Considering the imminence of new SARS-CoV-2 variants and COVID-19 vaccine availability, it is essential to understand the impact of the disease on the most vulnerable groups and those at risk of death from the disease. To this end, the odds ratio (OR) for mortality and hospitalization was calculated for different groups of patients by applying an adjusted logistic regression model based on the following variables of interest: gender, booster vaccination, age group, and comorbidity occurrence. A massive number of data were extracted and compiled from official Brazilian government resources, which include all reported cases of hospitalizations and deaths associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Brazil during the “wave” of the Omicron variant (BA.1 substrain). Males (1.242; 95% CI 1.196–1.290) aged 60–79 (3.348; 95% CI 3.050–3.674) and 80 years or older (5.453; 95% CI 4.966–5.989), and hospitalized patients with comorbidities (1.418; 95% CI 1.355–1.483), were more likely to die. There was a reduction in the risk of death (0.907; 95% CI 0.866–0.951) among patients who had received the third dose of the anti-SARS-CoV-2 vaccine (booster). Additionally, this big data investigation has found statistical evidence that vaccination can support mitigation plans concerning the current scenario of COVID-19 in Brazil since the Omicron variant and its substrains are now prevalent across the entire country.

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In ophthalmology, the registration problem consists of finding a geometric transformation that aligns a pair of images, supporting eye-care specialists who need to record and compare images of the same patient. Considering the registration methods for handling eye fundus images, the literature offers only a limited number of proposals based on deep learning (DL), whose implementations use the supervised learning paradigm to train a model. Additionally, ensuring high-quality registrations while still being flexible enough to tackle a broad range of fundus images is another drawback faced by most existing methods in the literature. Therefore, in this paper, we address the above-mentioned issues by introducing a new DL-based framework for eye fundus registration. Our methodology combines a U-shaped fully convolutional neural network with a spatial transformation learning scheme, where a reference-free similarity metric allows the registration without assuming any pre-annotated or artificially created data. Once trained, the model is able to accurately align pairs of images captured under several conditions, which include the presence of anatomical differences and low-quality photographs. Compared to other registration methods, our approach achieves better registration outcomes by just passing as input the desired pair of fundus images.

Resumo. Por muito tempo as mulheres sofreram uma grande exclusão em diversas áreas, tanto no que diz respeito ao acesso à educação quanto em relação a inserção no mercado de trabalho, visto que estudar e "trabalhar fora" eram atividades restritas aos homens, por isso pouquíssimas chegavam ao ensino superior. Pesquisas educacionais e científicas relacionando gênero e educação começaram a ganhar espaço somente em meados dos anos de 1990, com o crescimento de reivindicações de políticas públicas visando a criação de medidas contra a discriminação da mulher. Nesta linha, o presente trabalho tem como objetivo investigar como políticas públicas no âmbito do ensino superior impactam no acesso à universidade, no que diz respeito a composição de gênero em paralelo às áreas do conhecimento tidas como masculinas, como é o caso das STEM (sigla em inglês para Ciências, Tecnologias, Engenharias e Matemática). Dentro deste recorte, foram analisados os bancos de dados do Programa Universidade para Todos (Prouni) do Governo Federal, que visa oferecer bolsas de estudos parciais e integrais em instituições superior, entre os anos de 2011 e 2020. O estudo mostrou que a ocupação de bolsas femininas em STEM foram menores em todos os anos, mesmo as mulheres sendo maioria na ocupação geral das bolsas.