Intraoperative ultrasound-guided iodine-125 seed implantation for unresectable pancreatic carcinoma

<p>Abstract</p> <p>Background</p> <p>To assess the feasibility and efficacy of using ¹²⁵I seed implantation under intraoperative ultrasound guidance for unresectable pancreatic carcinoma.</p> <p>Methods</p> <p>Fourteen patients with pancreatic carcinoma that underwent laparotomy and considered unresectable were included in this study. Nine patients were pathologically diagnosed with Stage II disease, five patients with Stage III disease. Fourteen patients were treated with ¹²⁵I seed implantation guided by intraoperative ultrasound and received D₉₀of ¹²⁵I seeds ranging from 60 to 140 Gy with a median of 120 Gy. Five patients received an additional 35–50 Gy from external beam radiotherapy after seed implantation and six patients received 2–6 cycles of chemotherapy.</p> <p>Results</p> <p>87.5% (7/8) of patients received partial to complete pain relief. The response rate of tumor was 78.6%, One-, two-and three-year survival rates were 33.9% and 16.9%, 7.8%, with local control of disease achieved in 78.6% (11/14), and the median survival was 10 months (95% CI: 7.7–12.3).</p> <p>Conclusion</p> <p>There were no deaths related to ¹²⁵I seed implant. In this preliminary investigation, ¹²⁵I seed implant provided excellent palliation of pain relief, local control and prolong the survival of patients with stage II and III disease to some extent.</p

Improving Fairness for Distributed Interactive Applications in Software-Defined Networks

With the popularization of distributed interactive applications (DIAs), for getting good interactive experience among participants, efficient and fair allocation of network resource should be considered. In software-defined networks, the presence of central controllers provides novel solution to deploy customizable routing for interactive applications, which allows fine-grained resource allocation for DIAs to achieve fairness among participants. But opportunities always come with challenges, the wide spread user locations often require distribution of controllers to meet the requirements of applications. Hence, the latency involved among participants is directly affected by the processing time of controllers. In this context, we address the DIAs’ fair resource provisioning problems on computing and links load with the objective of balancing the achievable request rate and fairness among multiple flows in SDN networks. We firstly formulate the problems as a combination of controller loading and routing optimization. Then, we propose proactive assignment controller algorithm based on deep learning and fairness path allocation algorithm to share the bottleneck links. Compared with the state-of-the-art greedy assignment algorithm and priority order allocating algorithm, the final result is proven to get better fairness on controller and link load among DIAs’ participants by trace driven simulation

Unknown Security Attack Detection of Industrial Control System by Deep Learning

With the rapid development of network technologies, the network security of industrial control systems has aroused widespread concern. As a defense mechanism, an ideal intrusion detection system (IDS) can effectively detect abnormal behaviors in a system without affecting the performance of the industrial control system (ICS). Many deep learning methods are used to build an IDS, which rely on massive numbers of variously labeled samples for model training. However, network traffic is imbalanced, and it is difficult for researchers to obtain sufficient attack samples. In addition, the attack variants are rich, and constructing all possible attack types in advance is impossible. In order to overcome these challenges and improve the performance of an IDS, this paper presents a novel intrusion detection approach which integrates a one-dimensional convolutional autoencoder (1DCAE) and support vector data description (SVDD) for the first time. For the two-stage training process, 1DCAE fails to retain the key features of intrusion detection and SVDD has to add restrictions, so a joint optimization solution is introduced. A three-stage optimization process is proposed to obtain better performance. Experiments on the benchmark intrusion detection dataset NSL-KDD show that the proposed method can effectively detect various unknown attacks, learning with only normal traffic. Compared with the recent state-of-art intrusion detection baselines, the proposed method is improved in most metrics