Matthew Kelly on Muck Rack

Matthew Kelly

As seen in: Emerging Infectious Diseases Journal, Nature, Wiley Online Library, PLOS, BMJ Open, Frontiers, MDPI, Sydney Morning Herald, Taylor & Francis Online, The Conversation and
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Articles

Effectiveness of the BALatrine intervention on soil-transmitted helminth infections in Central Java, Indonesia: a cluster-randomised controlled trial

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By Darren Gray, Mary Lorraine Mationg, Salvador Amaral, Johanna Kurscheid, Min Jeong Park, Aldy aldy, Galuh Wulandari, Sukardi Sukardi, Suharyo Hadisaputro, Fasil Wagnew, Matthew Kelly, Callum Lowe, Lee Jones, Gunter Hartel, Susana Vaz Nery, Kate Halton, James McCarthy, Archie C. A. Clements, Donald Stewart, Budi Laksono, Juniawati Juniawati, Muharsono Muharsono, Sukoto Sukoto, Léa Indjein, Ricardo Soares-Magalhaes
| Nature Verified
Abstract Soil-transmitted helminths (STH) remain a global health challenge, particularly in areas lacking improved sanitation. We evaluated the effectiveness of an integrated BALatrine (an improved pit-latrine) intervention to prevent STH in Central Java, Indonesia. We conducted a cluster-randomized controlled trial involving 2155 participants in 8 village clusters between October 2016 and October 2017.
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Case Report: First case of West Nile virus meningoencephalitis in Southwest Michigan in a patient on ixekizumab and prednisone

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By Henry Zou, Sara ElZalabany, Matthew Kelly, Ingrid Kenyon
| Frontiers
Introduction Meningoencephalitis refers to inflammation of both the meninges and brain parenchyma most commonly caused by viral and bacterial infection (1). It can present with classic meningitis (fever, headache, neck stiffness, photophobia) and encephalitis (altered mentation, focal neurologic deficits, seizures) symptoms (1). Rash sometimes occurs and is frequently concerning for systemic infection (2).
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Machine learning-based classification of ice hockey skating tasks using kinematic data

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By Oussama Jlassi, Matthew Kelly, Philippe Renaud Verified, David Pearsall
| Taylor & Francis Online
ABSTRACT This study evaluates the ability of body segment kinematic data to identify skating tasks in ice hockey using machine learning models and compares the performance of models trained on different body segments. We employed XGBoost, Support Vector Machine and Random Forest models to classify four primary ice-hockey skating tasks: forward skating start and strides, skating stop & go, and skating into a wrist shot.
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