Publications by authors named "P Arevalo"

Article Synopsis
  • State-of-the-art cloud computing platforms like Google Earth Engine (GEE) enhance the process of mapping land cover changes globally using machine learning, but high-quality training data for accurate mapping is still expensive and labor-heavy.
  • To solve this, we developed a global database with nearly 2 million training units from 1984 to 2020, covering seven main and nine secondary land cover classes, using GEE and machine learning for quality and representation.
  • Our database, which includes diverse datasets and reflects regional land characteristics, is useful for various fields, including land cover change studies, agriculture, forestry, hydrology, and urban development.
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Antimicrobial resistance is an important health concern globally, and probiotics are considered an alternative to minimize it. The present study examined the in vitro probiotic characteristics and in vivo immunomodulatory potential of Bacillus sp. 62A - an extremophile bacterium.

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Phagocytosis and phagosome maturation are central processes to the development of the innate and adaptive immune response. Phagosome maturation is a continuous and dynamic process that occurs rapidly. In this chapter we describe fluorescence-based live cell imaging methods for the quantitative and temporal analysis of phagosome maturation of beads and M.

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In non-pharmaceutical management of COVID-19, occupancy of intensive care units (ICU) is often used as an indicator to inform when to intensify mitigation and thus reduce SARS-CoV-2 transmission, strain on ICUs, and deaths. However, ICU occupancy thresholds at which action should be taken are often selected arbitrarily. We propose a quantitative approach using mathematical modeling to identify ICU occupancy thresholds at which mitigation should be triggered to avoid exceeding the ICU capacity available for COVID-19 patients and demonstrate this approach for the United States city of Chicago.

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Influenza imprinting reduces risks of influenza A virus clinical infection by 40%-90%, estimated from surveillance data in western countries. We analyzed surveillance data from 2010 to 2019 in Hong Kong. Based on the best model, which included hemagglutinin group-level imprinting, we estimated that individuals imprinted to H1N1 or H2N2 had a 17% (95% confidence interval [CI], 3%-28%) lower risk of H1N1 clinical infection, and individuals imprinted to H3N2 would have 12% (95% CI, -3% to 26%) lower risk of H3N2 clinical infection.

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