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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5226169 | PMC |
| http://dx.doi.org/10.1308/rcsann.2016.0079 | DOI Listing |
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The feasibility of using machine learning to predict COVID-19 cases.
Int J Med Inform
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School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, United Kingdom. Electronic address:
Background: Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, emerged as a global health crisis in 2019, resulting in widespread morbidity and mortality. A persistent challenge during the pandemic has been the accuracy of reported epidemic data, particularly in underdeveloped regions with limited access to COVID-19 test kits and healthcare infrastructure. In the post-COVID era, this issue remains crucial.
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College of Kinesiology, University of Saskatchewan, Saskatoon, SK, Canada.
Resistance training (RT) load and volume are considered crucial variables to appropriately prescribe and manage for eliciting the targeted acute responses (i.e., minimizing neuromuscular fatigue) and chronic adaptations (i.
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Department of Biology, New York University, New York, New York, 10003, USA. Electronic address:
The outer membrane is the defining structure of Gram-negative bacteria. We previously demonstrated that it is a major load-bearing component of the cell envelope and is therefore critical to the mechanical robustness of the bacterial cell. Here, to determine the key molecules and moieties within the outer membrane that underlie its contribution to cell envelope mechanics, we measured cell-envelope stiffness across several sets of mutants with altered outer-membrane sugar content, protein content, and electric charge.
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