In Gaming Advertising: a percepção do consumidor de jogos eletrônicos

O presente estudo tem como objetivo identificar se os jogadores de jogos eletrônicos se atentam às marcas inseridas nos jogos eletrônicos. O mercado de games teve crescimento devido à pandemia do novo Corona vírus, uma vez que o consumidor passou a acessar por mais tempo os jogos eletrônicos, despertando o interesse das empresas em divulgar sua marca através das mais diversas plataformas nos jogos eletrônicos, sendo uma estratégia de visibilidade e de aproximação com os consumidores do setor. A fim de chegar no objetivo estipulado, foi aplicado um questionário on-line, em uma amostra por acesso, junto aos consumidores de jogos eletrônicos divulgado em mídias sociais, Discord Facebook e WhatsApp, e por razões operacionais o estudo foi limitado ao estado de São Paulo, por ser um estado que possui grande influência no mercado de jogos eletrônicos. O resultado da pesquisa identificou que os consumidores de jogos eletrônicos se atentam as publicidades inseridas no contexto de jogos e se sentem atraídos pelas marcas e produtos presentes nos jogos

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

Operator and replicability bias in comparative taphonomic studies

The operator effect is a well-known analytical bias already quantified in some taphonomic studies. However, the influence of operator bias in the replicability on taphonomic studies has still not been considered. Here, we quantified for the first time this bias using different multivariate statistical techniques, testing if the operator effect is related to the replicability. We analyzed the results reported by 15 operators working on the same dataset. Each operator analyzed 30 bioclasts (bivalve shells) by site, from a total of five sites, considering the following taphonomic attributes: shell fragmentation, edge rounding, corrasion, bioerosion, and color alteration. The operator effect followed the same pattern reported in previous studies, characterized by a worse correspondence for those attributes having more than two levels of damage categories. However, the effect did not appear to have relation to replicability, because nearly all operators found differences among sites. The binary attribute bioerosion exhibited 83% of correspondence among operators, but at the same time, it was the taphonomic attribute that showed the highest dispersion among operators (28%). Therefore, we concluded that binary attributes, despite indicating a reduction of the operator effect diminishes replicability, result in different interpretations of concordant data. We found that a variance value of nearly 8% among operators was enough to generate a different taphonomic interpretation, in a Q-mode cluster analysis. The results reported here showed that the statistical method employed influences the level of replicability and comparability of a study and that the availability of results may be a valid alternative to reduce bias

El sesgo del operador en la replicabilidad de los estudios tafonómicos comparativos

The operator effect is a well-known analytical bias already quantified in some taphonomic studies. However, the influence of operator bias in the replicability on taphonomic studies has still not been considered. Here, we quantified for the first time this bias using different multivariate statistical techniques, testing if the operator effect is related to the replicability. We analyzed the results reported by 15 operators working on the same dataset. Each operator analyzed 30 bioclasts (bivalve shells) by site, from a total of five sites, considering the following taphonomic attributes: shell fragmentation, edge rounding, corrasion, bioerosion, and color alteration. The operator effect followed the same pattern reported in previous studies, characterized by a worse correspondence for those attributes having more than two levels of damage categories. However, the effect did not appear to have relation to replicability, because nearly all operators found differences among sites. The binary attribute bioerosion exhibited 83% of correspondence among operators, but at the same time it was the taphonomic attribute that showed the highest dispersion among operators (28%). Therefore, we concluded that binary attributes, despite indicating a reduction of the operator effect diminishes replicability, resulting in different interpretations of concordant data. We found that a variance value of nearly 8% among operators was enough to generate a different taphonomic interpretation, in a Q-mode cluster analysis. The results reported here showed that the statistical method employed influences the level of replicability and comparability of a study and that the availability of results may be a valid alternative to reduce bias.Fil: Do Nascimento Ritter, Matias. Universidade Federal do Rio Grande do Sul; BrasilFil: Francischini, Heitor. Universidade Federal do Rio Grande do Sul; BrasilFil: Kuhn, Lidia Aumond. Universidade Federal do Rio Grande do Sul; BrasilFil: Da Luz, Nathália Carvalho. Universidade Federal do Rio Grande do Sul; BrasilFil: Michels, Fernando Heck. Universidade Federal do Rio Grande do Sul; BrasilFil: De Morais, Anderson Luiz Martins. Universidade Federal do Rio Grande do Sul; BrasilFil: Paim, Protásio Antônio Vervloet. Universidade Federal do Rio Grande do Sul; BrasilFil: Xavier, Pedro Luis Ammon. Universidade Federal do Rio Grande do Sul; BrasilFil: de Francesco, Claudio German. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentin

ABC-SPH risk score for in-hospital mortality in COVID-19 patients : development, external validation and comparison with other available scores

The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Median (25-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO/FiO ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19

O Museu do Estado da Bahia, entre ideais e realidades (1918 a 1959)

This article retraces the trajectory of the Museu do Estado da Bahia from 1918 to 1959. The aim is to identify its successive museological practices and the implementation of the so-called processes of musealization during its institutionalization and consolidation, which were also related to the personalities of the administrators and their expectations regarding political and social interactions, as well their expectations toward the State of Bahia itself. Having dealt with a wide range of conflicts, alterations have been observed in how the museum functions. Within the framework in question, three successive periods have been characterized: its establishment as a historic museum; its consolidation as an eclectic museum with a focus on history, and the path towards art under the direction of José Valladares

ABC₂-SPH risk score for in-hospital mortality in COVID-19 patients

Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Methods: Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. Results: Median (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.</p

Núcleos de Ensino da Unesp: artigos 2007

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq