NOTES:The question for minimal resection and sentinel node in early gastric cancer

Surgical therapy for gastric cancer involves both removal of the cancer lesion and complete lymph node dissection.Natural orifice transluminal endoscopic surgery(NOTES) is considered to represent the next revolution in surgery.Many surgeons and endoscopists believe that NOTES may be a superior alternative for early gastric cancer treatment.Sentinel node(SN) navigation surgery for gastric cancer:Single institution results of SN mapping for early gastric cancer are increasingly being considered acceptable.Furthermore,a major large-scale clinical trial of SN mapping for gastric cancer has recently been completed by The Japan Society of SN Navigation Surgery study group.They reported false negative rate of 7.0 while the sensitivity of metastasis detection based on SN status was 93.Combination of SN biopsy and NOTES:This concept was first described by Cahill et al who proved the feasibility of lymphatic mapping and SN biopsy by NOTES.Lymphatic channel filling was immediately observable via the intraperitoneal optics.Partial resection of the stomach by hybrid NOTES:Several centers have already reported gastrectomy assisted by NOTES using the transvaginal route.However,the main problem of full-thickness resection of gastric wall remains endoscopic gastric closure.Establishing an endoscopic suturing method would be an important step toward expanding potential indications.NOTES is met with both enthusiasm and skepticism but will gain its own place as human creativity eventually provides solutions to its technical limitations.In the near future,NOTES can evolve the capacity to complement the existing armamentarium for gastric cancer surgery.

Predictive factors for body weight loss and its impact on quality of life following gastrectomy

AIM To determine the predictive factors and impact of body weight loss on postgastrectomy quality of life(QOL). METHODS We applied the newly developed integrated questionnaire postgastrectomy syndrome assessment scale-45, which consists of 45 items including those from the Short Form-8 and Gastrointestinal Symptom Rating Scale instruments, in addition to 22 newly selected items. Between July 2009 and December 2010, completed questionnaires were received from 2520 patients with curative resection at 1 year or more after having undergone one of six types of gastrectomy for Stage Ⅰ gastric cancer at one of 52 participating institutions. Of those, we analyzed 1777 eligible questionnaires from patients who underwent total gastrectomy with Roux-en-Y procedure(TGRY) or distal gastrectomy with Billroth-I(DGBI) or Roux-en-Y(DGRY) procedures. RESULTS A total of 393, 475 and 909 patients underwent TGRY, DGRY, and DGBI, respectively. The mean age of patients was 62.1 ± 9.2 years. The mean time interval between surgery and retrieval of the questionnaires was 37.0 ± 26.8 mo. On multiple regression analysis, higher preoperative body mass index, total gastrectomy, and female sex, in that order, were independent predictors of greater body weight loss after gastrectomy. There was a significant difference in the degree of weight loss(P < 0.001) among groups stratified according to preoperative body mass index(< 18.5, 18.5-25 and > 25 kg/m2). Multiple linear regression analysis identified lower postoperative body mass index, rather than greater body weight loss postoperatively, as a certain factor for worse QOL(P < 0.0001) after gastrectomy, but the influence of both such factors on QOL was relatively small(R2, 0.028-0.080).CONCLUSION While it is certainly important to maintain adequate body weight after gastrectomy, the impact of body weight loss on QOL is unexpectedly small.

Support-Vector-Machine-based Adaptive Scheduling in Mode 4 Communication

Vehicular ad-hoc networks(VANETs)are mobile networks that use and transfer data with vehicles as the network nodes.Thus,VANETs are essentially mobile ad-hoc networks(MANETs).They allow all the nodes to communicate and connect with one another.One of the main requirements in a VANET is to provide self-decision capability to the vehicles.Cognitive memory,which stores all the previous routes,is used by the vehicles to choose the optimal route.In networks,communication is crucial.In cellular-based vehicle-to-everything(CV2X)communication,vital information is shared using the cooperative awareness message(CAM)that is broadcast by each vehicle.Resources are allocated in a distributed manner,which is known as Mode 4 communication.The support vector machine(SVM)algorithm is used in the SVM-CV2X-M4 system proposed in this study.The k-fold model with different values of k is used to evaluate the accuracy of the SVM-CV2XM4 system.The results show that the proposed system achieves an accuracy of 99.6%.Thus,the proposed system allows vehicles to choose the optimal route and is highly convenient for users.

An advanced segmentation using area and boundary tracing technique in extraction of lungs region

A new method is presented for the segmentation of pulmonary parenchyma. The proposed method is based on the area calculation of different objects in the image. The main purpose of the proposed algorithm is the segment of the lungs images from the computer tomography(CT) images. The original image is binarized using the bit-plane slicing technique and among the different images the best binarized image is chosen. After binarization, the labeling is done and the area of each label is calculated from which the next level of binarized image is obtained. Then, the boundary tracing algorithm is applied to get another level of binarized image. The proposed method is able to extract lung region from the original images. The experimental results show the significance of the proposed method.

Erratum to: An advanced segmentation using area and boundary tracing technique in extraction of lungs region

Erratum to:J.Cent.South Univ.(2014)21:3811-3820DOI:10.1007/s11771-014-2366-9The original version of this article unfortunately contained three mistakes.The mistakes are corrected as follows:1)The spelling of third author is incorrect.The correct name is Jae-Young PYUN.2)The information of corresponding author is incorrect.The correct information should be Goo-Rak KWON,Professor,PhD;Tel/Fax:+98-711-7264102;E-mail:grkwon@chosun.ac.

Data Fusion-Based Machine Learning Architecture for Intrusion Detection

In recent years,the infrastructure of Wireless Internet of Sensor Networks(WIoSNs)has been more complicated owing to developments in the internet and devices’connectivity.To effectively prepare,control,hold and optimize wireless sensor networks,a better assessment needs to be conducted.The field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data analysis.This study investigates the methodology of Real Time Sequential Deep Extreme LearningMachine(RTS-DELM)implemented to wireless Internet of Things(IoT)enabled sensor networks for the detection of any intrusion activity.Data fusion is awell-knownmethodology that can be beneficial for the improvement of data accuracy,as well as for the maximizing of wireless sensor networks lifespan.We also suggested an approach that not only makes the casting of parallel data fusion network but also render their computations more effective.By using the Real Time Sequential Deep Extreme Learning Machine(RTSDELM)methodology,an excessive degree of reliability with a minimal error rate of any intrusion activity in wireless sensor networks is accomplished.Simulation results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed approach.Eventually,threats and a more general outlook are explored.

Deep Learning Approach for Cosmetic Product Detection and Classification

As the amount of online video content is increasing,consumers are becoming increasingly interested in various product names appearing in videos,particularly in cosmetic-product names in videos related to fashion,beauty,and style.Thus,the identification of such products by using image recognition technology may aid in the identification of current commercial trends.In this paper,we propose a two-stage deep-learning detection and classification method for cosmetic products.Specifically,variants of the YOLO network are used for detection,where the bounding box for each given input product is predicted and subsequently cropped for classification.We use four state-of-the-art classification networks,namely ResNet,InceptionResNetV2,DenseNet,and EfficientNet,and compare their performance.Furthermore,we employ dilated convolution in these networks to obtain better feature representations and improve performance.Extensive experiments demonstrate that YOLOv3 and its tiny version achieve higher speed and accuracy.Moreover,the dilated networks marginally outperform the base models,or achieve similar performance in the worst case.We conclude that the proposed method can effectively detect and classify cosmetic products.