The induction of a peripheral nerve injury is a widely used method in neuroscience for the assessment of repair and pain mechanisms among others. In addition, in the research field of movement disorders, sciatic crush injury has been employed to trigger a dystonia-like phenotype in genetically predisposed DYT-TOR1A rodent models of dystonia. To achieve consistent, reproducible and comparable results after a sciatic nerve crush injury, a standardized method for inducing the nerve crush is essential, in addition to a standardized phenotypical characterization. Attention must be paid not only to the specific assortment of behavioral tests, but also to the technical requirements, the correct execution and consecutive data analysis. This protocol describes in detail how to perform a sciatic nerve crush injury and provides a behavioral test battery for the assessment of motor deficits in rats that includes the open field test, the CatWalk XT gait analysis, the beam walking task, and the ladder rung walking task.

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The induction of a peripheral nerve injury is a widely used method in neuroscience for the assessment of repair and pain mechanisms among others. In addition, in the research field of movement disorders, sciatic crush injury has been employed to trigger a dystonia-like phenotype in genetically predisposed DYT-TOR1A rodent models of dystonia. To achieve consistent, reproducible and comparable results after a sciatic nerve crush injury, a standardized method for inducing the nerve crush is essential, in addition to a standardized phenotypical characterization. Attention must be paid not only to the specific assortment of behavioral tests, but also to the technical requirements, the correct execution and consecutive data analysis. This protocol describes in detail how to perform a sciatic nerve crush injury and provides a behavioral test battery for the assessment of motor deficits in rats that includes the open field test, the CatWalk XT gait analysis, the beam walking task, and the ladder rung walking task.

With the increasing number of augmented reality apps for houses in recent years, home modeling is essential to complete a 3D reconstruction via identifying the primary features of the house based on a 2D floorplan. Due to the dispersed wall arrangement in 2D floor layouts and the abundant interference information surrounding varied thicknesses, existing segmentation methods mainly rely on image morphology or use deep learning models in other fields like Unet. However, these schemes do not solve poor robust performance problems. In this paper, we propose an Reflect Strip Pooling Unet (RSP-Unet) to enhance the segmentation capabilities of the network for strip features. Specifically, we utilize reflect strip pooling to replace the maximum pooling step and reduce feature loss during the downsampling in the Unet network. More importantly, the proposed module is also integrated with the SE (Squeeze-and- Excitation) mechanism to interact with input from several channels, lessen model overfitting, and increase model robustness. Finally, our extensive experience shows that the results on the self-built floorplan dataset demonstrate that the mean Intersection Over Union(mIOU) is increased by 8.34% and the Dice coefficient is increased by 8.78% compared with the original Unet model.