Asymmetries in grasp force matching extend beyond quantifying a single measure of maximum grip strength and advance our application of side-specific treatment interventions. A cross sectional study design investigated grasp-force matching performance in right-handed individuals with a stroke and age-matched healthy controls. A visual representation of the 20% Maximum Voluntary Contraction (MVC) was matched in three conditions in the absence of visual feedback with the same (Ipsilateral Remembered - IR) or opposite hand (Concurrent - CC and Contralateral Remembered - CR). Greater overall relative error (RE) was found in contralateral compared to ipsilateral matching tasks. In the CR condition, post hoc analysis revealed significant differences between control and right hemisphere damage (RHD) group (95% CI [16.41-88.59]; p < 0.01) as well as left hemisphere damage (LHD) group and RHD (95% CI [23.4-95.09]; p < 0.01). Right hand matching relative error was 2.49 times larger in the RHD compared to the LHD group. Within the RHD group, matching errors were greater for the right than left hand in both contralateral conditions (95% CI [34.25-101.07]; p < 0.001). Individuals with RHD showed greater asymmetries in contralateral matching tasks compared to LHD and controls. More specifically, the RHD group had the greatest difficulty matching tasks with their right (non-paretic) than left (paretic) hand. In order to elucidate this asymmetry in the clinic the use of complementary grasp measures may be considered.
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http://dx.doi.org/10.1016/j.neulet.2018.07.034 | DOI Listing |
Health Sci Rep
January 2025
Background And Aims: High contact stresses involving the hip have been shown to increase the risk of developing hip osteoarthritis (OA). Although several risk factors have been identified for OA, a holistic approach to predicting contributed factors toward increased hip contact stresses have not been explored. This study was conducted to comprehensively understand the effects of physical activity on high hip contact stress as predisposing factors of OA.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Background: Hemodynamic force (HDF) from cardiac MRI can indicate subclinical myocardial dysfunction, and help identify early cardiac changes in patients with Fabry disease (FD). The hemodynamic change in FD patients remains unclear.
Purpose: To explore HDF changes in FD and the potential of HDF measurements as diagnostic markers indicating early cardiac changes in FD.
Commun Med (Lond)
January 2025
Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Background: High-field magnetic resonance imaging (MRI) is a powerful diagnostic tool but can induce unintended physiological effects, such as nystagmus and dizziness, potentially compromising the comfort and safety of individuals undergoing imaging. These effects likely result from the Lorentz force, which arises from the interaction between the MRI's static magnetic field and electrical currents in the inner ear. Yet, the Lorentz force hypothesis fails to explain observed eye movement patterns in healthy adults fully.
View Article and Find Full Text PDFJ Phys Chem B
January 2025
Department of Chemistry, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran.
The studies on ionic liquids (ILs) and their interaction with different solvents have always been an interesting topic for experimental and computational chemists. Recently, however, deep insights on the molecular structures of the IL-water binary mixtures have been mainly performed through classical simulations. Here, a comprehensive quantum mechanical study is presented on seven 1-butyl-3-methylimidazolium-based ILs in the absence and presence of water.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Microsoft Research AI for Science, 21 Station Road, Cambridge CB1 2FB, United Kingdom.
Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation.
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