Redo-redo aortic root replacement with a mechanical valved conduit in a patient with von Willebrand's disease: Case report

A 40 year-old female, with a history of cardiac surgery for congenital aortic valve stenosis and von Willebrand's disease (VWD) presented with increasing shortness of breath due to mixed aortic valve dysfunction. With a paucity of such cases in the literature, we describe the successful outcome of a patient with VWD who underwent elective redo-redo aortic root replacement with a mechanical valved conduit. She was given a three-month trial of warfarin pre-operatively to evaluate the extent of bleeding risk. Her post-operative course was uneventful and she was discharged home after six days

Local Fast R-CNN Flow for Object-centric Event Recognition in Complex Traffic Scenes

This paper presents a solution for an integrated object-centric event recognition problem for intelligent traffic supervision. We propose a novel event-recognition framework using deep local flow in a fast regionbased convolutional neural network (R-CNN). First, we use a fine-tuned fast R-CNN to accurately extract multi-scale targets in the open environment. Each detected object corresponds to an event candidate. Second, a deep belief propagation method is proposed for the calculation of local fast R-CNN flow (LFRCF) between local convolutional feature matrices of two non-adjacent frames in a sequence. Third, by using the LFRCF features, we can easily identify the moving pattern of each extracted object and formulate a conclusive description of each event candidate. The contribution of this paper is to propose an optimized framework for accurate event recognition. We verify the accuracy of multi-scale object detection and behavior recognition in extensive experiments on real complex road-intersection surveillance videos

A Content-Based Image Retrieval Method Based on the Google Cloud Vision API with WordNet

[[abstract]]Content-Based Image Retrieval (CBIR) method analyzes the content of an image and extracts the features to describe images, also called the image annotations (or called image labels). A machine learning (ML) algorithm is commonly used to get the annotations, but it is a time-consuming process. In addition, the semantic gap is another problem in image labeling. To overcome the first difficulty, Google Cloud Vision API is a solution because it can save much computational time. To resolve the second problem, a transformation method is defined for mapping the undefined terms by using the WordNet. In the experiments, a well-known dataset, Pascal VOC 2007, with 4952 testing figures is used and the Cloud Vision API on image labeling implemented by R language, called Cloud Vision API. At most ten labels of each image if the scores are over 50. Moreover, we compare the Cloud Vision API with well-known ML algorithms. This work found this API yield 42.4% mean average precision (mAP) among the 4,952 images. Our proposed approach is better than three well-known ML algorithms. Hence, this work could be extended to test other image datasets and as a benchmark method while evaluating the performances.[[notice]]補正完