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Metrics are key elements that can give us valuable information about the effectiveness of agile software development processes, particularly considering the Scrum environment. This study aims to learn about the metrics adopted to assess agile development processes and explore the impact of how the role performed by each member in Scrum contributed to increasing/reducing the perception of the importance of these metrics. The impact of years of experience in Scrum on this perception was also explored. To this end, a quantitative study was conducted with 191 Scrum professionals in companies based in Portugal. The results show that the Scrum role is not a determining factor, while individuals with more years of experience have a higher perception of the importance of metrics related to team performance. The same conclusion is observed for the business value metric of the product backlog and the percentage of test automation in the testing phase. The findings allow for extending the knowledge about Scrum project management processes and their teams, in addition to offering important insights into the implementation of metrics for software engineering companies that adopt Scrum. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an incurable and highly debilitating condition characterized by the progressive degeneration and/or death of nerve cells, which leads to manifestation of disabilities in cognitive functioning. In recent years, the development of biosensors for determination of AD's main biomarkers has made remarkable progress, particularly based on the tremendous advances in nanoscience and nanotechnology. The unique and outstanding properties of nanomaterials (such as graphene, carbon nanotubes, gold, silver and magnetic nanoparticles, polymers and quantum dots) have been contributing to enhance the electrochemical and optical behavior of transducers while offering a suitable matrix for the immobilization of biological recognition elements. Therefore, optical and electrochemical immuno- and DNA-biosensors with higher sensitivity, selectivity and longer stability have been reported. Nevertheless, strategies based on the detection of multiple analytes still need to be improved, as they will play a crucial role in minimizing misdiagnosis. This review aims to provide insights into the conjugation of nanomaterials with different transducers highlighting their crucial role in the construction of biosensors for detection of AD main biomarkers. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) aims to incorporate molecular information directly into the prediction of individual genetic merit. Regularized quantile regression (RQR) can be used to fit models for all portions of a probability distribution of the trait, enabling the conditional quantile that "best" represents the functional relationship between dependent and independent variables to be chosen. The objective of this study was to predict the individual genetic merits of the traits associated with flowering time (DFF—days to first flower; DTF—days to flower) in the common bean using RQR and to compare the predictive abilities obtained from Random Regression Best Linear Unbiased Predictor (RR-BLUP), Bayesian LASSO (BLASSO), BayesB, and RQR for predicting the genetic merit. GS was performed using 80 genotypes of common beans genotyped for 380 single nucleotide polymorphism (SNP) markers. Considering the "best" RQR fit models (RQR0.3 for DFF, and RQR0.2 for DTF), the gains in predictive ability in relation to BLASSO, BayesB, and RR-BLUP were 18.75%, 22.58%, and 15.15% for DFF, respectively, and 15.20%, 24.65%, and 12.55% for DTF, respectively. The potential cultivars selected, considering the RQR "best" models, were among the 5% of cultivars with the lowest genomic estimated breeding value (GEBV) for the DFF and DTF traits—the IAC Imperador, IPR Colibri, Capixaba Precoce, and IPR Andorinha were included in the list of early cycle cultivars. [ABSTRACT FROM AUTHOR]