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Latent Interacting Variable Effects (LIVE) modeling is a framework to integrate different types of microbiome multi-omics data by combining latent variables from single-omic models into a structured meta-model to determine discriminative, interacting multi-omics features driving disease status. We implemented and tested LIVE modeling in publicly available metagenomics and metabolomics datasets from Crohn’s Disease and Ulcerative Colitis patients. Here, LIVE modeling reduced the number of feature correlations from the original data set for CD and UC to tractable numbers and facilitated prioritization of biological associations between microbes, metabolites, enzymes and IBD status through the application of stringent thresholds on generated inferential statistics. We determined LIVE modeling confirmed previously reported IBD biomarkers and uncovered potentially novel disease mechanisms in IBD. LIVE modeling makes a distinct and complementary contribution to the current methods to integrate microbiome data to predict IBD status because of its flexibility to adapt to different types of microbiome multi-omics data, scalability for large and small cohort studies via reliance on latent variables and dimensionality reduction, and the intuitive interpretability of the linear meta-model integrating -omic data types. The results of LIVE modeling and the biological relationships can be represented in networks that connect local correlation structure of single omic data types with global community and omic structure in the latent variable VIP scores. This model arises as novel tool that allows researchers to be more selective about omic feature interaction without disrupting the structural correlation framework provided by sPLS-DA interaction effects modeling. It will lead to form testable hypothesis by identifying potential and unique interactions between metabolome and microbiome that must be considered for future studies. 

Hurricane Maria drastically altered the landscape across the island of Puerto Rico. This article investigates modifications to surface‐atmospheric interactions due to Hurricane Maria induced land damage and the associated impacts on local convective dynamics. Herein, we employed LANDSAT‐8 image mosaics to quantify the hurricane induced land modification. Results of the analysis indicate that the island suffered significant forest damage—much of which registered as a 28.35% increase in barren land and a 10.85% increase in pasture. Smaller changes included a decrease in cultivated agricultural land cover by 0.76%, along with wetland and water increases of 0.62% and 0.25%, respectively. Pre and postMaria land classifications were then assimilated into the Regional Atmospheric Modeling System cloud resolving model for the simulation of the June 23 to July 2, 2018 period under two land conditions. Results of the numerical experiments indicate that surface to atmosphere interactions were significantly modified when the land cover was altered, and that the highest deviations between pre and postMaria convection occurred over elevated areas with extreme hurricane induced land changes, such as the Cordillera Central mountain range and the El Yunque rainforest.