BackgroundEvery effort should be made to optimize surgical techniques and to minimize potential morbidity rates associated with live donor operations. Advances in a minimally invasive approach by robotic surgery to donor nephrectomy have raised the possibility of applying this technique to live donor bowel resections for intestinal transplantation.MethodsWe report the first 5 consecutive cases of a robotic-assisted live donor ileal segmentectomy. We describe the technical aspects of the procedure, discuss the rationale for considering this option, and evaluate potential advantages of this approach.ResultsWe found that this new approach is associated with less postoperative discomfort, a shorter hospital length of stay, and a faster recovery of bowel function compared to our previous open surgery.ConclusionsOur initial experience suggests that robotic surgery is a safe and feasible procedure for live donor ileal resection for intestinal transplantation and is a useful alternative to conventional open surgery.
Stacked denoising autoencoder (SDAE) model has a strong feature learning ability and has shown great success in the classification of remote sensing images. However, built-up area (BUA) information is easily interfered with by broken rocks, bare land, and other features with similar spectral features. SDAEs are vulnerable to broken and similar features in the image. We propose a multiscale SDAE model to overcome these problems, which can extract BUA features in different scales and recognize the type of land object from multiple scales. The model effectively improves the recognition rate of BUA. The experimental results show that our algorithm can resist the disturbance information, and the classification accuracies are better than support vector machine, backpropagation, random forests, and SDAE. Then we investigate an application in Wuhan (China) metropolitan area analysis with the classification results of our algorithm. The range of the metropolitan area is 1.5-h isochronous circle calculated by Tencent map big data and is divided into three layers: core metropolitan area, subcore metropolitan area, and daily metropolitan. Finally, from the comprehensive statistical data and traffic data, we know that the Wuhan metropolitan area has a "target-shaped" distribution structure radiating outward from the core metropolitan area.
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