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      Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults

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          Abstract

          There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these are not entirely maintained in older age.

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          brms: An R Package for Bayesian Multilevel Models Using Stan

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            Stan: A Probabilistic Programming Language

            Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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              Learning the parts of objects by non-negative matrix factorization.

              Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: Writing – review & editing
                Role: ConceptualizationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                3 June 2022
                2022
                : 17
                : 6
                : e0269417
                Affiliations
                [1 ] Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
                [2 ] Berlin School of Movement Science, Humboldt-Universität zu Berlin, Berlin, Germany
                [3 ] Network Aging Research, Heidelberg University, Heidelberg, Germany
                [4 ] Department of Economics, Engineering, Society and Business Organization, University of Tuscia, Viterbo, Italy
                University of Innsbruck, AUSTRIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-6577-5101
                https://orcid.org/0000-0002-0886-9734
                https://orcid.org/0000-0002-4985-0335
                Article
                PONE-D-21-25840
                10.1371/journal.pone.0269417
                9165881
                35658057
                bf2760e8-63f5-47f6-8ffd-77068e7f8b2c
                © 2022 Santuz et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 August 2021
                : 22 March 2022
                Page count
                Figures: 8, Tables: 1, Pages: 22
                Funding
                The article processing charge was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 491192747 and the Open Access Publication Fund of Humboldt-Universität zu Berlin.
                Categories
                Research Article
                Biology and Life Sciences
                Physiology
                Biological Locomotion
                Biology and Life Sciences
                Physiology
                Biological Locomotion
                Walking
                Biology and Life Sciences
                Physiology
                Biological Locomotion
                Running
                People and Places
                Population Groupings
                Age Groups
                Adults
                Elderly
                Physical Sciences
                Mathematics
                Geometry
                Fractals
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Skeletal Joints
                Knees
                Knee Joints
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Skeletal Joints
                Knees
                Knee Joints
                Biology and Life Sciences
                Anatomy
                Body Limbs
                Legs
                Knees
                Knee Joints
                Medicine and Health Sciences
                Anatomy
                Body Limbs
                Legs
                Knees
                Knee Joints
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Pelvis
                Hip
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Pelvis
                Hip
                Research and Analysis Methods
                Bioassays and Physiological Analysis
                Electrophysiological Techniques
                Muscle Electrophysiology
                Electromyography
                Custom metadata
                All the recordings and the code used for the analysis can be downloaded from the supplementary data set, accessible at Zenodo (doi: 10.5281/zenodo.5171754).

                Uncategorized
                Uncategorized

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