Human movement involves the coordination of individual segments controlled by the central nervous system and powered by the muscles. However, visualization of this high-dimensional coordination between kinematic and kinetic parameters is challenging. The purposes of this study were (a) to identify differences in lower extremity coordination between different types of foot orthoses using Kohonen self-organizing maps (SOM) and (b) to demonstrate the SOM visualization of high-dimensional coordination in gait. This study used gait data for twenty subjects while running in four different orthotic conditions (control, posted, molded, and posted-molded) from a previous study. Data for one exemplar participant was used to demonstrate the visualization technique. In this visualization, areas on an output map represent certain characteristics of the gait cycle. By comparing trials of gait in different orthotic conditions a visual analysis of high-dimensional coordination is possible. Posting orthoses were shown to reduce and molded orthoses were shown to increase ankle mobility, respectively. However, when posting and molding were combined, the effects of the molded orthoses over-rode those of the posted orthoses. In fact, trials using posted-molded orthoses enhanced the effects of molded orthoses. SOMs may contribute to a better understanding of changes in the coordination of kinematic and kinetic variables at certain phases of the gait cycle under different conditions.
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http://dx.doi.org/10.1016/j.gaitpost.2011.06.024 | DOI Listing |
J Chem Phys
December 2024
Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, USA.
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View Article and Find Full Text PDFComput Struct Biotechnol J
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Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
In the last decade, it has been recognized that tumors do not exist in isolation but interact with surrounding cells, blood vessels, immune cells, and extracellular matrix components. This understanding has shifted the focus from tumor cells alone to the broader context in which they exist, known as tumor microenvironment (TME). The TME is highly heterogeneous, consisting of various cell types, mainly cancer cells, immune cells, and stromal cells.
View Article and Find Full Text PDFPhys Chem Chem Phys
December 2024
Institut of Physical Chemistry and Electrochemistry, Leibniz University Hannover, Germany.
Accurately calculated infrared spectra are essential for supporting experimental interpretation, yet full-space anharmonic vibrational structure calculations are only feasible for a limited number of degrees of freedom. Fortunately, characteristic spectroscopic signatures are often dominated by a few key vibrations. We propose a computational protocol specifically tailoring high dimensional anharmonic potential energy surfaces for the accurate and efficient calculation of such spectral signatures with vibrational coupled cluster response theory.
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View Article and Find Full Text PDFSensors (Basel)
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School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia.
For solving the facial expression recognition (FER) problem, we introduce a novel feature extractor called the coordinate-based neighborhood attention mechanism (CNAM), which uses the coordinate attention (CA) method to capture the directional relationships in separate horizontal and vertical directions, the input features from a preprocessing unit, and then passes this to two residual blocks, one consisting of the neighborhood attention (NA) mechanism, which captures the local interaction of features within the neighborhood of a feature vector, while the other one contains a channel attention implemented by a multilayer perceptron (MLP). We apply the feature extractor, the CNAM module, to four FER benchmark datasets, namely, RAF-DB, AffectNet(7cls), AffectNet(8cls), and CK+, and through qualitative and quantitative analysis techniques, we conclude that the insertion of the CNAM module could decrease the intra-cluster distances and increase the inter-cluster distances among the high-dimensional feature vectors. The CNAM compares well with other state-of-the-art (SOTA) methods, being the best-performing method for the AffectNet(7cls) and CK+ datasets, while for the RAF-DB and AffectNet(8cls) datasets, its performance is among the top-performing SOTA methods.
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