Passenger Exposure to Magnetic Fields in Electric Vehicles

In electric vehicles, passengers sit very close to an electric system of significant power, usually for a considerable amount of time. The relatively high currents achieved in these systems and the short distances between the power devices and the passengers mean that the latter could be exposed to relevant magnetic fields. This implies that it becomes necessary to evaluate the electromagnetic environment in the interior of these vehicles before releasing them in the market. Moreover, the hazards of magnetic field exposure must be taken into account when designing electric vehicles and their components. For this purpose, estimation tools based on finite element simulations can prove to be very useful. With appropriate design guidelines, it might be possible to make electric vehicles safe from the electromagnetic radiation point of view

Congenital Chagas’ disease transmission in the United States: Diagnosis in adulthood

Two brothers with congenitally-acquired Chagas’ disease (CD) diagnosed during adulthood are reported. The patients were born in the USA to a mother from Bolivia who on subsequent assessment was found to be serologically positive for Trypanosoma cruzi. Serologic screening of all pregnant women who migrated from countries with endemic CD is strongly recommended

SDN-DMM for intelligent mobility management in heterogeneous mobile IP networks

[EN] Mobility management applied to the traditional architecture of the Internet has become a great challenge because of the exponential growth in the number of devices that can connect to the network. This article proposes a Software-Defined Networking (SDN)-based architecture, called SDN-DMM (SDN-Distributed Mobility Management), that deals with the distributed mode of mobility management in heterogeneous access networks in a simplified and efficient way, ensuring mainly the continuity of IP sessions. Intent-based mobility management with an IP mapping schema for mobile node identification offers optimized routing without tunneling techniques, hence, an efficient use of the network infrastructure. The simplified mobility control API reduces both signaling and handover latency costs and provides a better scalability and performance in comparison with traditional and SDN-based DMM approaches. An analytical evaluation of such costs demonstrated the better performance of SDN-DMM, and a proof of concept of the proposal was implemented in a real environment.CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior) - Brasil; Secretaria de Estado de Investigacion, Desarrollo e Innovacion, Grant/Award Number: TIN2017-84802-C2-1-P; "Convocatoria 2017 - Proyectos I+D+I Programa Estatal de Investigacion, Desarrollo e Innovacion, convocatoria excelencia", Grant/Award Number: TIN2017-84802-C2-1-P; FAP-DF ("Fundacao de Apoio a Pesquisa do Distrito Federal")-BrazilTorres Cordova, R.; Gondim, PRL.; Llerena, YP.; Lloret, J. (2019). SDN-DMM for intelligent mobility management in heterogeneous mobile IP networks. International Journal of Communication Systems. 32(17):1-31. https://doi.org/10.1002/dac.4140131321

Augmented synthetic dataset with structured light to develop Ai-based methods for breast depth estimation

Breast interventions are common healthcare procedures that nor mally require experienced professionals, expensive setups, and high execution times. With the evolution of robot-assisted technologies and image analysis algorithms, new methodologies can be imple mented to facilitate the interventions in this area. To enable the introduction of robot-assisted approaches for breast procedures, strategies with real-time capacity and high precision for 3D breast shape estimation are required. In this paper, it is proposed to fuse the structured light (SL) and deep learning (DL) techniques to perform the depth estimation of the breast shape with high precision. First, multiple synthetic datasets of breasts with different printed patterns, resembling the SL technique, are created. Thus, it is possi ble to take advantage of the pattern’s deformation induced by the breast surface in order to improve the quality of the depth infor mation and to study the most suitable design. Then, distinct DL architectures, taken from the literature, were implemented to esti mate the breast shape from the created datasets and study the DL architectures’ influence on depth estimation. The results obtained with the introduction of a yellow grid pattern, composed of thin stripes, fused with the DenseNet-161 architecture achieved the best results. Overall, the current study demonstrated the potential of the proposed practice for breast depth estimation or other human body parts in the future when we rely exclusively on 2D images.The authors acknowledge the funding of the projects "NORTE01-0145-FEDER000045” and "NORTE-01-0145-FEDER-000059", supported by Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). It was also funded by national funds, through the Fundação para a Ciência e a Tecnologia (FCT) and FCT/MCTES in the scope of the projects UIDB/05549/2020,UIDP/05549/2020 and LASILA/P/0104/2020. The authors also acknowledge FCT, Portugal and the European Social Found, European Union, for funding support through the “Programa Operacional Capital Humano” (POCH) in the scope of the PhD grants SFRH/BD/136721/2018 (B. Oliveira) and SFRH/BD/136670/2018 (H. Torres)

Dual consistency loss for contour-aware segmentation in medical images

Medical image segmentation is a paramount task for several clinical applications, namely for the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries. With the development of deep learning, Convolutional Neural Networks (CNN) have become the state-of-the-art for medical image segmentation. However, issues are still raised concerning the precise object boundary delineation, since traditional CNNs can produce non-smooth segmentations with boundary discontinuities. In this work, a U-shaped CNN architecture is proposed to generate both pixel-wise segmentation and probabilistic contour maps of the object to segment, in order to generate reliable segmentations at the object's boundaries. Moreover, since the segmentation and contour maps must be inherently related to each other, a dual consistency loss that relates the two outputs of the network is proposed. Thus, the network is enforced to consistently learn the segmentation and contour delineation tasks during the training. The proposed method was applied and validated on a public dataset of cardiac 3D ultrasound images of the left ventricle. The results obtained showed the good performance of the method and its applicability for the cardiac dataset, showing its potential to be used in clinical practice for medical image segmentation.Clinical Relevance-The proposed network with dual consistency loss scheme can improve the performance of state-of-the-art CNNs for medical image segmentation, proving its value to be applied for computer-aided diagnosis.- (undefined

Tratamiento del Síndrome de Larva Cutanea Migratoria con Albendazol: reporte Preliminar

Twenty three patients with Cutaneous Larva Migrans syndrome were prospectively treated with 400 mg/day of Albendazole for 3 consecutive days. Clinical response, compliance and tolerance was excellent. Patients were asymptomatic within the first 72 hours of treatment and recurrences did not occurred. Preliminary results with three additional patients suggest that a single oral 400 mg dose may be effective as well.Veintitrés pacientes con el sindrome de Larva Cutanea Migratoria, fueron tratados prospectivamente con 400 mg/dia VO de albendazol por tres dias consecutivos. La respuesta clínica, aceptación y tolerancia del tratamiento fué excelente. Todos los pacientes se volvieron asintomáticos durante las primeras 72 horas de tratamiento y no se observaron recurrencias de lesiones. Los resultados preliminares obtenidos entres pacientes adicionales, sugieren que la dosis única de 400 mg puede ser igualmente efectiva

Time to desaturation in the 6-min walking distance test predicts 24-hour oximetry in COPD patients with a PO2 between 60 and 70mmHg

SummaryBackgroundThe 6-min walking distance (6MWD) test is a useful tool for assessing patients with chronic obstructive pulmonary disease (COPD), but little is known about the changes in oxygen saturation that occur during the test.ObjectiveTo predict the oximetry profile during daily living activities by the time to desaturation in the 6MWD test in COPD-affected patients.Patients and methodsWe studied 67 COPD patients with moderate hypoxemia performing a 6MWD test and a 24-hour ambulatory pulse oximetry (24-hr PO). We determined the time to desaturation (SatO2⩽90%) in the 6MWD test, in the daytime, nighttime and 24-hr PO. We then estimated the time to desaturation that better predicts desaturation in diurnal, nocturnal and 24-hour oximetries using the ROC type II analysis.ResultsThe patients who desaturated after 3′30min have a 100% probability not to desaturate during diurnal, nocturnal and 24-hr PO. Those patients who desaturated during the first minute of the 6MWD test have a 74% probability to desaturate in these oximetries.ConclusionsThe time to desaturation in the 6MWD test can discriminate early desaturators who desaturate during their daily living activities and late desaturators who do not desaturate. Ambulatory oximetry would thus only be necessary in patients with a time to desaturation that ranges between 1 and 3′30″

Kidney segmentation in 3D CT images using B-Spline Explicit Active Surfaces

In this manuscript, we propose to adapt the B-Spline Explicit Active Surfaces (BEAS) framework for semi-automatic kidney segmentation in computed tomography (CT) images. To study the best energy functional for kidney CT extraction, three different localized region-based energies were implemented within the BEAS framework, namely localized Chan-Vese, localized Yezzi, and signed localized Yezzi energies. Moreover, a novel gradient-based regularization term is proposed. The method was applied on 18 kidneys from 9 CT datasets, with different image properties. Several energy combinations were contrasted using surface-based comparison against ground truth meshes, assessing their accuracy and robustness against surface initialization. Overall, the hybrid energy functional combining the localized signed Yezzi energy with gradient-based regularization simultaneously showed the highest accuracy and the lowest sensitivity to the initialization. Volumetric analysis demonstrated the feasibility of the method from a clinical point of view, with similar reproducibility to manual observers.The authors acknowledge FCT - Fundação para a Ciência e a Tecnologia, Portugal, and the European Social Found, European Union, for funding support through the Programa Operacional Capital Humano (POCH) in the scope of the PhD grants SFRH/BD/93443/2013 (S. Queirós) and SFRH/BD/95438/2013 (P. Morais).info:eu-repo/semantics/publishedVersio

A review of image processing methods for fetal head and brain analysis in ultrasound images

Background and objective: Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods. Methods: In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement. Results: For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications. Conclusions: A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection. (c) 2022 Elsevier B.V. All rights reserved.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020)This work was funded by projects “NORTE-01–0145-FEDER- 0 0 0 059 , NORTE-01-0145-FEDER-024300 and “NORTE-01–0145- FEDER-0 0 0 045 , supported by Northern Portugal Regional Opera- tional Programme (Norte2020), under the Portugal 2020 Partner- ship Agreement, through the European Regional Development Fund (FEDER). It was also funded by national funds, through the FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and by FCT and FCT/MCTES in the scope of the projects UIDB/05549/2020 and UIDP/05549/2020 . The authors also acknowledge support from FCT and the Euro- pean Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136670/2018 and SFRH/BD/136721/2018