Transferência de Aprendizado para Redes Bayesianas com Aplicação em Predição de Falha de Discos Rígidos

Predizer falhas em Discos Rígidos é muito importante para evitar perda de dados e custos adicionais. Logo, um esforço pode ser observado para encontrar métodos adequados de predição de falhas. Apesar dos resultados encorajantes alcançados por vários métodos, um aspecto notado é a falta de dados disponíveis para construir modelos confiáveis. Transferência de Aprendizado oferece uma alternativa válida, uma vez que pode ser usada para transferir conhecimento de modelos de Disco com muitos dados para Discos com menos dados. Neste trabalho, avaliamos estratégias de Transferência de Aprendizado para esta tarefa. Além disso propomos uma estratégia para construir fontes de informação baseadas no agrupamento de modelos de disco parecidos. Resultados mostraram que todos os cenários testados de transferência melhoram a performance dos métodos de predição, principalmente para Discos com muito poucos dados

Private Reverse Top-k Algorithms Applied on Public Data of COVID-19 in the State of Ceará

In this article we propose a differentially private reverse top-k query. Our strategy allows obtaining the less frequent data according to a search criteria, with a high guarantee of privacy of the individuals who contributed with personal data in the original database. We apply our strategy on public data for COVID-19 in the State of Ceará using two different queries. Our experimental results show that the result of the proposed top-k query returns a high degree of similarity to the result of a conventional top-k query, when the chosen budget is suitable, providing useful results for researchers, while ensuring a low probability of re-identification of individuals arising from the properties of differential privacy.</jats:p

Effect of oxygen on multidrug resistance in the first trimester human placenta

Introduction: The multidrug resistance proteins, P-glycoprotein (P-gp, encoded by the ABCB1 gene) and breast cancer resistance protein (BCRP, encoded by ABCG2) are highly expressed in the first trimester placenta. These transporters protect the fetus from exposure to maternally derived toxins and xenobiotics. Since oxygen is a regulator of multidrug resistance in various tissues, we hypothesized that changes in oxygen tension alter placental ABCB1/P-gp and ABCG2/BCRP expression in the first trimester. Methods: Placental specimens were collected from first (n=7), second (n=5) and term pregnancies (n=5). First trimester placental villous explants were incubated (24 or 48h) in different oxygen tension (3-20%). ABCB1, ABCG2 and VEGFA mRNA expression levels were assessed by RT-PCR and protein was localized by IHC. Results: ABCB1 is expressed most highly in the first trimester placenta (p<0.05), whereas ABCG2 expression does not change significantly over pregnancy. P-gp and BCRP staining is present in the syncytiotrophoblast and in cytotrophoblasts. ABCG2 mRNA is increased in hyperoxic (20%) conditions after 48h (p<0.05). In contrast, hypoxia (3%) did not change ABCB1 mRNA expression but significantly increased VEGFA mRNA (p<0.05). Hypoxia resulted in increased BCRP staining in cytotrophoblasts and in the microvillous membrane of the syncytium. Whereas, hypoxia resulted in increased P-gp staining in proliferating cytotrophoblasts. Conclusion: We conclude that placental multidrug resistance expression, specifically ABCG2, is regulated by oxygen tension in the first trimester. It is possible that changes in placental oxygen supply are capable of altering fetal drug exposure especially during early pregnancy.This study was funded by the Canadian Institutes for Health Research (grant: FRN-57746; to S.G.M. and W.G.)

MAPSkew: Metaheuristic Approaches for Partitioning Skew in MapReduce

MapReduce is a parallel computing model in which a large dataset is split into smaller parts and executed on multiple machines. Due to its simplicity, MapReduce has been widely used in various applications domains. MapReduce can significantly reduce the processing time of a large amount of data by dividing the dataset into smaller parts and processing them in parallel in multiple machines. However, when data are not uniformly distributed, we have the so called partitioning skew, where the allocation of tasks to machines becomes unbalanced, either by the distribution function splitting the dataset unevenly or because a part of the data is more complex and requires greater computational effort. To solve this problem, we propose an approach based on metaheuristics. For evaluating purposes, three metaheuristics were implemented: Simulated Annealing, Local Beam Search and Stochastic Beam Search. Our experimental evaluation, using a MapReduce implementation of the Bron-Kerbosch Clique Algorithm, shows that the proposed method can find good partitionings while better balancing data among machines