The iris can be considered as one of the most important biometric traits due to its high degree of uniqueness. Iris-based biometrics applications depend mainly on the iris segmentation whose suitability is not robust for different environments such as near-infrared (NIR) and visible (VIS) ones. In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described. Similar to a common convolutional network, but without the fully connected layers (i.e., the classification layers), an FCN employs at its end combination of pooling layers from different convolutional layers. Based on the game theory, a GAN is designed as two networks competing with each other to generate the best segmentation. The proposed segmentation networks achieved promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4, IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in both non-cooperative and cooperative domains, outperforming the baselines techniques which are the best ones found so far in the literature, i.e., a new state of the art for these datasets. Furthermore, we manually labeled 2,431 images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks available for research purposes.

The draft sequence of the genome of Bradyrhizobium elkanii 587 is presented. This was obtained using Illumina Next-Gen DNA sequencing combined with Sanger sequencing. Genes for the pathways involved in biological nitrogen fixation (the nif gene cluster), nod genes including nodABC , and genes for the type III protein secretion system (T3SS) are present.

Cancer is one of the leading causes of mortality worldwide, specifically lung cancer. Computer-Aided Detection (CADe) systems are being proposed to assist radiologists in the task of pulmonary nodule detection. In this paper, we propose a CADe system that uses Deep Convolutional Neural Network (DCNN). In the Nodule Candidate Detection (NCD) step, we used Mask Region-Convolutional Neural Network (Mask R-CNN) to detect bounding boxes in 2D slices of low-dose Computed Tomography (CT) scans. In the False Positive Reduction (FPR) step, we used a classifier ensemble based on CT attenuation patterns to boost 3D pulmonary nodule classification performance. The final confidence index generated by the CADe system to the pulmonary nodule candidates is the average of the prediction obtained with the NCD and FPR steps. The CADe system was validated on the publicly available LUng Nodule Analysis 2016 (LUNA16) challenge and obtained a sensitivity of 94.90% and an average of 1.0 False Positives per scan (FP/Scan), against 96.90% of the proposal that combines different existing CADe systems. To the best of our knowledge, our proposal has one of the best results of CADe systems, outperforming other state-of-the-art individual methods.