The quality of fertility data in the web-based Generations and Gender Survey

Background: The Generations and Gender Survey (GGS) enables investigating family-related events from a life course perspective. After its first round of face-to-face implementation, various factors resulted in the second round being implemented on the web. Despite its advantages, implementing a web-based GGS has its drawbacks ‒ for instance, possible misreporting, and especially underreporting, of life history variables due to the lack of on-site guidance. Objective: To assess the quality of GGS second-round data collected through the web by verifying the accuracy of fertility histories. Methods: We compare the GGS data with population-based estimates from open access sources, the Human Fertility Database (HFD) and the United Nations Population Division (UN), using three cohort indicators and one period fertility indicator that are frequently used as summary measures. We restrict the analysis to the female fertility history data of countries where the second round of the GGS was implemented via the web and the data processing has been completed: Estonia, Norway, Finland, Denmark, and Sweden. Results: For the four indicators, the GGS estimates are consistent with the population-based estimates. With a few exceptions, HFD and UN estimates fall within the GGS confidence intervals (CIs). Conclusions: Overall, we found similarities that demonstrate the high quality of the data. Our assessment finds no systematic deviation for the cohort indicators and small scale underreporting for the period indicator (nevertheless, also usually within the CIs). Contribution: The high level of similarity is encouraging for the use of GGS second-round data and the implementation of web-based methods of data collection

Explorando a forma fraca da (in)eficiência de mercado por meio de algoritmos de inteligência artificial

A pesquisa apresentada neste trabalho visou analisar o desempenho de diferentes algoritmos de inteligência artificial (IA) para previsão de movimentos dos principais índices das maiores bolsas de valores ao redor do mundo. Para tanto, foram coletados dados diários de 34 índices, entre os anos de 2010 e 2019, e estimados os movimentos desses índices com o uso de quatro dos principais algoritmos de IA: Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Naive Bayes (NB) e Random Forest (RF). Tais algoritmos foram treinados com base em nove indicadores técnicos amplamente empregados na análise de ativos financeiros. De forma geral, evidenciou-se a possibilidade de se obter retornos superiores à média de mercado a partir dos algoritmos selecionados e treinados com base em indicadores técnicos. Destaca-se, portanto, o potencial de exploração de ineficiências de diferentes mercados de capitais ao redor do mundo em sua forma fraca a partir de algoritmos de IA. De forma específica, constatou-se que o desempenho dos algoritmos variou de acordo com a medida de desempenho utilizada. Quando se considerou a acurácia como medida de desempenho, o algoritmo ANN obteve desempenhos superiores aos dos demais; ao passo que o algoritmo NB apresentou os piores desempenhos independentemente das medidas empregadas para mensurá-lo. O estudo desenvolvido traz uma série de contribuições à pesquisa sobre o emprego desses algoritmos para previsão do movimento de índices de ativos financeiros nos mercados de capitais ao redor do mundo: (i)  obtiveram-se evidências robustas da utilidade e relevância de algoritmos de IA para prever movimentos de preços nas principais bolsas de valores do mundo; (ii) verificou-se que a medida empregada para mensurar o desempenho dos algoritmos influencia de forma significativa sua avaliação; e (iii) constatou-se que os indicadores técnicos podem auxiliar em decisões que agregam valor ao serem conjugados com técnicas de IA

Randomized, phase 1/2, double-blind pioglitazone repositioning trial combined with antifungals for the treatment of cryptococcal meningitis – PIO study

Background: Cryptococcosis affects more than 220,000 patients/year, with high mortality even when the standard treatment [amphotericin B (AMB), 5-flucytosin (5-FC) and fluconazole] is used. AMB presents high toxicity and 5-FC is not currently available in Brazil. In a pre-clinical study, pioglitazone (PIO - an antidiabetic drug) decreased AMB toxicity and lead to an increased mice survival, reduced morbidity and fungal burden in brain and lungs. The aim of this trial is to evaluate the efficacy and safety of PIO combined with standard antifungal treatment for human cryptococcosis. Methods: A phase 1/2, randomized, double blind, placebo-controlled trial will be performed with patients from Belo Horizonte, Brazil. They will be divided into three groups (placebo, PIO 15 mg/day or PIO 45 mg/day) and will receive an additional pill during the induction phase of cryptococcosis’ treatment. Our hypothesis is that treated patients will have increased survival, so the primary outcome will be the mortality rate. Patients will be monitored for survival, side effects, fungal burden and inflammatory mediators in blood and cerebrospinal fluid. The follow up will occur for up 60 days. Conclusions: We expect that PIO will be an adequate adjuvant to the standard cryptococcosis’ treatment. Trial registration: ICTRP/WHO (and International Clinical Trial Registry Plataform (ICTRP/WHO) (http://apps.who.int/trialsearch/Trial2.aspx?TrialID=RBR-9fv3f4), RBR-9fv3f4 (http://www.ensaiosclinicos.gov.br/rg/RBR-9fv3f4). UTN Number: U1111-1226-1535. Ethical approvement number: CAAE 17377019.0.0000.5149