Edge computing has become a prominent solution when it comes to mobile applications and data management, due to its ability to considerably reduce data transmission costs, and to analyze data requiring fewer computing resources, since the analysis occurs at lower data volumes, without having to relocate data to centralized infrastructures. One major challenge, indicates the optimal data placement regarding data-intensive applications and, in general, applications requiring vast transmission of large amount of data. In this paper, we propose a novel user mobility-based data placement strategy, considering a trade-off between latency and data migration, which has not been investigated before. We classify the users into three mobility classes; namely static, local or mobile, via the use of a causal-aware Deep Learning network. This information is then exploited in order to optimize the data placement through specific data placement and retrieval algorithms for each mobility class. We evaluate the performance of the proposed solution using simulations, and prove that our solution manages to reduce the average data accessing cost by 60% for static or local users and 10% for mobile users, while the average path length is reduced by 50% for static and local users, and by 12% for mobile users.

This report describes an association between intrahepatic and peripheral blood cytotoxic T lymphocytes (CTL) activity present prior to receiving treatment, and a response to combination interferon-alpha (IFN-alpha) and ribavirin therapy for chronic hepatitis C virus (HCV) infection. Recombinant vaccinia virus constructs were used to expand and detect cytotoxic effectors against the entire genotype 1a HCV polyprotein. Six patients with a sustained response to therapy were significantly more likely to display intrahepatic and peripheral blood HCV-specific CTL activity than patients who relapsed or had no treatment response. Limited longitudinal data suggested that rather than combination therapy acting to enhance the CTL response to achieve viral clearance, detectable CTL prior to treatment increases the likehood of the host responding to the direct antiviral activity of IFN-alpha and ribavirin.

Over the past few years, increasing attention has been given to the health sector and the integration of new technologies into it. Cloud computing and storage clouds have become essentially state of the art solutions for other major areas and have started to rapidly make their presence powerful in the health sector as well. More and more companies are working toward a future that will allow healthcare professionals to engage more with such infrastructures, enabling them a vast number of possibilities. While this is a very important step, less attention has been given to the citizens. For this reason, in this paper, a citizen-centered storage cloud solution is proposed that will allow citizens to hold their health data in their own hands while also enabling the exchange of these data with healthcare professionals during emergency situations. Not only that, in order to reduce the health data transmission delay, a novel context-aware prefetch engine enriched with deep learning capabilities is proposed. The proposed prefetch scheme, along with the proposed storage cloud, is put under a two-fold evaluation in several deployment and usage scenarios in order to examine its performance with respect to the data transmission times, while also evaluating its outcomes compared to other state of the art solutions. The results show that the proposed solution shows significant improvement of the download speed when compared with the storage cloud, especially when large data are exchanged. In addition, the results of the proposed scheme evaluation depict that the proposed scheme improves the overall predictions, considering the coefficient of determination ($R^2$ > 0.94) and the mean of errors (RMSE < 1), while also reducing the training data by 12%.