2 research outputs found

    Development and Application of 3D Kinematic Methodologies for Biomechanical Modelling in Adaptive Sports and Rehabilitation

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    Biomechanical analysis is widely used to assess human movement sciences, specifically using three-dimensional motion capture modelling. There are unprecedented opportunities to increase quantitative knowledge of rehabilitation and recreation for disadvantaged population groups. Specifically, 3D models and movement profiles for human gait analysis were generated with emphasis on post-stroke patients, with direct model translation to analyze equivalent measurements while horseback riding in use of the alternative form of rehabilitation, equine assisted activities and therapies (EAAT) or hippotherapy (HPOT). Significant improvements in gait symmetry and velocity were found within an inpatient rehabilitation setting for patients following a stroke, and the developed movement profiles for patients have the potential to address patient recovery timelines. For population groups, such as those following a cerebral incident, alternative forms of rehabilitation like EAAT and HPOT are largely unexplored. Within these studies, relevant muscular activations were found between healthy human gait and horseback riding, supporting the belief that horseback riding can stimulate similar movements within the rider. Even more, there was a strong correlation between the horse’s pelvic rotations, and the responsive joint moments and rotations of the rider. These findings could have greater implications in choosing horses, depending on the desired physical outcome, for EAAT and HPOT purposes. Similar approaches were also used to address another biomechanically disadvantage population, adaptive sport athletes. Utilizing similar methodologies, a novel 3D wheelchair tennis athlete model was created to analyze match-simulation assessments. Significant findings were present in the energy expenditure between two drill assessments. Overall, the quantitative results, coupled with the qualitative assessment chapter, provide a robust assessment of the effects of 3D movement analysis on rehabilitation and adaptive activities

    A global metagenomic map of urban microbiomes and antimicrobial resistance

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    We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.
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