BackgroundWe used multimodal compositional magnetic resonance imaging (MRI) techniques, combined with clinical outcomes, to differentiate the alternations of composition in repair cartilage with allogeneic human adipose-derived mesenchymal progenitor cells (haMPCs) in knee osteoarthritis (KOA) patients.MethodsEighteen patients participated a phase I/IIa clinical trial. All patients were divided randomly into three groups with intra-articular injections of haMPCs: the low-dose (1.0 × 107 cells), mid-dose (2.0 × 107), and high-dose (5.0 × 107) groups with six patients each. Compositional MRI examinations and clinical evaluations were performed at different time points.ResultsSignificant differences were observed in quantitative T1rho, T2, T2star, R2star, and ADC measurements in patients of three dose groups, suggesting a possible compositional changes of cartilage with the treatment of allogeneic haMPCs. Also significant reduction in WOMAC and SF-36 scores showed the symptoms might be alleviated to some extent with this new treatment. As regards sensibilities of multi-parametric mappings to detect compositional or structural changes of cartilage, T1rho mapping was most sensitive to differentiate difference between three dose groups.ConclusionsThese results showed that multi-compositional MRI sequences might be an effective tool to evaluate the promotion of the repair of cartilage with allogeneic haMPCs by providing information of compositional alterations of cartilage.Trial registrationClinicaltrials, NCT02641860. Registered 3 December 2015.

With the rapid increase of mobile devices and online media, more and more people prefer posting/viewing videos online. Generally, these videos are presented on video streaming sites with image thumbnails and text titles. While facing huge amounts of videos, a viewer clicks through a certain video with high probability because of its eye-catching thumbnail. However, current video thumbnails are created manually, which is time-consuming and quality-unguaranteed. And static image thumbnails contain very limited information of the corresponding videos, which prevents users from successfully clicking what they really want to view. In this paper, we address a novel problem, namely GIF thumbnail generation, which aims to automatically generate GIF thumbnails for videos and consequently boost their Click-Through-Rate (CTR). Here, a GIF thumbnail is an animated GIF file consisting of multiple segments from the video, containing more information of the target video than a static image thumbnail. To support this study, we build the first GIF thumbnails benchmark dataset that consists of 1070 videos covering a total duration of 69.1 hours, and 5394 corresponding manually-annotated GIFs. To solve this problem, we propose a learning-based automatic GIF thumbnail generation model, which is called Generative Variational Dual-Encoder (GEVADEN). As not relying on any user interaction information (e.g. time-sync comments and real-time view counts), this model is applicable to newly-uploaded/rarely-viewed videos. Experiments on our built dataset show that GEVADEN significantly outperforms several baselines, including video-summarization and highlight-detection based ones. Furthermore, we develop a pilot application of the proposed model on an online video platform with 9814 videos covering 1231 hours, which shows that our model achieves a 37.5% CTR improvement over traditional image thumbnails. This further validates the effectiveness of the proposed model and the promising application prospect of GIF thumbnails.

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