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Canonical models of intestinal regeneration emphasize the critical role of the crypt stem cell niche to generate enterocytes that migrate to villus ends. Burmese pythons possess extreme intestinal regenerative capacity yet lack crypts, thus providing opportunities to identify noncanonical but potentially conserved mechanisms that expand our understanding of regenerative capacity in vertebrates, including humans. Here, we leverage single-nucleus RNA sequencing of fasted and postprandial python small intestine to identify the signaling pathways and cell–cell interactions underlying the python’s regenerative response. We find that python intestinal regeneration entails the activation of multiple conserved mechanisms of growth and stress response, including core lipid metabolism pathways and the unfolded protein response in intestinal enterocytes. Our single-cell resolution highlights extensive heterogeneity in mesenchymal cell population signaling and intercellular communication that directs major tissue restructuring and the shift out of a dormant fasted state by activating both embryonic developmental and wound healing pathways. We also identify distinct roles of BEST4+ enterocytes in coordinating key regenerative transitions via NOTCH signaling. Python intestinal regeneration shares key signaling features and molecules with mammalian gastric bypass, indicating that conserved regenerative programs are common to both. Our findings provide different insights into cooperative and conserved regenerative programs and intercellular interactions in vertebrates independent of crypts which have been otherwise obscured in model species where temporal phases of generative growth are limited to embryonic development or recovery from injury. 

Abstract In order to accurately predict the performance of materials under dynamic loading conditions, models have been developed that describe the rate-dependent material behavior and irrecoverable plastic deformation that occurs at elevated strains and applied loads. Most of these models have roots in empirical fits to data and, thus, require the addition of specific parameters that reflect the properties and response of specific materials. In this work, we present a systematic approach to the problem of calibrating a Johnson-Cook plasticity model for 304L stainless steel using experimental testing in which the parameters are treated as dependent on the state of the material and uncovered using experimental data. The results obtained indicate that the proposed approach can make the presence of a discrepancy term in calibration unnecessary and, at the same time, improve the prediction accuracy of the model into new input domains and provide improved understanding of model bias compared to calibration with stationary parameter values.