Search Results (3)

Dysregulation of mRNA processing results in diseases such as cancer. Although RNA editing technologies attract attention as gene therapy for repairing aberrant mRNA, substantial sequence defects arising from mis-splicing cannot be corrected by existing techniques using adenosine deaminase acting on RNA (ADAR) due to the limitation of adenosine-to-inosine point conversion. Here, we report an RNA editing technology called “RNA overwriting” that overwrites the sequence downstream of a designated site on the target RNA by utilizing the RNA-dependent RNA polymerase (RdRp) of the influenza A virus. To enable RNA overwriting within living cells, we developed a modified RdRp by introducing H357A and E361A mutations in the polymerase basic 2 of RdRp and fusing the C-terminus with catalytically inactive Cas13b (dCas13b). The modified RdRp knocked down 46% of the target mRNA and further overwrote 21% of the mRNA. RNA overwriting is a versatile editing technique that can perform various modifications, including addition, deletion, and mutation introduction, and thus allow for repair of the aberrant mRNA produced by dysregulation of mRNA processing, such as mis-splicing. Full article

Unmanned aerial vehicles (UAV), also known as drones have been used for a variety of reasons and the commercial drone market growth is expected to reach remarkable levels in the near future. However, some drone users can mistakenly or intentionally fly into flight paths at major airports, flying too close to commercial aircraft or invading people’s privacy. In order to prevent these unwanted events, counter-drone technology is needed to eliminate threats from drones and hopefully they can be integrated into the skies safely. There are various counter-drone methods available in the industry. However, a counter-drone system supported by an artificial intelligence (AI) method can be an efficient way to fight against drones instead of human intervention. In this paper, a deep reinforcement learning (DRL) method has been proposed to counter a drone in a 3D space by using another drone. In a 2D space it is already shown that the deep reinforcement learning method is an effective way to counter a drone. However, countering a drone in a 3D space with another drone is a very challenging task considering the time required to train and avoid obstacles at the same time. A Deep Q-Network (DQN) algorithm with dueling network architecture and prioritized experience replay is presented to catch another drone in the environment provided by an Airsim simulator. The models have been trained and tested with different scenarios to analyze the learning progress of the drone. Experiences from previous training are also transferred before starting a new training by pre-processing the previous experiences and eliminating those considered as bad experiences. The results show that the best models are obtained with transfer learning and the drone learning progress has been increased dramatically. Additionally, an algorithm which combines imitation learning and reinforcement learning is implemented to catch the target drone. In this algorithm, called deep q-learning from demonstrations (DQfD), expert demonstrations data and self-generated data by the agent are sampled and the agent continues learning without overwriting the demonstration data. The main advantage of this algorithm is to accelerate the learning process even if there is a small amount of demonstration data. Full article

In this article, we analyse the impact of the introduction of options on an investment portfolio. Our first objective is to derive closed-form formulae for the standard measures of portfolio efficiency: risk premium, risk, Sharpe ratio, and beta, of any portfolio containing any combination of options. Using these formulae on three examples of simple option strategies (call overwriting, put protection, and collars), we show how these statistics are altered by the inclusion of an option overlay in a portfolio. Our second objective is to show that if an option strategy is repeated over multiple investment time periods, the long-run return becomes normally distributed. Our motivation is to provide investors with the mathematics to measure the impact of the introduction of options on portfolio efficiency and encourage a potential portfolio rebalance to account for this impact. Then, we highlight that whilst options can create asymmetric non-normal outcomes, their repeated use may not alter the long-run portfolio return in the desired way and thus to encourage investors to assess if an option overlay will deliver the desired long-run outcome. Full article