bluuebunny/crossref_metadata_2025_split huggingface dataset:
Small changes in ball position at address cause a chain effect in golf swing
Authors: Kim Sung Eun , Lee Jangyun , Lee Sae Yong , Lee Hae-Dong , Shim Jae Kun , Lee Sung-Cheol 
Published: January 2021

AbstractThe purpose of this study was to investigate how the ball position along the mediolateral (M-L) direction of a golfer causes a chain effect in the ground reaction force, body segment and joint angles, and whole-body centre of mass during the golf swing. Twenty professional golfers were asked to complete five straight shots for each 5 different ball positions along M-L: 4.27 cm (ball diameter), 2.14 cm (ball radius), 0 cm (reference position at preferred ball position), – 2.14 cm, and – 4.27 cm, while their ground reaction force and body segment motions were captured. The dependant variables were calculated at 14 swing events from address to impact, and the differences between the ball positions were evaluated using Statistical Parametric Mapping. The left-sided ball positions at address showed a greater weight distribution on the left foot with a more open shoulder angle compared to the reference ball position, whereas the trend was reversed for the right-sided ball positions. These trends disappeared during the backswing and reappeared during the downswing. The whole-body centre of mass was also located towards the target for the left-sided ball positions throughout the golf swing compared to the reference ball position, whereas the trend was reversed for the right-sided ball positions. We have concluded that initial ball position at address can cause a series of chain effects throughout the golf swing.
Learning a reach trajectory based on binary reward feedback
Authors: van der Kooij Katinka , van Mastrigt Nina M. , Crowe Emily M. , Smeets Jeroen B. J. 
Published: January 2021

AbstractBinary reward feedback on movement success is sufficient for learning some simple sensorimotor mappings in a reaching task, but not for some other tasks in which multiple kinematic factors contribute to performance. The critical condition for learning in more complex tasks remains unclear. Here, we investigate whether reward-based motor learning is possible in a multi-dimensional trajectory matching task and whether simplifying the task by providing feedback on one factor at a time (‘factorized feedback’) can improve learning. In two experiments, participants performed a trajectory matching task in which learning was measured as a reduction in the error. In Experiment 1, participants matched a straight trajectory slanted in depth. We factorized the task by providing feedback on the slant error, the length error, or on their composite. In Experiment 2, participants matched a curved trajectory, also slanted in depth. In this experiment, we factorized the feedback by providing feedback on the slant error, the curvature error, or on the integral difference between the matched and target trajectory. In Experiment 1, there was anecdotal evidence that participants learnt the multidimensional task. Factorization did not improve learning. In Experiment 2, there was anecdotal evidence the multidimensional task could not be learnt. We conclude that, within a complexity range, multiple kinematic factors can be learnt in parallel.
3D characterization of low optical absorption structures in large crystalline sapphire substrates for gravitational wave detectors
Authors: Marchiò Manuel , Leonardi Matteo , Bazzan Marco , Flaminio Raffaele 
Published: January 2021

AbstractVery high-quality sapphire substrates are key elements of the cryogenic Japanese gravitational interferometer KAGRA, in which they are used to build the main mirrors, working as the test masses to sense the gravitational waves. To meet the extreme requirements of this system, the sapphire test masses must possess an extremely low optical absorption, which makes their study challenging using standard methods. In this paper, we illustrate the results obtained on two typical samples using a specialized absorption setup based on the technique of Photo-thermal Common-path Interferometry (PCI). Our system combines a very high sensitivity to small absorption features with the possibility to perform a full three-dimensional mapping of the sample volume. Our results elucidate how the ultra-low absorption variations inside the sample possess a structure that is probably inherited from the growth history of the sample. Some conclusions on the role of structural defects as preferential sites for the inclusion of absorbing centers are drawn.
Role of circulating endothelial cells in assessing the severity of systemic sclerosis and predicting its clinical worsening
Authors: Di Martino Maria Luisa , Frau Alessandra , Losa Francesca , Muggianu Emma , Mura Mario Nicola , Rotta Gianluca , Scotti Lorenza , Marongiu Francesco 
Published: January 2021

AbstractEndothelial damage and fibro-proliferative vasculopathy of small vessels are pathological hallmarks of systemic sclerosis (SSc). The consequence is the detachment of resident elements that become circulating endothelial cells (CECs). The aim of our study was to evaluate the potential of CECs as biomarker in SSc. We enrolled 50 patients with limited cutaneous (lcSSc) and diffuse cutaneous (dcSSc) subset of SSc, who underwent clinical evaluation to establish the organ involvement. CECs were measured by flow-cytometry utilizing a polychromatic panel. An evident difference was observed in CEC counts comparing controls to SSc patients (median 10.5 vs. 152 cells/ml, p < 0.0001) and for the first time, between the two subsets of disease (median lcSSc 132 vs. dcSSc 716 CEC/ml, p < 0.0001). A significant correlation was established between CECs and some SSc clinical parameters, such as digital ulcers, skin and pulmonary involvement, presence of Scl-70 antibodies, nailfold videocapillaroscopy patterns and EUSTAR activity index. After 12 months, CECs correlated with clinical worsening of patients, showing that a number higher than 414 CEC/ml is a strong negative prognostic factor (RR 5.70). Our results indicate that CECs are a direct indicator of systemic vascular damage. Therefore, they can be used as a reliable marker of disease severity.
Impact of a toothpaste with microcrystalline hydroxyapatite on the occurrence of early childhood caries: a 1-year randomized clinical trial
Authors: Paszynska Elzbieta , Pawinska Malgorzata , Gawriolek Maria , Kaminska Inga , Otulakowska-Skrzynska Justyna , Marczuk-Kolada Grazyna , Rzatowski Szymon , Sokolowska Katarzyna , Olszewska Aneta , Schlagenhauf Ulrich , May Theodor W. , Amaechi Bennett T. , Luczaj-Cepowicz Elzbieta 
Published: January 2021

AbstractThe aim of this trial was to determine whether a toothpaste with microcrystalline hydroxyapatite is not inferior to a fluoride toothpaste in prevention of caries in children. This double-blinded randomized control trial compared two toothpastes regarding the occurrence of caries lesions using International Caries Detection and Assessment System (ICDAS) ≥ code 1 on the primary dentition within 336 days. The test group used a fluoride-free hydroxyapatite toothpaste three times daily while control group used a toothpaste with fluoride. 207 children were included in the intention-to-treat analysis; 177 of them finished the study per protocol. An increase in caries ICDAS ≥ code 1 per tooth was observed in 72.7% of the hydroxyapatite-group (n = 88), compared with 74.2% of the fluoride-group (n = 89). The exact one-sided upper 95% confidence limit for the difference in proportion of participants with ICDAS increase ≥ 1 (-1.4%) was 9.8%, which is below the non-inferiority margin of 20% demonstrating non-inferiority of hydroxyapatite compared to the fluoride control toothpaste. This RCT showed for the first time, that in children, the impact of the daily use of a toothpaste with microcrystalline hydroxyapatite on enamel caries progression in the primary dentition is not inferior to a fluoride control toothpaste (Clinical Trials NCT03553966).
An integrated set-up for ex vivo characterisation of biaxial murine artery biomechanics under pulsatile conditions
Authors: van der Bruggen Myrthe M. , Reesink Koen D. , Spronck Paul J. M. , Bitsch Nicole , Hameleers Jeroen , Megens Remco T. A. , Schalkwijk Casper G. , Delhaas Tammo , Spronck Bart 
Published: January 2021

AbstractEx vivo characterisation of arterial biomechanics enables detailed discrimination of the various cellular and extracellular contributions to arterial stiffness. However, ex vivo biomechanical studies are commonly performed under quasi-static conditions, whereas dynamic biomechanical behaviour (as relevant in vivo) may differ substantially. Hence, we aim to (1) develop an integrated set-up for quasi-static and dynamic biaxial biomechanical testing, (2) quantify set-up reproducibility, and (3) illustrate the differences in measured arterial stiffness between quasi-static and dynamic conditions. Twenty-two mouse carotid arteries were mounted between glass micropipettes and kept fully vasodilated. While recording pressure, axial force (F), and inner diameter, arteries were exposed to (1) quasi-static pressure inflation from 0 to 200 mmHg; (2) 300 bpm dynamic pressure inflation (peaking at 80/120/160 mmHg); and (3) axial stretch (λz) variation at constant pressures of 10/60/100/140/200 mmHg. Measurements were performed in duplicate. Single-point pulse wave velocities (PWV; Bramwell-Hill) and axial stiffness coefficients (cax = dF/dλz) were calculated at the in vivo value of λz. Within-subject coefficients of variation were ~ 20%. Dynamic PWVs were consistently higher than quasi-static PWVs (p < 0.001); cax increased with increasing pressure. We demonstrated the feasibility of ex vivo biomechanical characterisation of biaxially-loaded murine carotid arteries under pulsatile conditions, and quantified reproducibility allowing for well-powered future study design.
Measuring myocardial extracellular volume of the right ventricle in patients with congenital heart disease
Authors: Al-Wakeel-Marquard Nadya , Ferreira da Silva Tiago , Jeuthe Sarah , Rastin Sanaz , Muench Frédéric , O h-Ici Darach , Yilmaz Sevim , Berger Felix , Kuehne Titus , Messroghli Daniel R. 
Published: January 2021

AbstractThe right ventricle´s (RV) characteristics—thin walls and trabeculation—make it challenging to evaluate extracellular volume (ECV). We aimed to assess the feasibility of RV ECV measurements in congenital heart disease (CHD), and to introduce a novel ECV analysis tool. Patients (n = 39) and healthy controls (n = 17) underwent cardiovascular magnetic resonance T1 mapping in midventricular short axis (SAX) and transverse orientation (TRANS). Regions of interest (ROIs) were evaluated with regard to image quality and maximum RV wall thickness per ROI in pixels. ECV from plane ROIs was compared with values obtained with a custom-made tool that derives the mean T1 values from a “line of interest” (LOI) centered in the RV wall. In CHD, average image quality was good (no artifacts in the RV, good contrast between blood/myocardium), and RV wall thickness was 1–2 pixels. RV ECV was not quantifiable in 4/39 patients due to insufficient contrast or wall thickness < 1 pixel. RV myocardium tended to be more clearly delineated in SAX than TRANS. ECV from ROIs and corresponding LOIs correlated strongly in both directions (SAX/TRANS: r = 0.97/0.87, p < 0.001, respectively). In conclusion, RV ECV can be assessed if image quality allows sufficient distinction between myocardium and blood, and RV wall thickness per ROI is ≥ 1 pixel. T1 maps in SAX are recommended for RV ECV analysis. LOI application simplifies RV ECV measurements.
Mechanomyography and acceleration show interlimb asymmetries in Parkinson patients without tremor compared to controls during a unilateral motor task
Authors: Schaefer Laura V. , Löffler Nils , Klein Julia , Bittmann Frank N. 
Published: January 2021

AbstractThe mechanical muscular oscillations are rarely the objective of investigations regarding the identification of a biomarker for Parkinson’s disease (PD). Therefore, the aim of this study was to investigate whether or not this specific motor output differs between PD patients and controls. The novelty is that patients without tremor are investigated performing a unilateral isometric motor task. The force of armflexors and the forearm acceleration (ACC) were recorded as well as the mechanomyography of the biceps brachii (MMGbi), brachioradialis (MMGbra) and pectoralis major (MMGpect) muscles using a piezoelectric-sensor-based system during a unilateral motor task at 70% of the MVIC. The frequency, a power-frequency-ratio, the amplitude variation, the slope of amplitudes and their interlimb asymmetries were analysed. The results indicate that the oscillatory behavior of muscular output in PD without tremor deviates from controls in some parameters: Significant differences appeared for the power-frequency-ratio (p = 0.001, r = 0.43) and for the amplitude variation (p = 0.003, r = 0.34) of MMGpect. The interlimb asymmetries differed significantly concerning the power-frequency-ratio of MMGbi (p = 0.013, r = 0.42) and MMGbra (p = 0.048, r = 0.39) as well as regarding the mean frequency (p = 0.004, r = 0.48) and amplitude variation of MMGpect (p = 0.033, r = 0.37). The mean (M) and variation coefficient (CV) of slope of ACC differed significantly (M: p = 0.022, r = 0.33; CV: p = 0.004, r = 0.43). All other parameters showed no significant differences between PD and controls. It remains open, if this altered mechanical muscular output is reproducible and specific for PD.
The role of gene to gene interaction in the breast’s genomic signature of pregnancy
Authors: Gutiérrez-Díez Pedro J. , Gomez-Pilar Javier , Hornero Roberto , Martínez-Rodríguez Julia , López-Marcos Miguel A. , Russo Jose 
Published: January 2021

AbstractFull-term pregnancy at an early age confers long-term protection against breast cancer. Published data shows a specific transcriptomic profile controlling chromatin remodeling that could play a relevant role in the pregnancy-induced protection. This process of chromatin remodeling, induced by the breast differentiation caused by the first full-term pregnancy, has mainly been measured by the expression level of genes individually considered. However, genes equally expressed during the process of chromatin remodeling may behave differently in their interaction with other genes. These changes at the gene cluster level could constitute an additional dimension of chromatin remodeling and therefore of the pregnancy-induced protection. In this research, we apply Information and Graph Theories, Differential Co-expression Network Analysis, and Multiple Regression Analysis, specially designed to examine structural and informational aspects of data sets, to analyze this question. Our findings demonstrate that, independently of the changes in the gene expression at the individual level, there are significant changes in gene–gene interactions and gene cluster behaviors. These changes indicate that the parous breast, through the process of early full-term pregnancy, generates more modules in the networks, with higher density, and a genomic structure performing additional and more complex functions than those found in the nulliparous breast.
Verifying explainability of a deep learning tissue classifier trained on RNA-seq data
Authors: Yap Melvyn , Johnston Rebecca L. , Foley Helena , MacDonald Samual , Kondrashova Olga , Tran Khoa A. , Nones Katia , Koufariotis Lambros T. , Bean Cameron , Pearson John V. , Trzaskowski Maciej , Waddell Nicola 
Published: January 2021

AbstractFor complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions. Explainability models allow transparency of ML algorithms, however their reliability within high-dimensional data is unclear. To test the reliability of the explainability model SHapley Additive exPlanations (SHAP), we developed a convolutional neural network to predict tissue classification from Genotype-Tissue Expression (GTEx) RNA-seq data representing 16,651 samples from 47 tissues. Our classifier achieved an average F1 score of 96.1% on held-out GTEx samples. Using SHAP values, we identified the 2423 most discriminatory genes, of which 98.6% were also identified by differential expression analysis across all tissues. The SHAP genes reflected expected biological processes involved in tissue differentiation and function. Moreover, SHAP genes clustered tissue types with superior performance when compared to all genes, genes detected by differential expression analysis, or random genes. We demonstrate the utility and reliability of SHAP to explain a deep learning model and highlight the strengths of applying ML to transcriptome data.
Water use of Prosopis juliflora and its impacts on catchment water budget and rural livelihoods in Afar Region, Ethiopia
Authors: Shiferaw Hailu , Alamirew Tena , Dzikiti Sebinasi , Bewket Woldeamlak , Zeleke Gete , Schaffner Urs 
Published: January 2021

AbstractDense impenetrable thickets of invasive trees and shrubs compete with other water users and thus disrupt ecosystem functioning and services. This study assessed water use by the evergreenProsopis juliflora,one of the dominant invasive tree species in semi-arid and arid ecosystems in the tropical regions of Eastern Africa. The objectives of the study were to (1) analyze the seasonal water use patterns ofP. juliflorain various locations in Afar Region, Ethiopia, (2) up-scale the water use from individual tree transpiration and stand evapotranspiration (ET) to the entire invaded area, and 3) estimate the monetary value of water lost due to the invasion. The sap flow rates of individualP. julifloratrees were measured using the heat ratio method while stand ET was quantified using the eddy covariance method. Transpiration by individual trees ranged from 1–36 L/day, with an average of 7 L of water per tree per day. The daily average transpiration of aProsopistree was about 3.4 (± 0.5) mm and the daily average ET of a denseProsopisstand was about 3.7 (± 1.6) mm. Using a fractional cover map ofP. juliflora(over an area of 1.18 million ha), water use ofP. juliflorain Afar Region was estimated to be approximately 3.1–3.3 billion m3/yr. This volume of water would be sufficient to irrigate about 460,000 ha of cotton or 330,000 ha of sugar cane, the main crops in the area, which would generate an estimated net benefit of approximately US$ 320 million and US$ 470 million per growing season from cotton and sugarcane, respectively. Hence,P. juliflorainvasion in the Afar Region has serious impacts on water availability and on the provision of other ecosystem services and ultimately on rural livelihoods.
Custom, spray coated receive coils for magnetic resonance imaging
Authors: Zamarayeva A. M. , Gopalan K. , Corea J. R. , Liu M. Z. , Pang K. , Lustig M. , Arias A. C. 
Published: January 2021

AbstractWe have developed a process for fabricating patient specific Magnetic Resonance Imaging (MRI) Radio-frequency (RF) receive coil arrays using additive manufacturing. Our process involves spray deposition of silver nanoparticle inks and dielectric materials onto 3D printed substrates to form high-quality resonant circuits. In this paper, we describe the material selection and characterization, process optimization, and design and testing of a prototype 4-channel neck array for carotid imaging. We show that sprayed polystyrene can form a low loss dielectric layer in a parallel plate capacitor. We also demonstrate that by using sprayed silver nanoparticle ink as conductive traces, our devices are still dominated by sample noise, rather than material losses. These results are critical for maintaining high Signal-to-Noise-Ratio (SNR) in clinical settings. Finally, our prototype patient specific coil array exhibits higher SNR (5 × in the periphery, 1.4 × in the center) than a commercially available array designed to fit the majority of subjects when tested on our custom neck phantom. 3D printed substrates ensure an optimum fit to complex body parts, improve diagnostic image quality, and enable reproducible placement on subjects.
Upscaling the porosity–permeability relationship of a microporous carbonate for Darcy-scale flow with machine learning
Authors: Menke H. P. , Maes J. , Geiger S. 
Published: January 2021

AbstractThe permeability of a pore structure is typically described by stochastic representations of its geometrical attributes (e.g. pore-size distribution, porosity, coordination number). Database-driven numerical solvers for large model domains can only accurately predict large-scale flow behavior when they incorporate upscaled descriptions of that structure. The upscaling is particularly challenging for rocks with multimodal porosity structures such as carbonates, where several different type of structures (e.g. micro-porosity, cavities, fractures) are interacting. It is the connectivity both within and between these fundamentally different structures that ultimately controls the porosity–permeability relationship at the larger length scales. Recent advances in machine learning techniques combined with both numerical modelling and informed structural analysis have allowed us to probe the relationship between structure and permeability much more deeply. We have used this integrated approach to tackle the challenge of upscaling multimodal and multiscale porous media. We present a novel method for upscaling multimodal porosity–permeability relationships using machine learning based multivariate structural regression. A micro-CT image of Estaillades limestone was divided into small 603 and 1203 sub-volumes and permeability was computed using the Darcy–Brinkman–Stokes (DBS) model. The microporosity–porosity–permeability relationship from Menke et al. (Earth Arxiv, https://doi.org/10.31223/osf.io/ubg6p, 2019) was used to assign permeability values to the cells containing microporosity. Structural attributes (porosity, phase connectivity, volume fraction, etc.) of each sub-volume were extracted using image analysis tools and then regressed against the solved DBS permeability using an Extra-Trees regression model to derive an upscaled porosity–permeability relationship. Ten test cases of 3603 voxels were then modeled using Darcy-scale flow with this machine learning predicted upscaled porosity–permeability relationship and benchmarked against full DBS simulations, a numerically upscaled Darcy flow model, and a Kozeny–Carman model. All numerical simulations were performed using GeoChemFoam, our in-house open source pore-scale simulator based on OpenFOAM. We found good agreement between the full DBS simulations and both the numerical and machine learning upscaled models, with the machine learning model being 80 times less computationally expensive. The Kozeny–Carman model was a poor predictor of upscaled permeability in all cases.