bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of ventricular flow dynamics by 4D-flow MRI in patients following surgical repair of d-transposition of the great arteries
Authors: Callaghan Fraser M. , Burkhardt Barbara , Valsangiacomo Buechel Emanuela R. , Kellenberger Christian J. , Geiger Julia 
Published: March 2021

Abstract
Objectives
To use 4D-flow MRI to describe systemic and non-systemic ventricular flow organisation and energy loss in patients with repaired d-transposition of the great arteries (d-TGA) and normal subjects.

Methods
Pathline tracking of ventricular volumes was performed using 4D-flow MRI data from a 1.5-T GE Discovery MR450 scanner. D-TGA patients following arterial switch (n = 17, mean age 14 ± 5 years) and atrial switch (n = 15, 35 ± 6 years) procedures were examined and compared with subjects with normal cardiac anatomy and ventricular function (n = 12, 12 ± 3 years). Pathlines were classified by their passage through the ventricles as direct flow, retained inflow, delayed ejection flow, and residual volume and visually and quantitatively assessed. Additionally, viscous energy losses (ELv) were calculated.

Results
In normal subjects, the ventricular flow paths were well ordered following similar trajectories through the ventricles with very little mixing of flow components. The flow paths in all atrial and some arterial switch patients were more irregular with high mixing. Direct flow and delayed ejection flow were decreased in atrial switch patients’ systemic ventricles with a corresponding increase in residual volume compared with normal subjects (p = 0.003 and p < 0.001 respectively) and arterial switch patients (p < 0.0001 and p < 0.001 respectively). In non-systemic ventricles, arterial switch patients had increased direct flow and decreased delayed ejection fractions compared to normal (p = 0.007 and p < 0.001 respectively) and atrial switch patients (p = 0.01 and p < 0.001 respectively). Regions of high levels of mixing of ventricular flow components showed elevated ELv.

Conclusions
4D-flow MRI pathline tracking reveals disordered ventricular flow patterns and associated ELv in d-TGA patients.

Key Points
• 4D-flow MRI can be used to assess intraventricular flow dynamics in d-TGA patients.
• d-TGA arterial switch patients mostly show intraventricular flow dynamics representative of normal subjects, while atrial switch patients show increased flow disorder and different proportions of intraventricular flow volumes.
• Flow disruption and disorder increase viscous energy losses.
Application of clinical prediction modeling in pediatric neurosurgery: a case study
Authors: Mijderwijk Hendrik-Jan , Beez Thomas , Hänggi Daniel , Nieboer Daan 
Published: March 2021

AbstractThere has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual’s risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.
MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study
Authors: Pinter Daniela , Kober Silvia Erika , Fruhwirth Viktoria , Berger Lisa , Damulina Anna , Khalil Michael , Neuper Christa , Wood Guilherme , Enzinger Christian 
Published: March 2021

Abstract
Objective
Neurofeedback training may improve cognitive function in patients with neurological disorders. However, the underlying cerebral mechanisms of such improvements are poorly understood. Therefore, we aimed to investigate MRI correlates of cognitive improvement after EEG-based neurofeedback training in patients with MS (pwMS).

Methods
Fourteen pwMS underwent ten neurofeedback training sessions within 3–4 weeks at home using a tele-rehabilitation system. Half of the pwMS (N = 7, responders) learned to self-regulate sensorimotor rhythm (SMR, 12–15 Hz) by visual feedback and improved cognitively after training, whereas the remainder (non-responders, n = 7) did not. Diffusion-tensor imaging and resting-state fMRI of the brain was performed before and after training. We analyzed fractional anisotropy (FA) and functional connectivity (FC) of the default-mode, sensorimotor (SMN) and salience network (SAL).

Results
At baseline, responders and non-responders were comparable regarding sex, age, education, disease duration, physical and cognitive impairment, and MRI parameters. After training, compared to non-responders, responders showed increased FA and FC within the SAL and SMN. Cognitive improvement correlated with increased FC in SAL and a correlation trend with increased FA was observed.

Conclusions
This exploratory study suggests that successful neurofeedback training may not only lead to cognitive improvement, but also to increases in brain microstructure and functional connectivity.
Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images
Authors: D’Angelo Gianni , Palmieri Francesco 
Published: March 2021

AbstractWith the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.
Simulative investigation of ring creep on a planetary bearing of a wind turbine gearbox
Authors: Gnauert Jonas , Schlüter Felix , Jacobs Georg , Bosse Dennis , Witter Stefan 
Published: March 2021

AbstractWind turbines (WT) must be further optimized concerning availability and reliability. One of the major reasons of WT downtime is the failure of gearbox bearings. Some of these failures occur, due to the ring creep phenomenon, which is mostly detected in the planetary bearings. The ring creep phenomenon describes a relative movement of the outer ring to the planetary gear. In order to improve the understanding of ring creep, the finite element method (FEM) is used to simulate ring creep in planetary gears. First, a sensitivity analysis is carried out on a small bearing size (NU205), to characterize relevant influence parameters for ring creep—considered parameters are teeth module, coefficient of friction, interference fit and normal tooth forces. Secondly, a full-scale planetary bearing (SL185030) of a 1MW WT is simulated and verified with experimental data.
Rivermead assessment of somatosensory performance: Italian normative data
Authors: Russo Cristina , Spandri Viviana , Gallucci Marcello , Halligan Peter , Bolognini Nadia , Vallar Giuseppe 
Published: March 2021

AbstractThe Rivermead assessment of somatosensory performance (RASP) provides a quantitative assessment of somatosensory processing, suitable for brain-damaged patients suffering from stroke. It consists of seven subcomponents: Subtest 1 (sharp/dull discrimination), Subtest 2 (surface pressure touch), Subtest 3 (surface localization), Subtest 4 (sensory extinction), Subtest 5 (2-point discrimination), Subtest 6 (temperature discrimination), and Subtest 7 (proprioception). Overall, the RASP assesses 5 bilateral body regions: face (cheek), hand (palm and back), and foot (sole and back). This study aimed at providing normative data and cut-off scores for RASP subtests, for each body region, in a large Italian population sample. We present results from 300 healthy Italian individuals aged 19 to 98 years. Data represent a comprehensive set of norms that cover each subtest and each body region tested. Performance in Subtests 1, 5, and 6 decreased, for some body regions, with increasing age. Based on these results, norms were stratified for age (seven groups), with the pathological/non-pathological cut-off coinciding with the 5th percentile. Conversely, other results were not influenced by age; in such cases, a single error, in each body region, has to be considered indicative of pathological performance. This independent investigation of all subcomponents of the somatosensory system, for each body region, further confirms RASP’s potential in clinical practice, for neurological assessment, as well as in research settings.
Clock bias prediction algorithm for navigation satellites based on a supervised learning long short-term memory neural network
Authors: Huang Bohua , Ji Zengxi , Zhai Renjian , Xiao Changfu , Yang Fan , Yang Bohang , Wang Yupu 
Published: March 2021

AbstractIn a satellite navigation system, high-precision prediction of satellite clock bias directly determines the accuracy of navigation, positioning, and time synchronization and is the key to realizing autonomous navigation. To further improve satellite clock bias prediction accuracy, we establish a satellite clock bias prediction model by using long short-term memory (LSTM) that can accurately express the nonlinear characteristics of the navigation satellite clock bias. Outliers in the original clock bias should be preprocessed before using the clock bias for prediction. By analyzing the working principle of the traditional median absolute deviations method, the ambiguity of the mathematical model of that method was improved. Experimental results show that the improved method is better than the traditional method at detecting gross errors. The single difference sequence of the preprocessed satellite clock bias was taken as the research object. First, a quadratic polynomial model was fit to the trend term of the single difference sequence. Second, based on the LSTM neural network model and the basic principles of supervised learning, a supervised learning LSTM network model (SL-LSTM) was proposed that models cyclic and random terms. Finally, the prediction function of the satellite clock bias was realized by extrapolating the model by adding a trend term. We adopt the GPS precision satellite clock bias of International GNSS Service data forecast experiments and apply wavelet neural network (WNN), autoregressive integrated moving average (ARIMA), and quadratic polynomial (QP) models to compare their prediction effects. The average prediction RMSE for 3 h, 6 h, 12 h, 1 d, and 3 d based on the SL-LSTM improved by approximately −21.80, −1.85, 8.57, 2.27, and 40.79%, respectively, compared with the results of the WNN. The average prediction RMSE based on the SL-LSTM improved by approximately 38.23, 65.48, 80.22, 85.18, and 94.51% compared with the ARIMA results. The average prediction RMSE based on the SL-LSTM improved by approximately 82.37, 75.88, 67.24, 45.71, and 58.22% compared with the QP results. Compared with the WNN, the SL-LSTM method has no obvious advantages in the prediction accuracy and stability in short-term prediction but achieves a better long-term prediction accuracy and stability. With an increased prediction duration, the SL-LSTM method is clearly better than the other three methods in terms of the prediction accuracy and stability. The results indicated that the quality of satellite clock bias prediction by the SL-LSTM method is better than that of the above three methods and is more suitable for the middle- and long-term prediction of satellite clock bias.
The role of P53 up-regulated modulator of apoptosis (PUMA) in ovarian development, cardiovascular and neurodegenerative diseases
Authors: Li Mei 
Published: March 2021

AbstractP53 up-regulated modulator of apoptosis (PUMA), a pro-apoptotic BCL-2 homology 3 (BH3)-only member of the BCL-2 family, is a direct transcriptional target of P53 that elicits mitochondrial apoptosis under treatment with radiation and chemotherapy. It also induces excessive apoptosis in cardiovascular and/or neurodegenerative diseases. PUMA has been found to play a critical role in ovarian apoptosis. In the present paper, we review the progress of the study in PUMA over the past two decades in terms of its inducement and/or amplification of programmed cell death and describe recent updates to the understanding of both P53-dependent and P53-independent PUMA-mediated apoptotic pathways that are implicated in physiology and pathology, including the development of the ovary and cardiovascular and neurodegenerative diseases. We propose that PUMA may be a key regulator during ovary development, provide a model for PUMA-mediated apoptotic pathways, including intrinsic and extrinsic apoptotic pathways.
Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions
Authors: Ginesi Michele , Meli Daniele , Roberti Andrea , Sansonetto Nicola , Fiorini Paolo 
Published: March 2021

AbstractObstacle avoidance for Dynamic Movement Primitives (DMPs) is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to represent volumes. In this work, we extend our previous work to include the velocity of the system in the definition of the potential. Our formulations guarantee smoother behavior with respect to state-of-the-art point-like methods. Moreover, our new formulation allows obtaining a smoother behavior in proximity of the obstacle than when using a static (i.e. velocity independent) potential. We validate our framework for obstacle avoidance in a simulated multi-robot scenario and with different real robots: a pick-and-place task for an industrial manipulator and a surgical robot to show scalability; and navigation with a mobile robot in a dynamic environment.
Natural radioactivity and radiological risks of common building materials used in Semnan Province dwellings, Iran
Authors: Imani Morteza , Adelikhah Mohammademad , Shahrokhi Amin , Azimpour Ghazaleh , Yadollahi Ali , Kocsis Erika , Toth-Bodrogi Edit , Kovács Tibor 
Published: March 2021

AbstractImpact assessment of building materials is a focused topic in the field of radioecology. A radiological survey has conducted to monitor radioactivity of most common building materials in Semnan Province, Iran, and assess the radiation risk. Activity concentrations of 226Ra, 232Th, and 40K were measured in 29 samples including nine commonly used building materials that were collected from local suppliers and manufacturers, using a high purity germanium gamma-ray detector. The activity concentrations of 226Ra, 232Th, and 40K varied from 6.7±1 to 43.6±9, 5.9±1 to 60±11, and 28.5±3 to 1085±113 Bq kg−1 with averages of 26.8±5, 22.7±4, and 322.4±4 Bq kg−1, respectively. By applying multivariate statistical approach (Pearson correlation, cluster, and principal component analyses (PCA)), the radiological health hazard parameters were analyzed to obtain similarities and correlations between the various samples. The Pearson correlation showed that the 226Ra distribution in the samples is controlled by changing the 232Th concentration. The variance of 95.58% obtained from PCA resulted that the main radiological health hazard parameters exist due to the concentration of 226Ra and 232Th. The resulting dendrogram of cluster analysis also shows a well coincidence with the correlation analysis.
Optimization of the CCT Curves for Steels Containing Al, Cu and B
Authors: Miettinen Jyrki , Koskenniska Sami , Somani Mahesh , Louhenkilpi Seppo , Pohjonen Aarne , Larkiola Jari , Kömi Jukka 
Published: March 2021

Abstract
New continuous cooling transformation (CCT) equations have been optimized to calculate the start temperatures and critical cooling rates of phase formations during austenite decomposition in low-alloyed steels. Experimental CCT data from the literature were used for applying the recently developed method of calculating the grain boundary soluble compositions of the steels for optimization. These compositions, which are influenced by solute microsegregation and precipitation depending on the heating/cooling/holding process, are expected to control the start of the austenite decomposition, if initiated at the grain boundaries. The current optimization was carried out rigorously for an extended set of steels than used previously, besides including three new solute elements, Al, Cu and B, in the CCT-equations. The validity of the equations was, therefore, boosted not only due to the inclusion of new elements, but also due to the addition of more low-alloyed steels in the optimization. The final optimization was made with a mini-tab tool, which discarded statistically insignificant parameters from the equations and made them prudently safer to use. Using a thermodynamic-kinetic software, IDS, the new equations were further validated using new experimental CCT data measured in this study. The agreement is good both for the phase transformation start temperatures as well as the final phase fractions. In addition, IDS simulations were carried out to construct the CCT diagrams and the final phase fraction diagrams for 17 steels and two cast irons, in order to outline the influence of solute elements on the calculations and their relationship with literature recommendations.
Fire Behavior of 3D-Printed Polymeric Composites
Authors: Babu Karthik , Das Oisik , Shanmugam Vigneshwaran , Mensah Rhoda Afriye , Försth Michael , Sas Gabriel , Restás Ágoston , Berto Filippo 
Published: March 2021

Abstract3D printing or additive manufacturing (AM) is considered as a flexible manufacturing method with the potential for substantial innovations in fabricating geometrically complicated structured polymers, metals, and ceramics parts. Among them, polymeric composites show versatility for applications in various fields, such as constructions, microelectronics and biomedical. However, the poor resistance of these materials against fire must be considered due to their direct relation to human life conservation and safety. In this article, the recent advances in the fire behavior of 3D-printed polymeric composites are reviewed. The article describes the recently developed methods for improving the flame retardancy of 3D-printed polymeric composites. Consequently, the improvements in the fire behavior of 3D-printed polymeric materials through the change in formulation of the composites are discussed. The article is novel in the sense that it is one of the first studies to provide an overview regarding the flammability characteristics of 3D-printed polymeric materials, which will further incite research interests to render AM-based materials fire-resistant.
Virtuelle Planung der chirurgischen Korrektur einer Kraniosynostose
Authors: Raith S. , Lautner N. , Clusmann H. , Hölzle F. , Modabber A. 
Published: March 2021

ZusammenfassungKraniosynostosen sind schwerwiegende kongenitale Komplikationen, die das physiologische Schädelwachstum behindern. Um die dadurch entstehenden Fehlstellungen zu therapieren und eine normale Entwicklung der Schädelform zu ermöglichen, ist oftmals die chirurgische Korrektur unausweichlich. Da Formen und Ausprägungen bei diesem Krankheitsbild sehr stark variieren, ist eine patientenindividuelle Operationsplanung notwendig. Bei dem vorgestellten Fall handelt es sich um die Operationsplanung und -durchführung bei einem 6 Monate alten Mädchen, das mit einer Verknöcherung sowohl der mediofrontalen als auch beider koronalen Suturen geboren wurde, was zu einer Fehlbildung der Schädelform mit flacher, enger Stirn und stark ausgeprägtem Turmschädel führte. Basierend auf computertomographischen Daten wurde eine digitale Planung erstellt, bei der diverse Varianten virtueller Osteotomien vorab am Computer erprobt und beurteilt werden konnten. Dabei lassen sich die Abstände zwischen den Segmenten quantifizieren, um zu große Spaltmaße zu verhindern, die zu Komplikationen führen könnten. Des Weiteren kann mit einem eigens entwickelten Algorithmus die Vergrößerung des Schädelinnenvolumens als Zielgröße berechnet werden. Schließlich wurde eine 3‑D-gedruckte Osteotomieschablone aus Polyamid hergestellt, die intraoperativ eine passgenaue Überführung der geplanten Schnittführung ermöglichte. Die Segmente konnten nach Vorgabe platziert werden, sodass sich harmonische Proportionen ergaben. Das gemessene Schädelinnenvolumen wurde gemäß der digitalen Planung um 5,5 % erhöht. Die erfolgreiche Umsetzung der Methode zeigt, dass computergestützte Planung einen relevanten Mehrwert für die patientenindividuelle Behandlung von Kraniosynostosen haben kann.
Unterschiede zwischen Schüler*innen im Sportunterricht in der Wahrnehmung von Lehrkräften
Authors: Wagner Ingo , Bartsch Fabienne , Rulofs Bettina 
Published: March 2021

ZusammenfassungEin Ziel des vom BMBF geförderten Projektes „Schulsport 2020“ (FKZ: 01JA1622) ist die Unterstützung von (zukünftigen) Sportlehrkräften beim Umgang mit der Heterogenität von Schüler*innen und der Förderung von Inklusion im Sportunterricht. Der Beitrag untersucht, welche Unterschiede Sportlehrkräfte zwischen Schüler*innen wahrnehmen und welche dieser Unterscheidungen sie als relevant für das Fach Sport erachten. Dazu wurden zunächst bestehende Erkenntnisse zu Heterogenität und Differenzen im Sportunterricht systematisiert und darauf aufbauend im Rahmen einer Fragebogenstudie mit 958 Sportlehrkräften verschiedener Schulformen aus Nordrhein-Westfalen Wahrnehmungen der Lehrkräfte von Unterschieden zwischen Schüler*innen erhoben und analysiert. Die Ergebnisse zeigen für 30 Items die jeweilige Relevanzattribuierung. Durch eine explorative Faktorenanalyse hat sich eine 6‑dimensionale Faktorstruktur herauskristallisiert. Diese Faktoren umfassen in abnehmender Relevanzzuschreibung durch die Sportlehrkräfte 1. Unterschiede in Bezug auf personale Merkmale (u. a. Motivation, Selbstvertrauen), 2. in Bezug auf Förderbedarfe, 3. körperbezogene Unterschiede (Geschlecht, Alter), 4. kognitiv-sprachliche (u. a. Intelligenz, Sprachverständnis), 5. organisational-strukturelle (u. a. Zugehörigkeit zu schulsportlichen Talentfördermaßnahmen) und 6. soziokulturell-ökonomische (u. a. Herkunftskultur, Besitz) Unterschiede, die von Sportlehrkräften bei Schüler*innen wahrgenommen werden. Insgesamt bieten die Ergebnisse eine Grundlage für einen systematischen Rahmen, der zur Sensibilisierung von (angehenden) Lehrkräften und zum reflektierten Umgang mit Unterschieden im Fach Sport beitragen kann.