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

Outfit Completion via Conditional Set Transformation

In this paper, we formulate the outfit completion problem as a set retrieval task and propose a novel framework for solving this problem. The proposal includes a conditional set transformation architecture with deep neural networks and a compatibility-based regularization method. The proposed method utilizes a map with permutation-invariant for the input set and permutation-equivariant for the condition set. This allows retrieving a set that is compatible with the input set while reflecting the properties of the condition set. In addition, since this structure outputs the element of the output set in a single inference, it can achieve a scalable inference speed with respect to the cardinality of the output set. Experimental results on real data reveal that the proposed method outperforms existing approaches in terms of accuracy of the outfit completion task, condition satisfaction, and compatibility of completion results.Comment: 8 pages, 8 figure

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