The regeneration of bone tissue is regulated by both osteogenic and angiogenic growth factors which are expressed in a coordinated cascade of events. The aim of this study was to create a dual growth factor-release system that allows for time-controlled release to facilitate bone regeneration. We fabricated core−shell SF/PCL/PVA nanofibrous mats using coaxial electrospinning and layer-by-layer (LBL) techniques, where bone morphogenetic protein 2 (BMP2) was incorporated into the core of the nanofibers and connective tissue growth factor (CTGF) was attached onto the surface. Our study confirmed the sustained release of BMP2 and a rapid release of CTGF. Both in vitro and in vivo experiments demonstrated improvements in bone tissue recovery with the dual-drug release system. In vivo studies showed improvement in bone regeneration by 43% compared with single BMP2 release systems. Time-controlled release enabled by the core−shell nanofiber assembly provides a promising strategy to facilitate bone healing.
Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods. While multistage methods are seemingly more suited for the task, their performance in current practice is not as good as singlestage methods.This work studies this issue. We argue that the current multi-stage methods' unsatisfactory performance comes from the insufficiency in various design choices. We propose several improvements, including the single-stage module design, cross stage feature aggregation, and coarse-tofine supervision. The resulting method establishes the new state-of-the-art on both MS COCO and MPII Human Pose dataset, justifying the effectiveness of a multi-stage architecture. The source code is publicly available for further research.
Current methods for depth map prediction from monocular images tend to predict smooth, poorly localized contours for the occlusion boundaries in the input image. This is unfortunate as occlusion boundaries are important cues to recognize objects, and as we show, may lead to a way to discover new objects from scene reconstruction. To improve predicted depth maps, recent methods rely on various forms of filtering or predict an additive residual depth map to refine a first estimate. We instead learn to predict, given a depth map predicted by some reconstruction method, a 2D displacement field able to re-sample pixels around the occlusion boundaries into sharper reconstructions. Our method can be applied to the output of any depth estimation method and is fully differentiable, enabling endto-end training. For evaluation, we manually annotated the occlusion boundaries in all the images in the test split of popular NYUv2-Depth dataset. We show that our approach improves the localization of occlusion boundaries for all state-of-the-art monocular depth estimation methods that we could evaluate ([32, 10, 6, 28]), without degrading the depth accuracy for the rest of the images.
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