RESUMO

Aneurysmal coronary artery disease (ACAD) has been reported rarely in patients with neurofibromatosis type 1 (NF1), mostly in adults. We report on a female newborn affected by NF1 with ACAD disclosed during investigation for an abnormal prenatal ultrasound along with a review of the previously reported cases. The proposita had multiple café-au-lait spots and had no cardiac symptoms. Echocardiography, and cardiac computed tomography angiography confirmed aneurysms on the left coronary artery, left anterior descending coronary artery, and of the sinus of Valsalva. Molecular analysis detected the pathogenic variant NM_001042492.3(NF1):c.3943C>T (p.Gln1315*). Literature findings on ACAD in NF1 indicated that this mostly occurs in males, showing predilection for the development of aneurysms at the left anterior descending coronary artery, and manifesting predominantly as acute myocardial infarction, inclusively in teenagers, though it may be also asymptomatic as in our case. This report documents the first case of ACAD in a patient with NF1 diagnosed at birth, emphasizing that its early diagnosis is essential to prevent potential life-threatening events attributable directly to coronary lesions.

Assuntos

Aneurisma , Neurofibromatose 1 , Masculino , Adulto , Recém-Nascido , Adolescente , Humanos , Feminino , Neurofibromatose 1/complicações , Neurofibromatose 1/diagnóstico , Neurofibromatose 1/genética , Vasos Coronários/diagnóstico por imagem , Vasos Coronários/patologia , Manchas Café com Leite/patologia , Angiografia por Tomografia Computadorizada

RESUMO

We report a novel proposal for reducing the digital divide in rural multigrade schools, incorporating knowledge of robotics with a STEM approach to simultaneously promote curricular learning in mathematics and science in several school grades. We used an exploratory qualitative methodology to implement the proposal with 12 multigrade rural students. We explored the contribution of the approaches to the promotion of curricular learning in mathematics and science and the perceptions of using robotics to learn mathematics and science. As data collection techniques, we conducted focus groups and semi-structured interviews with the participants and analyzed their responses thematically. We concluded that the proposal could contribute to meeting the challenges of multigrade teaching. Our findings suggest that the proposal would simultaneously promote the development of curricular learning in mathematics and science in several school grades, offering an alternative for addressing various topics with different degrees of depth.

Assuntos

Instituições Acadêmicas , Estudantes , Humanos , Aprendizagem , Grupos Focais , Coleta de Dados

RESUMO

Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5-32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices.

RESUMO

In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.

RESUMO

This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR).