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Inflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. While therapies exist, response can be limited within the patient population. Researchers have thus studied mouse models of colitis to further understand pathogenesis and identify new treatment targets. Flow cytometry and RNA-sequencing can phenotype immune populations with single-cell resolution but provide no spatial context. Spatial context may be particularly important in colitis mouse models, due to the simultaneous presence of colonic regions that are involved or uninvolved with disease. These regions can be identified on hematoxylin and eosin (H&E)-stained colonic tissue slides based on the presence of abnormal or normal histology. However, detection of such regions requires expert interpretation by pathologists. This can be a tedious process that may be difficult to perform consistently across experiments. To this end, we trained a deep learning model to detect 'Involved' and 'Uninvolved' regions from H&E-stained colonic tissue slides. Our model was trained on specimens from controls and three mouse models of colitis–the dextran sodium sulfate (DSS) chemical induction model, the recently established intestinal epithelium-specific, inducible Klf5ΔIND (Villin-CreERT2;Klf5fl/fl) genetic model, and one that combines both induction methods. Image patches predicted to be 'Involved' and 'Uninvolved' were extracted across mice to cluster and identify histological classes. We quantified the proportion of 'Uninvolved' patches and 'Involved' patch classes in murine swiss-rolled colons. Furthermore, we trained linear determinant analysis classifiers on these patch proportions to predict mouse model and clinical score bins in a prospectively treated cohort of mice. Such a pipeline has the potential to reveal histological links and improve synergy between various colitis mouse model studies to identify new therapeutic targets and pathophysiological mechanisms. [ABSTRACT FROM AUTHOR]

Many soils in the Midwest need artificial drainage for economical crop production, but nitrate loss from subsurface tile drains can cause environmental problems downstream. The impact of tile drains on nitrate loss at the watershed scale is not well quantified. SWAT2005, with its modified tile drain component, was used to evaluate nitrate loss through tile drains in a heavily drained watershed, Sugar Creek watershed in Indiana. Monthly streamflow predictions resulted in Nash-Sutcliffe efficiency values of 0.88 for both the 5-year calibration period and the 6-year validation period. Monthly nitrate-N loads were also predicted well, with Nash-Sutcliffe efficiency values of 0.63 in the calibration period and 0.67 in the validation period. Nitrate-N load from tile drains was added as an output, and the simulated nitrate loss through tile drains was compared with nitrate losses from surface flow, groundwater, and lateral flow. The estimated median percentage of monthly nitrate loss entering Sugar Creek that flows through tile drains ranged from 0% to 37% and was more than 30% for three months, April to June. Results like these could be used in estimating potential nitrate reductions at the watershed level that would be achieved by implementing drainage-related conservation practices throughout the watershed. [ABSTRACT FROM AUTHOR]