After the arterial switch procedure, decreased distensibility of the aortic root has been reported, which means impaired aortic reservoir function of the coronary circulation, but there have been no reports regarding the relationship of this issue to myocardial perfusion. Therefore, in the present study the aortic reservoir function and coronary supply-demand balance were examined in patients after undergoing the arterial switch operation (ASO) around the time of entering elementary school.Diastolic runoff (DR), which is the percentage of diastolic blood flow to total cardiac output, was measured as the index of aortic reservoir function. The subendocardial viability ratio was investigated as the index of coronary supply - demand balance. In the patient group, the aortic root was dilated (p<0.0001) and distensibility was impaired (p<0.0001) in comparison with an age-matched control group. However, there was no difference between the 2 groups in DR or subendocardial viability ratio.Coronary supply - demand balance was preserved in the pediatric ASO patients, despite the aortic root dysfunction. The preserved DR suggests that dilatation of the aorta compensates for aortic reservoir function. Because large artery dysfunction predicts future cardiovascular diseases, careful follow-up is crucial.
Whole Brain Connectomic Architecture (WBCA) is defined as a software architecture of the artificial intelligence (AI) computing platform which consists of empirical neural circuit information in the entire brain. It is constructed with the aim of developing a general-purpose biologically plausible AI to exert brain-like multiple cognitive functions and behaviors in a computational system. We have developed and implemented several functional machine learning modules, based on open mouse connectomic information, which correspond to specific brain regions. WBCA can accelerate efficient engineering development of the intelligent machines built on the architecture of the biological nervous system.
After successful surgical repair in patients with aortic coarctation, the early onset of cardiovascular diseases is an important subsequent complication and one of the causes is the enhanced aortic pressure wave reflection. Balloon angioplasty has become established as an effective alternative to surgery, but there have been no reports regarding pressure wave reflection after balloon dilatation in patients with aortic coarctation. A 29-year-old patient with aortic coarctation was admitted for angioplasty, which was performed successfully. Six months later, catheter examination demonstrated enhanced aortic pressure wave reflection, although there was no pressure difference. After balloon dilatation patients with aortic coarctation may be also at high risk for future cardiovascular diseases. (Circ J 2007; 71: 1821 - 1822)
Scarecrow is an avatar representation method that uses biological information to improve the immersive experience. Traditional computer games and virtual reality (VR) environments focus on operability and the immersive experience. Controller devices are used to communicate from the real world with the virtual world. The usability of control methods affect the immersive experience of the users. Recently, many of these VR environments have adopted motion sensors (e.g. Microsoft Kinect sensors) Motion sensors can measure the user's movements and gestures to input control parameters for the virtual world and a self-avatar. Motion sensor-based games can help users to become more active and take steps to advance their health. Biological information, such as heart rate, body temperature, and muscle fatigue change depending on exercise volume. In this paper, we use a real-time biological information feedback to synchronize the user and the avatar representation. For instance, the color of the avatar changes according to the skin temperature of the user's face. We hypothesize that this representation method will motivate user activities, and improve the immersive experience. We implement a Scarecrow prototype using a streaming server for measurements of the user's behaviors and a front-end exercise game. We also perform an early user study to report the user experience and discuss the effects of the proposed method. We discuss the potential of the Scarecrow, which might encourage human exercise activities, and control human biological information.
We propose a new framework of synthesizing data using deep generative models in a differentially private manner. Within our framework, sensitive data are sanitized with rigorous privacy guarantees in a one-shot fashion, such that training deep generative models is possible without re-using the original data. Hence, no extra privacy costs or model constraints are incurred, in contrast to popular approaches such as Differentially Private Stochastic Gradient Descent (DP-SGD), which, among other issues, causes degradation in privacy guarantees as the training iteration increases. We demonstrate a realization of our framework by making use of the characteristic function and an adversarial re-weighting objective, which are of independent interest as well. Our proposal has theoretical guarantees of performance, and empirical evaluations on multiple datasets show that our approach outperforms other methods at reasonable levels of privacy.
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