Background The clinical characteristics of mTOR (mammalian target of rapamycin) inhibitors use in heart transplant recipients and their outcomes have not been well described. Methods and Results We compared patients who received mTOR inhibitors within the first 2 years after heart transplantation to patients who did not by inquiring the United Network for Organ Sharing (UNOS) database between 2010 and 2018. The primary end point was all‐cause mortality with retransplantation as a competing event. Rejection, malignancy, hospitalization for infection, and renal transplantation were secondary end points. There were 1619 (9%) and 15 686 (81%) mTOR inhibitors+ and mTOR inhibitors− patients, respectively. Body mass index, induction, cardiac allograft vasculopathy, calculated panel reactive antibody, and fewer days in 1A status were independently associated with mTOR inhibitors+ status. Over a follow‐up of 10.4 years, there was no difference in all‐cause mortality after adjusting for donor and recipient characteristics (adjusted subdistribution hazard ratio, 1.03 [0.90–1.19]; P =0.66). mTOR inhibitors+ were independently associated with increased risk for rejection (odds ratio [OR], 1.43 [1.11–1.83]; P =0.005) and basal skin cancer (OR, 1.35 [1.19–1.51]; P =0.012) but not for infection or renal transplantation. Conclusions mTOR inhibitors are used in <10% patients in the first 2 years after heart transplantation and are noninferior to contemporary immunosuppression regimens in terms of all‐cause mortality, infection, malignancy, or renal transplantation. They are associated with risk for rejection.

As energy markets become more and more dynamic, the importance of price forecasting has gained a lot of attention over the last few years. Considering also the introduction of new business models and roles, such as Aggregators and energy flexibility traders, in the constantly evolving energy landscape which follows the general opening of the European electricity markets, the need for anticipating energy price trends and flows holds significant business value. On top of that, the exponential renewable energy sources penetration, adds to the challenges introduced to this dynamic scheme of things. Given their volatile and intermittent nature, supply-demand imbalance can reach critical margins, threatening the overall system stability. In the scope of reducing the power imbalances, a forecast for the imbalance volume will be beneficial either from the perspective of the system operator that could minimise mitigation costs, or the market participants that could target extreme prices for maximising their profit, while effectively managing their risks. The development of a deep learning algorithm for the prediction of the net imbalance volume in the UK market is proposed in this paper in comparison with a common but widely used machine learning approach, namely a gradient boosting trees regression model. The variables which contributed the most on those models were mainly the historical values of net imbalance volume. The deep neural network returns a Root mean squared error (RMSE) and Mean Absolute Error (MAE) equal to 200 and 152 MWh in a range of values between [-1.5,2.0] GWh, respectively, the gradient boosting trees model has an RMSE and MAE equal to 203 and 154 MWh, in contrast to an ARIMA model that has RMSE and MAE equal to 226 and 173 MWh.

The presented work proposes ways that modern and upcoming border management systems can be utilized to help the authorities mitigate the spread of infectious diseases. This work was inspired by the latest COVID-19 pandemic that spread all around the world forcing governments to apply restrictions and bans on international travels. The paper presents the case of how the border management solution proposed and extended by the H2020 projects SMILE and ITFLOWS respectively, can be utilized to (i) allow the travellers to make a selfassessment before their travel, (ii) be notified of potential health-related alerts at their destination or transit and (iii) provide to the border authorities advanced information on the travel history of each passenger allowing them to better and faster assess their entry or exit of a country. To the best of the authors' knowledge, the proposed methods are not yet implemented in any border management system and would provide a valuable mean to help mitigate a pandemic from spreading.