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This evidence synthesis could better inform clinicians and researchers about the therapeutic effects and limitations of total knee arthroplasty concerning postural balance. Standardization of assessment tools is recommended to strengthen the certainty of cumulative evidence.

Rhythmic Auditory Stimulation (RAS) involves synchronizing footsteps to music or a metronome to improve gait speed and stability in patients with neurological disorders, such as Parkinson’s disease. However, responses to RAS vary across individuals, perhaps because of differences in enjoyment of the music or in musical abilities.

Intuitively, musical enjoyment may influence gait responses to RAS, but enjoyment has not been systematically manipulated nor the effects empirically assessed. In addition, differences in beat perception ability are likely to influence gait responses to music, particularly when synchronizing to the beat. Therefore, we asked how does music enjoyment alter gait, and do gait parameters differ between individuals with good versus poor beat perception ability, specifically when instructed to ‘walk freely’ versus ‘synchronize to the beat’?

Young adults and older adults walked on a pressure sensor walkway in silence and to music that they had rated as either high or low in enjoyment, and thus should be considered when optimizing RAS outcomes.Cone beam computed tomography (CBCT) is a diverse 3D x-ray imaging technique that has gained significant popularity in dental radiology in the last two decades. CBCT overcomes the limitations of traditional two-dimensional dental imaging and enables accurate depiction of multiplanar details of maxillofacial bony structures and surrounding soft tissues. In this review article, we provide an updated status on dental CBCT imaging and summarise the technical features of currently used CBCT scanner models, extending to recent developments in scanner technology, clinical aspects, and regulatory perspectives on dose optimisation, dosimetry, and diagnostic reference levels. We also consider the outlook of potential techniques along with issues that should be resolved in providing clinically more effective CBCT examinations that are optimised for the benefit of the patient.

Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research.

Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-the-art hardware and ongoing challenges and promising research directions in the field.

Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT.

All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.

All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.The present work aimed at determining the applicability of linear sweep voltammetry coupled to disposable carbon paste electrodes to predict chemical composition and wine oxygen consumption rates (OCR) by PLS-modeling of the voltammetric signal. Voltammetric signals were acquired in a set of 16 red commercial wines. Samples were extensively characterized including SO2, antioxidant indexes, metals and polyphenols measured by HPLC. Wine OCRs were calculated by measuring oxygen consumption under controlled oxidation conditions. PLS-Regression models were calculated to predict chemical variables and wine OCRs from first order difference voltammogram curves. A significant number of fully validated models predicting chemical variables from voltammetric signals were obtained. Interestingly, monomeric and polymerized anthocyanins can be differently predicted from the first and second wave of the first derivative of voltammograms, respectively. This fast, cheap and easy-to-use approach presents an important potential to be used in wineries for rapid wine chemical characterization.Pyrolysis kinetics and thermodynamic parameters of two non-edible seeds, Pongamia pinnata (PP) and Sapindus emarginatus (SE), and their blend in the ratio of 11 (PS) were studied using the thermogravimetric analyzer. Kinetic triplets were determined using both model-free [Starink (STR), Friedman (FRM), Iterative Kissinger-Akahira-Sunose (IT-KAS), Iterative Ozawa-Flynn-Wall (IT-OFW), Vyazovkin (VYZ), and Master plot (MP)] and model fitting Coats-Redfern (CR) methods at three different heating rates 10, 30 and 50 °C/min. Activation energies were 192.66, 179.44, and 163.25 kJ/mol for PP, SE, and PS, respectively. buy 7ACC2 It was found that the blend of the two-biomass (PS) showed promising results with lower activation energy compared to the individual biomass. Thermodynamic parameters (ΔG, ΔS, and ΔH) were obtained using the model-free isoconversional method. The three hidden layers of complex neuron topology are well fitted to the experimental DTG curves by artificial neural network (ANN). The study confirmed that the heating rate had a significant impact on the kinetics and thermodynamic parameters.