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Various types of magnetic nanoparticles have been developed for a range of medical applications and some have the potential to treat diseases. In this paper, a new method for controlling the position of these magnetic particles by changing the magnetic field surrounding them using three Halbach ringsis investigated with the aid of a simulation. The inner ring controls the direction, and the position of the two outer rings can keep the group of particles together. In the simulation, the magnetic field is calculated analytically for each time step and the movement of the particles is simulated by applying the corresponding forces. These result from the manipulable external magnetic field, the magnetic field between the particles, the Lennard-Jones potential to model attractive interactions between the particles and the drag. In the simulation, we show how the particles are manipulated using a Levenberg-Marquard algorithm and develop a closedloop control for the position of the grouped particles by manipulating the magnetic field inside the Halbach rings by changing the angular position of these rings.

This paper presents a simulation system (“patient model”) for intraoperative neuromonitoring (IONM) applied to mastoidectomy. IONM is an electrophysiological method for monitoring the integrity and localization of nerve tracts, which helps the surgeon to avoid injuries and damage to neural risk structures (e.g. facial nerve) during surgery. To use the IONM successfully, the surgeon needs appropriate training and experience. The presented simulation system provides training possibilities in a realistic, cost-efficient and reproducible way. In the simulation system, the position of the probe during training is determined by a magnetic tracking method. Depending on the distance to the virtual nerve, a synthetic electromyogram (EMG) signal is sent to a real neuromonitor. The trainee learns to interpret the output of the neuromonitor. The trainer can choose different training scenarios, such as localization of the nerve, milling or coagulating, using a web application.

Digitale Zwillinge werden in der Betriebsphase einer Maschine oder Anlage für verschiedene Fragestellungen angewendet, bspw. für die Zustandserkennung. Voraussetzung dafür ist, dass der Digitale Zwilling über Modelle des Anlagenverhaltens verfügt, die verschiedene Zustände, einschließlich von Fehlerzustände, adäquat beschreiben. Ein wesentlicher Aspekt dabei ist die Identifikation der Modellparameter. Dieser Beitrag dient der Analyse und Anpassung ausgewählter Optimierungsmethoden (gradientenbasierter Verfahren und ein Partikelschwarmoptimierer) am Beispiel von Modellen für Kompressoren und Turbinenanlagen. Dazu wird mit einem analytischen Ansatz ein Modell einer realen Kompressoranlage erstellt. Um die Qualität der Optimierungsergebnisse vergleichen zu können, wird eine Fehlermetrik eingeführt. Das Verhalten der Optimierer wird unter den folgenden realistischen Fehlerszenarien ermittelt: Fehler in den Modellparametern, Rauschen, initialer Abstand vom Optimum, fehlerhaft Messstellen (ohne und mit Detektion). Zur numerischen Bewertung der Identifizierbarkeit werden zwei Kriterien eingeführt.

Purpose: African-American men (AAM) have a greater risk of hypertension (HTN) than Caucasian men (CM). To reduce this risk, determining the differences in mechanisms involved in HTN and understanding the relationship between these mechanisms and factors affecting blood pressure (BP) in AAM and CM is necessary. One such mechanism is spontaneous baroreflex sensitivity (sBRS) and two factors are cardiorespiratory fitness (CRF) and arterial stiffness (AS). The aims of this study were to determine, firstly, whether there are differences in sBRS between young, normotensive AAM and CM, and secondly, to determine if CRF and AS are significant predictors of sBRS in young, normotensive AAM and CM. Methods: Twenty-three normotensive AAM and 36 CM were recruited from Southern Connecticut State University. Measures included anthropometric, sBRS (alpha-index), and CRF (maximal oxygen consumption [VO2max]), as well as AS (carotid-femoral pulse wave velocity [Cf-PWV]). Independent t-tests were used to determine differences between groups and multiple regression analysis was used to determine how much of the variation in sBRS was explained by CRF and AS. Results: The sBRS was significantly lower in AAM (10.3 ± 3.8 ms/mmHg) vs. CM (13.3 ± 5.7 ms/ mmHg), P = 0.03. CRF and AS were not significant predictors of sBRS in AAM (P = 0.25) and CM (P = 0.30). There was no relationship between, sBRS, CRF and AS; CRF was significantly reduced in AAM vs. CM (45.1 ± 6.3 vs. 52.1 ± 7.5 mL·kg−1·min−1, P ≤ 0.001). Conclusions: Young normotensive AAM demonstrated significantly lower sBRS vs. CM, irrespective of having fair CRF and normal BP. CRF and AS are not significant predictors of sBRS in young, normotensive AAM and CM. The attenuation in sBRS in AAM did not result in AAM having higher BP versus CM. This finding underscores the need for more detailed examination of the role of sBRS in the etiology of HTN in AAM.

Purpose: Aortic augmentation index (AIx) and cardiovagal tone (CVT) are indicators of cardiovascular health. Associations between these variables provide information about their roles in cardiovascular disease. However, evaluating these associations from a gender perspective and gaining an understanding of the relationship between cardiorespiratory fitness and AIx based on gender can provide additional information. Therefore, we examined the relationships between CVT, measured as the log transformed high-frequency power of R-R intervals from electrocardiogram measurements (lnHFR-R); cardiorespiratory fitness, measured as maximum oxygen consumption (VO2max); and AIx at a heart rate of 75 beats·min-1(AIx@75). We hypothesized that the relationships between CVT, cardiorespiratory fitness, and AIx@75 would differ based on gender. Methods: We examined the associations between lnHFR-R, VO2max, and AIx@75 in 41 women and 39 men. Power spectral density analysis of heart rate variability determined lnHFR-R, radial tonometry determined AIx@75 and the Bruce Treadmill Protocol determined VO2max. Results: In men, lnHFR-R positively correlated with and significantly predicted AIx@75 (P = 0.005) but not in women (P = 0.49). For every unit increase in lnHFR-R, there was a 4.6 unit increase in AIx@75 in men versus a 1.0 unit increase in women. There was a significant inverse relationship between VO2max and AIx@75, with VO2max being a significant predictor of AIx@75 in men (P = 0.01). For every unit increase in VO2max, there was a 0.60 unit decrease in AIx@75 in men versus a 0.2 unit decrease in women. Conclusions: The associations between CVT and AIx@75 and between cardiorespiratory fitness and AIx@75 differ by gender. CVT and cardiorespiratory fitness are significant predictors of AIx@75 in men but not in women.

The following article summarizes United States Patent Application No. US20180052155A1, titled ‘Assay Devices and Methods’ (filed 16 August 2016, published 22 February 2018). While lateral flow assays (LFAs) have revolutionized point-of-care diagnostics by enabling accurate, inexpensive, and rapid detection of biomarkers, they typically do not provide quantitative results. Hence, there is a significant need for quantitative assays at the point of care. This patent summary describes a novel method of chronometric biomarker quantitation via enzymatic degradation of a metastable gelatin-based biomatrix, principally suited for use in paper-based microfluidic devices (microPADs). This new quantitation mechanism was designed to meet the ASSURED criteria for point-of-care diagnostic devices laid forth by the World Health Organization and may ultimately provide increased access to healthcare, at a significantly reduced cost, around the world.

We introduce a comprehensive public dataset, NightOwls, for pedestrian detection at night. In comparison to daytime conditions, pedestrian detection at night is more challenging due to variable and low illumination, reflections, blur, and changing contrast. NightOwls consists of 279k frames in 40 sequences recorded at night across 3 countries by an industry-standard camera, including different seasons and weather conditions. All the frames are fully annotated and contain additional object attributes such as occlusion, pose and difficulty, as well as tracking information to identify the same object across multiple frames. A large number of background frames for evaluating the robustness of detectors is included, a validation set for local hyper-parameter tuning, as well as a testing set for central evaluation on a submission server is provided. As a baseline for pedestrian detection at night time, we compare the performance of ACF, Checkerboards, Faster R-CNN, RPN+BF, and SDS-RCNN. In particular, we demonstrate that state-of-the-art pedestrian detectors do not perform well at night, even when specifically trained on night data, and we show there is a clear gap in accuracy between day and night detections. We believe that the availability of a comprehensive night dataset may further advance the research of pedestrian detection, as well as object detection and tracking at night in general.

Background: Cardiovascular disease is a primary cause of death worldwide. Low parasympathetic nervous activity (PNA) is associated with negative cardiovascular health (CVH) [1]. Central augmentation pressure (CAP), aortic pulse pressure (APP) and augmentation index (Alx) are all strong indicators of CVH. Heart rate variability (HRV) and recovery heart rate (RHR) are both indices of PNA but they do not correlate [2]. Since these indices do not correlation we hypothesized that there would be a difference in their relationship with CVH as determined by CAP, APP and Alx75. Aim: To determine if there is a difference in the relationships between HRV, RHR and CAP, APP and Alx inmen. Methods: Seventeen healthy college aged men were examined for HRV via power spectral density analysis. Recovery heart rate was examined at minute one (RHR1) and two (RHR 2)) following a VO2max test. Pulse wave analysis via applanation tonometry and a generalized transfer function was used to determine CAP, APP and Alx. Results: There was a significant correlation between HRV and CAP (r = 0.61, P = 0.01) and Alx (r = 0.51, P = 0.05). No correlation was observed between RHR 1, RHR 2 and CAP, Alx and APP. Central augmentation pressure was significantly correlated with APP (r = 0.67, P= 0.01) and Alx r = 0.64, P= 0.01). Conclusions: Findings suggest a difference in the relationship between HRV, RHR and CVH. Here HRV was positively correlated with indices of CVH, while RHR did not correlate with these indices. Indicating that HRV seem to be more indicative of CVH versus RHR.