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Abstract Aims This study investigates how lumen roughness and urethral length influence urinary flow speed. Methods We used micro‐computed tomography scans to measure the lumen roughness and dimensions for rabbits, cats, and pigs. We designed and fabricated three‐dimensional‐printed urethra mimics of varying roughness and length to perform flow experiments. We also developed a corresponding mathematical model to rationalize the observed flow speed. Results We update the previously reported relationship between body mass and urethra length and diameter, now including 41 measurements for urethra length and 10 measurements for diameter. We report the relationship between lumen diameter and roughness as a function of position down the urethra for rabbits, cats, and pigs. The time course of urinary speed from our mimics is reported, as well as the average speed as a function of urethra length. Conclusions Based on the behavior of our mimics, we conclude that the lumen roughness in mammals reduces flow speed by up to 25% compared to smooth urethras. Urine flows fastest when the urethra length exceeds 25 times its diameter. Longer urethras do not drain faster due to viscous effects counteracting the additional gravitational head. However, flows with our urethra mimics are still 6 times faster than those observed in nature, suggesting that further work is needed to understand flow resistance in the urethra. 

Many models of intuitive physical reasoning posit some kind of mental simulation mechanism, yet everyday environments frequently contain far more objects than people could plausibly represent with their limited cognitive capacity. What determines which objects are actually included in our representations? We asked participants to predict how a ball will bounce through a complex field of obstacles, and probed working memory for objects in the scene that were more and less likely to be relevant to the ball’s trajectory. We evaluate different accounts of relevance and find that successful object memory is best predicted by how frequently a ball’s trajectory is expected to contact that object under a probabilistic simulation model. This suggests that people construct representations for mental simulation efficiently and dynamically, on the fly, by adding objects “just in time”: only when they are expected to become relevant for the next stage of simulation. 

Abstract Science Gateways provide an easily accessible and powerful computing environment for researchers. These are built around a set of software tools that are frequently and heavily used by large number of researchers in specific domains. Science Gateways have been catering to a growing need of researchers for easy to use computational tools, however their usage model is typically single user-centric. As scientific research becomes ever more team oriented, the need driven by user-demand to support integrated collaborative capabilities in Science Gateways is natural progression. Ability to share data/results with others in an integrated manner is an important and frequently requested capability. In this article we will describe and discuss our work to provide a rich environment for data organization and data sharing by integrating the SeedMeLab (formerly SeedMe2) platform with two Science Gateways: CIPRES and GenApp. With this integration we also demonstrate SeedMeLab’s extensible features and how Science Gateways may incorporate and realize FAIR data principles in practice and transform into community data hubs.