A hydrogel scaffold for direct tissue-engineering application in water-irrigated, arthroscopic cartilage repair, is badly needed. However, such hydrogels must cure quickly under water, bind strongly and permanently to the surrounding tissue, and maintain sufficient mechanical strength to withstand the hydraulic pressure of arthroscopic irrigation (~10 kilopascal). To address these challenges, we report a versatile hybrid photocrosslinkable (HPC) hydrogel fabricated though a combination of photoinitiated radical polymerization and photoinduced imine cross-linking. The ultrafast gelation, high mechanical strength, and strong adhesion to native tissue enable the direct use of these hydrogels in irrigated arthroscopic treatments. We demonstrate, through in vivo articular cartilage defect repair in the weight-bearing regions of swine models, that the HPC hydrogel can serve as an arthroscopic autologous chondrocyte implantation scaffold for long-term cartilage regeneration, integration, and reconstruction of articular function.

This paper presents a neural network to estimate a detailed depth map of the foreground human in a single RGB image. The result captures geometry details such as cloth wrinkles, which are important in visualization applications. To achieve this goal, we separate the depth map into a smooth base shape and a residual detail shape and design a network with two branches to regress them respectively. We design a training strategy to ensure both base and detail shapes can be faithfully learned by the corresponding network branches. Furthermore, we introduce a novel network layer to fuse a rough depth map and surface normals to further improve the final result. Quantitative comparison with fused 'ground truth' captured by real depth cameras and qualitative examples on unconstrained Internet images demonstrate the strength of the proposed method. Our code will be released at Link

Repair of cartilage defects is highly challenging in clinical treatment. Tissue engineering provides a promising approach for cartilage regeneration and repair. As a core component of tissue engineering, scaffolds have a crucial influence on cartilage regeneration, especially in immunocompetent large animal and human. Native polymers, such as gelatin and hyaluronic acid, have known as ideal biomimetic scaffold sources for cartilage regeneration. However, how to precisely control their structure, degradation rate, and mechanical properties suitable for cartilage regeneration remains a great challenge. To address these issues, a series of strategies were introduced in the current study to optimize the scaffold fabrication. First, gelatin and hyaluronic acid were prepared into a hydrogel and 3D printing was adopted to ensure precise control in both the outer 3D shape and internal pore structure. Second, methacrylic anhydride and a photoinitiator were introduced into the hydrogel system to make the material photocurable during 3D printing. Finally, lyophilization was used to further enhance mechanical properties and prolong degradation time. According to the current results, by integrating photocuring 3D printing and lyophilization techniques, gelatin and hyaluronic acid were successfully fabricated into human ear- and nose-shaped scaffolds, and both scaffolds achieved shape similarity levels over 90% compared with the original digital models. The scaffolds with 50% infill density achieved proper internal pore structure suitable for cell distribution, adhesion, and proliferation. Besides, lyophilization further enhanced mechanical strength of the 3D-printed hydrogel and slowed its degradation rate matching to cartilage regeneration. Most importantly, the scaffolds combined with chondrocytes successfully regenerated mature cartilage with typical lacunae structure and cartilage-specific extracellular matrixes both in vitro and in the autologous goat model. The current study established novel scaffold-fabricated strategies for native polymers and provided a novel natural 3D scaffold with satisfactory outer shape, pore structure, mechanical strength, degradation rate, and weak immunogenicity for cartilage regeneration.

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Detection of video shot transition is a crucial pre-processing step in video analysis. Previous studies are restricted on detecting sudden content changes between frames through similarity measurement and multi-scale operations are widely utilized to deal with transitions of various lengths. However, localization of gradual transitions are still underexplored due to the high visual similarity between adjacent frames. Cut shot transitions are abrupt semantic breaks while gradual shot transitions contain low-level spatial-temporal patterns caused by video effects in addition to the gradual semantic breaks, e.g. dissolve. In order to address the problem, we propose a structured network which is able to detect these two shot transitions using targeted models separately. Considering speed performance trade-offs, we design the following framework. In the first stage, a light filtering module is utilized for collecting candidate transitions on multiple scales. Then, cut transitions and gradual transitions are selected from those candidates by separate detectors. To be more specific, the cut transition detector focus on measuring image similarity and the gradual transition detector is able to capture temporal pattern of consecutive frames, even locating the positions of gradual transitions. The light filtering module can rapidly exclude most of the video frames from further processing and maintain an almost perfect recall of both cut and gradual transitions. The targeted models in the second stage further process the candidates obtained in the first stage to achieve a high precision. With one TITAN GPU, the proposed method can achieve a 30× real-time speed. Experiments on public TRECVID07 and RAI databases show that our method outperforms the state-of-theart methods. In order to train a high-performance shot transition detector, we contribute a new database ClipShots, which contains 128636 cut transitions and 38120 gradual transitions from 4039 online videos. Clip-Shots intentionally collect short videos for more hard cases caused by hand-held camera vibrations, large object motions, and occlusion. The database is avaliable at https://github.com/Tangshitao/ClipShots.