveis nível socioeconômico, estilos de vida, bem como diferentes faixas etárias. Realizar um estudo dos fatores de riscos que estão associados à infecção do HIV na população de idosos. Os procedimentos metodológicos foram realizados através de levantamento narrativo bibliográfico de natureza qualitativa, que se desenvolve a partir de toda bibliografia já publicada no período entre 2012 e 2022, pela comunidade acadêmica na área da saúde, nesse caso, nos fatores de risco associados à infecção pelo HIV na população de idosos. Esse estudo identificou algumas das evidências que levam os idosos a se tornarem vulneráveis ao HIV, tais como: carência de informações, políticas de prevenção, capacitação profissional, podendo este ser uma ferramenta útil para os profissionais de saúde repensarem a sua prática, e o preconceito ao uso de preservativos

Análise da eficácia do uso terapêutico da trimetazidina nas principais síndromes coronarianas agudas

A injúria de reperfusão, ou isquemia, é um mecanismo fisiopatológico que ocorre nas etiopatogenias do miocárdio, como na angina instável. Assim, são utilizados fármacos adjuvantes citoprotetores, como a trimetazidina (TMZ), que visam à diminuição do tempo de hospitalização e melhora na função cardíaca com ação profilática contra essa lesão. No entanto, apesar dos seus potenciais benefícios no tratamento da síndrome coronariana aguda, ainda não está clara a sua eficácia em relação a outras terapias disponíveis. Nesse sentido, o objetivo do estudo é analisar a eficácia do uso terapêutico da trimetazidina nas principais síndromes coronarianas agudas. Foi realizada uma revisão sistemática usando as bases de dados PubMed, Cochrane Library e Embase. Um total de 3 estudos foi incluído na análise. Os resultados mostraram que a terapia com trimetazidina reduziu significativamente a incidência de eventos cardíacos adversos maiores (ECAM) (OR = 0,33, IC 95% 0,15-0,75, p = 0,007), menor dano miocárdico (p < 0,05) e fração de ejeção ventricular esquerda mais elevada e menos eventos adversos em comparação com o grupo placebo (p < 0,05). Não foram observadas diferenças significativas entre os grupos de trimetazidina e controle em termos de mortalidade por todas as causas, mortalidade cardiovascular ou incidência de eventos adversos. Os resultados deste estudo sugerem que a terapia adjuvante com trimetazidina pode melhorar os resultados clínicos e a função cardíaca em pacientes com IAM sem aumentar o risco de eventos adversos. No entanto, são necessários mais ensaios clínicos randomizados em larga escala para confirmar esses resultados e determinar a duração e dose ideais da terapia com trimetazidina nessa população de pacientes

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

ATLANTIC-PRIMATES: a dataset of communities and occurrences of primates in the Atlantic Forests of South America

Primates play an important role in ecosystem functioning and offer critical insights into human evolution, biology, behavior, and emerging infectious diseases. There are 26 primate species in the Atlantic Forests of South America, 19 of them endemic. We compiled a dataset of 5,472 georeferenced locations of 26 native and 1 introduced primate species, as hybrids in the genera Callithrix and Alouatta. The dataset includes 700 primate communities, 8,121 single species occurrences and 714 estimates of primate population sizes, covering most natural forest types of the tropical and subtropical Atlantic Forest of Brazil, Paraguay and Argentina and some other biomes. On average, primate communities of the Atlantic Forest harbor 2 ± 1 species (range = 1–6). However, about 40% of primate communities contain only one species. Alouatta guariba (N = 2,188 records) and Sapajus nigritus (N = 1,127) were the species with the most records. Callicebus barbarabrownae (N = 35), Leontopithecus caissara (N = 38), and Sapajus libidinosus (N = 41) were the species with the least records. Recorded primate densities varied from 0.004 individuals/km 2 (Alouatta guariba at Fragmento do Bugre, Paraná, Brazil) to 400 individuals/km 2 (Alouatta caraya in Santiago, Rio Grande do Sul, Brazil). Our dataset reflects disparity between the numerous primate census conducted in the Atlantic Forest, in contrast to the scarcity of estimates of population sizes and densities. With these data, researchers can develop different macroecological and regional level studies, focusing on communities, populations, species co-occurrence and distribution patterns. Moreover, the data can also be used to assess the consequences of fragmentation, defaunation, and disease outbreaks on different ecological processes, such as trophic cascades, species invasion or extinction, and community dynamics. There are no copyright restrictions. Please cite this Data Paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data. © 2018 by the The Authors. Ecology © 2018 The Ecological Society of Americ

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

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