Background: Rapid stepping reactions are a prevalent response to sudden loss of balance and are thought to play a crucial role in preventing falls. Previous dual-task studies, involving concurrent performance of step reactions and a visuomotor tracking task, indicated that online visual attention was not required to guide the step, even when nearby objects increased demands for accurate foot movement. However, the planning and execution of the step apparently required attentional resources initially allotted to the tracking task. Reallocation of these resources ("attention switching") was delayed in older adults. The present study examined the influence of the competition for attentional resources by comparing trials performed with and without the concurrent task.
Methods: Unpredictable platform perturbations were used to evoke rapid forward stepping reactions in healthy young and older adults. Challenging obstacles and/or step targets increased demands for accurate foot motion in some trials. A concurrent tracking task was performed in half of the trials.
Results: Although participants looked down more frequently in the absence of the tracking task, the ability to clear the obstacle or land on the step target and other spatiotemporal features of the stepping reactions were largely unaffected. There was, however, one notable exception: In older adults, the duration and amplitude of the "anticipatory postural adjustment" that preceded foot lift were reduced in tracking trials, resulting in increased lateral center-of-mass motion.
Conclusion: Impaired attention switching apparently compromised the control of lateral stability during stepping reactions in older adults, and may be an important contributor to increased risk of falling.
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http://dx.doi.org/10.1093/gerona/63.12.1370 | DOI Listing |
Atomic-scale changes can significantly impact heterogeneous catalysis, yet their atomic mechanisms are challenging to establish using conventional analysis methods. By using identical location scanning transmission electron microscopy (IL-STEM), which provides quantitative information at the single-particle level, we investigated the mechanisms of atomic evolution of Ru nanoclusters during the ammonia decomposition reaction. Nanometre-sized disordered nanoclusters transform into truncated nano-pyramids with stepped edges, leading to increased hydrogen production from ammonia.
View Article and Find Full Text PDFMethodsX
June 2025
Department of Materials Science and Engineering, Norwegian University of Science and Technology (NTNU), NO-7491, Trondheim, Norway.
Construction and experimental validation of electrochemical cells with multiple electrodes in a microfluidic channel is described. Details of the fabrication of the electrodes and polydimethylsiloxane channel using soft lithography methods are given. Calibration of the collection efficiencies and transit times between electrodes validate the use of these cells for fast electrochemical detection of soluble species.
View Article and Find Full Text PDFInorg Chem
January 2025
School of Chemistry and Chemical Engineering, and Institute for Innovative Materials and Energy, Yangzhou University, 180 Si-Wang-Ting Road, Yangzhou 225002, China.
The interaction between electrocatalytic active centers and their support is essential to the electrocatalytic performance, which could regulate the electronic structure of the metal centers but requires precise design. Herein, we report on covalent grafting of graphene quantum dots (GQDs) on stepped TiO as a support to anchoring cobalt phosphide nanoparticles (CoP/GQD/S-TiO) for electrocatalytic hydrogen evolution reaction (HER). The covalent ester bonds between GQDs and TiO endow enlarged anchoring sites to achieve highly dispersed electroactive CoP nanoparticles but, more importantly, provide an efficient electron-transfer pathway from TiO to GQDs which could regulate the electronic structure of CoP.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs).
View Article and Find Full Text PDFJ Biol Chem
December 2024
Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, Minnesota, USA. Electronic address:
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