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-Background -Several studies have shown that celiac disease, an autoimmune disorder that occurs in genetically susceptible individuals, is highly prevalent among relatives of celiac patients. Aim -To determine the prevalence of celiac disease in a group of first degree relatives of Brazilian celiac patients. Methods -First degree relatives of celiac patients attending the

Semantic segmentation of images is an important problem for mobile robotics and autonomous driving because it offers basic information which can be used for complex reasoning and safe navigation. Different solutions have been proposed for this problem along the last two decades, and a relevant increment on accuracy has been achieved recently with the application of deep neural networks for image segmentation. One of the main issues when comparing different neural networks architectures is how to select an appropriate metric to evaluate their accuracy. Furthermore, commonly employed evaluation metrics can display divergent outcomes, and thus it is not clear how to rank different image segmentation solutions. This paper proposes a new metric which accounts for both global and contour accuracy in a simple formulation to overcome the weaknesses of previous metrics. We show with several examples the suitability of our approach and present a comparative analysis of several commonly used metrics for semantic segmentation together with a statistical analysis of their correlation. Several network segmentation models are used for validation with virtual and real benchmark image sequences, showing that our metric captures information of the most commonly used metrics in a single scalar value.

Objective: Ictal (ICA) and postconvulsive central apnea (PCCA) have been implicated in sudden unexpected death in epilepsy (SUDEP) pathomechanisms. Previous studies suggest that serotonin reuptake inhibitors (SRIs) and benzodiazepines (BZDs) may influence breathing. The aim of this study was to investigate if chronic use of these drugs alters central apnea occurrence in patients with epilepsy. Methods: Patients with epilepsy admitted to epilepsy monitoring units (EMUs) in nine centers participating in a SUDEP study were consented. Polygraphic physiological parameters were analyzed, including video-electroencephalography (VEEG), thoracoabdominal excursions, and pulse oximetry. Outpatient medication details were collected. Patients and seizures were divided into SRI, BZD, and control (no SRI or BZD) groups. Ictal central apnea and PCCA, hypoxemia, and electroclinical features were assessed for each group. Results: Four hundred and seventy-six seizures were analyzed (204 patients). The relative risk (RR) for ICA in the SRI group was half that of the control group (p = 0.02). In the BZD group, ICA duration was significantly shorter than in the control group (p = 0.02), as was postictal generalized EEG suppression (PGES) duration (p = 0.021). Both SRI and BZD groups were associated with smaller seizure-associated oxygen desaturation (p = 0.009; p ≪ 0.001). Neither presence nor duration of PCCA was significantly associated with SRI or BZD (p ≫ 0.05). Conclusions: Seizures in patients taking SRIs have lower occurrence of ICA, and patients on chronic treatment with BZDs have shorter ICA and PGES durations. Preventing or shortening ICA duration by using SRIs and/or BZD in patients with epilepsy may play a possible role in SUDEP risk reduction.

Deploying advanced imaging solutions to robotic and autonomous systems by mimicking human vision requires simultaneous acquisition of multiple fields of views, named the peripheral and fovea regions. Among 3D computer vision techniques, LiDAR is currently considered at the industrial level for robotic vision. Notwithstanding the efforts on LiDAR integration and optimization, commercially available devices have slow frame rate and low resolution, notably limited by the performance of mechanical or solid-state deflection systems. Metasurfaces are versatile optical components that can distribute the optical power in desired regions of space. Here, we report on an advanced LiDAR technology that leverages from ultrafast low FoV deflectors cascaded with large area metasurfaces to achieve large FoV (150°) and high framerate (kHz) which can provide simultaneous peripheral and central imaging zones. The use of our disruptive LiDAR technology with advanced learning algorithms offers perspectives to improve perception and decision-making process of ADAS and robotic systems.

Hot electrons generated within plasmonic structures possess a high kinetic energy that can be employed to drive and catalyze a huge range of physicochemical processes at the metallic interface. Up to now, these photogenerated hot carriers were mainly generated within simple plasmonic nanoparticles where hot carrier localization coincides spatially with the position optical excitation. A current challenge for the development of future plasmonic-based hot electron devices requires the ability for a delocalized hot carrier production to control on a large-distance their spatial distribution. Here, we demonstrate the remote generation of hot electrons by launching a propagative surface plasmon on a gold waveguide. Such hot carriers can be produced at distances of several microns from the excitation. Moreover, using far- and near-field hyperspectral microscopy, we show that hot carriers present spatial and energy distributions driven by the propagating plasmon field distribution itself. This opens the door to the engineering of complex hot carrier devices through the management of the plasmon propagation for next level hot-electron-based applications.

Synthesizing human motion through learning techniques is becoming an increasingly popular approach to alleviating the requirement of new data capture to produce animations. Learning to move naturally from music, i.e., to dance, is one of the more complex motions humans often perform effortlessly. Each dance movement is unique, yet such movements maintain the core characteristics of the dance style. Most approaches addressing this problem with classical convolutional and recursive neural models undergo training and variability issues due to the non-Euclidean geometry of the motion manifold structure. In this paper, we design a novel method based on graph convolutional networks to tackle the problem of automatic dance generation from audio information. Our method uses an adversarial learning scheme conditioned on the input music audios to create natural motions preserving the key movements of different music styles. We evaluate our method with three quantitative metrics of generative methods and a user study. The results suggest that the proposed GCN model outperforms the state-of-the-art dance generation method conditioned on music in different experiments. Moreover, our graph-convolutional approach is simpler, easier to be trained, and capable of generating more realistic motion styles regarding qualitative and different quantitative metrics. It also presented a visual movement perceptual quality comparable to real motion data. The dataset and project are publicly available at: https://www.verlab.dcc. ufmg.br/motion-analysis/cag2020.

Hybrid components combining the optical power of a refractive and a diffractive optical system can form compact doublet lenses that correct various aberrations. Unfortunately, the diffraction efficiency of these devices decreases as a function of the deflection angle over the element aperture. Here, we address this issue, compensating for chromatic dispersion and correcting for monochromatic aberrations with centimeter-scale hybrid-metalenses. We demonstrate a correction of at least 80% for chromatic aberration and 70% for spherical aberration. We finally present monochromatic and achromatic images that clearly show how these hybrid systems outperform standard refractive lenses. The possibilities to adjust arbitrary spatial amplitude, phase, polarization, and dispersion profiles with hybrid metasurfaces offer unprecedented optical design opportunities for compact and broadband imaging, augmented reality/virtual reality, and holographic projection.