Functioning transferred free muscle innervated by part of the vascularized ulnar nerve connecting the contralateral cervical seventh root to themedian nerve: case report

<p>Abstract</p> <p>Background</p> <p>The limited nerve sources available for the reconstruction and restoration of upper extremity function is the biggest obstacle in the treatment of brachial plexus injury (BPI). We used part of a transplanted vascularized ulnar nerve as a motor source of a free muscle graft.</p> <p>Case presentation</p> <p>A 21-year-old man with a left total brachial plexus injury had received surgical intercostal nerve transfer to the musculocutaneous nerve and a spinal accessory nerve transfer to the suprascapular nerve in another hospital previously. He received transplantation of a free vascularized gracilis muscle, innervated by a part of the transplanted vascularized ulnar nerve connecting the contralateral healthy cervical seventh nerve root (CC7) to the median nerve, and recovered wrist motion and sensation in the palm. At the final examination, the affected wrist could be flexed dorsally by the transplanted muscle, and touch sensation had recovered up to the base of each finger. When his left index and middle fingers were touched or scrubbed, he felt just a mild tingling pain in his right middle fingertip.</p> <p>Conclusion</p> <p>Part of the transplanted vascularized ulnar nerve connected to the contralateral healthy cervical seventh nerve root can be used successfully as a motor source and may be available in the treatment of patients with BPI with scanty motor sources.</p

Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.Comment: 9 pages, 9 figures, 4 table

Expression and regulatory effects on cancer cell behavior of NELL1 and NELL2 in human renal cell carcinoma

We thank Professors Michael Rehli, Yoshiaki Ito, and Kristian Helin for gifting plasmids, Dr. Alasdair MacKenzie (University of Aberdeen) for helpful discussion, and Mr. Takashi Mizukami, Ms. Ryoko Tokuda, and Ms. Sanae Funaoka (Kanazawa University) for technical assistance.Peer reviewedPublisher PD

Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets

In this paper, we propose a method for cloud removal from visible light RGB satellite images by extending the conditional Generative Adversarial Networks (cGANs) from RGB images to multispectral images. Satellite images have been widely utilized for various purposes, such as natural environment monitoring (pollution, forest or rivers), transportation improvement and prompt emergency response to disasters. However, the obscurity caused by clouds makes it unstable to monitor the situation on the ground with the visible light camera. Images captured by a longer wavelength are introduced to reduce the effects of clouds. Synthetic Aperture Radar (SAR) is such an example that improves visibility even the clouds exist. On the other hand, the spatial resolution decreases as the wavelength increases. Furthermore, the images captured by long wavelengths differs considerably from those captured by visible light in terms of their appearance. Therefore, we propose a network that can remove clouds and generate visible light images from the multispectral images taken as inputs. This is achieved by extending the input channels of cGANs to be compatible with multispectral images. The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs. In the available dataset, the proportion of images of the forest or the sea is very high, which will introduce bias in the training dataset if uniformly sampled from the original dataset. Thus, we utilize the t-Distributed Stochastic Neighbor Embedding (t-SNE) to improve the problem of bias in the training dataset. Finally, we confirm the feasibility of the proposed network on the dataset of four bands images, which include three visible light bands and one near-infrared (NIR) band