Notes

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Empirical results demonstrate that the proposed algorithm has a strong competitiveness power in terms of both the performance and the algorithm compactness, and it can well deal with different types of problems with irregular PF and problems with different numbers of objectives.
Two-dimensional fluoroscopy is the standard guidance imaging method for closed endovascular intervention. However, two-dimensional fluoroscopy lacks depth perception for the intervention catheter and causes radiation exposure for both surgeons and patients. In this paper, we extend our previous study and develop the improved three-dimensional (3D) catheter shape estimation using ultrasound imaging. In addition, we perform further quantitative evaluations of endovascular navigation.

First, the catheter tracking accuracy in ultrasound images is improved by adjusting the state vector and adding direction information. Then, the 3D catheter points from the catheter tracking are further optimized based on the 3D catheter shape optimization with a high-quality sample set. Finally, the estimated 3D catheter shapes from ultrasound images are overlaid with preoperative 3D tissue structures for the intuitive endovascular navigation.

the tracking accuracy of the catheter increased by 24.39%, and the accuracy of the catheter shape optimization step also increased by approximately 17.34% compared with our previous study. Furthermore, the overall error of catheter shape estimation was further validated in the catheter intervention experiment of in vitro cardiovascular tissue and in a vivo swine, and the errors were 2.13 mm and 3.37 mm, respectively.

Experimental results demonstrate that the improved catheter shape estimation using ultrasound imaging is accurate and appropriate for endovascular navigation.

Improved navigation reduces the radiation risk because it decreases use of X-ray imaging. In addition, this navigation method can also provide accurate 3D catheter shape information for endovascular surgery.
Improved navigation reduces the radiation risk because it decreases use of X-ray imaging. In addition, this navigation method can also provide accurate 3D catheter shape information for endovascular surgery.To slow down the spread of COVID-19, governments worldwide are trying to identify infected people, and contain the virus by enforcing isolation, and quarantine. However, it is difficult to trace people who came into contact with an infected person, which causes widespread community transmission, and mass infection. To address this problem, we develop an e-government Privacy-Preserving Mobile, and Fog computing framework entitled PPMF that can trace infected, and suspected cases nationwide. We use personal mobile devices with contact tracing app, and two types of stationary fog nodes, named Automatic Risk Checkers (ARC), and Suspected User Data Uploader Node (SUDUN), to trace community transmission alongside maintaining user data privacy. Each user's mobile device receives a Unique Encrypted Reference Code (UERC) when registering on the central application. The mobile device, and the central application both generate Rotational Unique Encrypted Reference Code (RUERC), which broadcasted using the Bluetooth Low Energy (BLE) technology. The ARCs are placed at the entry points of buildings, which can immediately detect if there are positive or suspected cases nearby. HBI-8000 If any confirmed case is found, the ARCs broadcast pre-cautionary messages to nearby people without revealing the identity of the infected person. The SUDUNs are placed at the health centers that report test results to the central cloud application. The reported data is later used to map between infected, and suspected cases. Therefore, using our proposed PPMF framework, governments can let organizations continue their economic activities without complete lockdown.The iterative design of radiotherapy treatment plans is time-consuming and labor-intensive. In order to provide a guidance to treatment planning, Asymmetric network (A-Net) is proposed to predict the optimal 3D dose distribution for lung cancer patients. A-Net was trained and tested in 392 lung cancer cases with the prescription doses of 50Gy and 60Gy. In A-Net, the encoder and decoder are asymmetric, able to preserve input in-formation and to adapt the limitation of GPU memory. Squeeze and excitation (SE) units are used to improve the data-fitting ability. A loss function involving both the dose distribution and prescription dose as ground truth are designed. In the experiment, A-Net is separately trained and tested in the 50Gy and 60Gy da-taset and most of the metrics A-Net achieve similar performance as HD-Unet and 3D-Unet, and some metrics slightly better. In the 50Gy-and-60Gy-combined dataset, most of the A-Net's metrics perform better than the other two. In conclusion, A-Net can ac-curately predict the IMRT dose distribution in the three datasets of 50Gy and 50Gy-and-60Gy-combined dataset.Disturbance, which is generally unknown to the controller, is unavoidable in real-world systems and it may affect the expected system state and output. Existing control methods, like robust model predictive control, can produce robust solutions to maintain the system stability. However, these robust methods trade the solution optimality for stability. In this article, a method called generative adversarial control networks (GACNs) is proposed to train a controller via demonstrations of the optimal controller. By formulating the optimal control problem in the presence of disturbance, the controller trained by GACNs obtains neuro-optimal solutions without knowing the future disturbance and determines the objective function explicitly. A joint loss, composed of the adversarial loss and the least square loss, is designed to be used in the training of the generator. Experimental results on simulated systems with disturbance show that GACNs outperform other compared control methods.Microarray data and protein-protein interaction (PPI) networks have been extensively studied, due to their ability to depict important characteristics of disease-associated genes. Therefore, the paper presents a new gene prioritization algorithm to identify cancer-causing genes, integrating judiciously the complementary information obtained from two data sources. The proposed algorithm selects disease-causing genes by maximizing the importance of selected genes and functional similarity among them. A new quantitative index is introduced to evaluate the importance of a gene. It considers whether a gene exhibits differential expression pattern and has a strong connectivity in the PPI network. As disease-associated genes are expected to have similar expression profiles and topological structures, a scalable non-linear graph fusion technique, termed as ScaNGraF, is proposed to learn a disease-dependent functional similarity network from the co-expression and common neighbor based similarity networks. The proposed ScaNGraF, which is based on message passing algorithm, efficiently combines shared and complementary information provided by different data sources with significantly lower computational cost.
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