Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals by Sindhuja Tirumalai Govindarajan, Elizabeth Mamourian, Guray Erus, Ahmed Abdulkadir, Randa Melhem, Jimit Doshi, Raymond Pomponio, Duygu Tosun, Murat Bilgel, Yang An, Aristeidis Sotiras, Daniel S. Marcus, Pamela LaMontagne, Tammie L. S. Benzinger, Mark A. Espeland, Colin L. Masters, Paul Maruff, Lenore J. Launer, Jurgen Fripp, Sterling C. Johnson, John C. Morris, Marilyn S. Albert, R. Nick Bryan, Susan M. Resnick, Mohamad Habes, Haochang Shou, David A. Wolk, Ilya M. Nasrallah, Christos Davatzikos

“…To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. …”
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A Distributed Machine Learning Model for Blue and Green Water Resources With Transferable Applications in Similar Climatic Zones by Zhibin Li, Haroon Sahotra, Sajjad Ahmad, Wei Wang, Zhe Yang, Pute Wu, Eakalak Khan, La Zhuo

“…The model performed better in regions with higher human activity intensity. Precipitation and spatial encoding are respectively the most sensitive feature variables for the green water and blue water models, while nighttime lights and population density are respectively the most significant human activity‐related features. …”
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