Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping

The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-standard". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of what we refer to as silver standard masks for data augmentation in deep-learning-based skull-stripping also known as brain extraction. We generated the silver standard masks using the consensus algorithm Simultaneous Truth and Performance Level Estimation (STAPLE). We evaluated CNN models generated by the silver and gold standard masks. Then, we validated the silver standard masks for CNNs training in one dataset, and showed its generalization to two other datasets. Our results indicated that models generated with silver standard masks are comparable to models generated with gold standard masks and have better generalizability. Moreover, our results also indicate that silver standard masks could be used to augment the input dataset at training stage, reducing the need for manual segmentation at this step

Genomic selection in rubber tree breeding: A comparison of models and methods for managing G×E interactions

Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (H2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs

An Empirical Evaluation of an Evolutionary Game Theory Model of the Labor Market

In this paper we intend to perform an empirical evaluation of the evolutionary game theory model of the labor market developed by Araujo and Souza (2010). In order to accomplish this task we focus on the Brazilian labor market by using data from the National Household Sampling Survey – PNAD/IBGE, from 1995 to 2008. We used four different methodologies: the OLS, Pseudo-panel with fixed effects, Instrumental Variables and the Heckman Selection Model. Results indicate that the main difference between the 1995-2002 and 2003-2008 period is the impact of education over wages. According to these findings, investments in education were more profitable for the 2003-2008 period. However, all wage gaps between formal and informal markets reduced considerably.formal and informal and labor market, evolutionary game theory.