The complexity by which cells regulate gene and protein expression is multifaceted and intricate. Regulation of 3′ untranslated region (UTR) processing of mRNA has been shown to play a critical role in development and disease. However, the process by which cells select alternative mRNA forms is not well understood. We discovered that the Saccharomyces cerevisiae lysine demethylase, Jhd2 (also known as KDM5), recruits 3′UTR processing machinery and promotes alteration of 3′UTR length for some genes in a demethylase-dependent manner. Interaction of Jhd2 with both chromatin and RNA suggests that Jhd2 affects selection of polyadenylation sites through a transcription-coupled mechanism. Furthermore, its mammalian homolog KDM5B (also known as JARID1B or PLU1), but not KDM5A (also known as JARID1A or RBP2), promotes shortening of CCND1 transcript in breast cancer cells. Consistent with these results, KDM5B expression correlates with shortened CCND1 in human breast tumor tissues. In contrast, both KDM5A and KDM5B are involved in the lengthening of DICER1. Our findings suggest both a novel role for this family of demethylases and a novel targetable mechanism for 3′UTR processing.

Autonomous Unmanned Aerial Vehicle (UAV) landing remains a challenge in uncertain environments, e.g., landing on a mobile ground platform such as an Unmanned Ground Vehicle (UGV) without knowing its motion dynamics. A traditional PID (Proportional, Integral, Derivative) controller is a choice for the UAV landing task, but it suffers the problem of manual parameter tuning, which becomes intractable if the initial landing condition changes or the mobile platform keeps moving. In this paper, we design a novel learning-based controller that integrates a standard PID module with a deep reinforcement learning module, which can automatically optimize the PID parameters for velocity control. In addition, corrective feedback based on heuristics of parameter tuning can speed up the learning process compared with traditional DRL algorithms that are typically time-consuming. In addition, the learned policy makes the UAV landing smooth and fast by allowing the UAV to adjust its speed adaptively according to the dynamics of the environment. We demonstrate the effectiveness of the proposed algorithm in a variety of quadrotor UAV landing tasks with both static and dynamic environmental settings.

Infrared and visible image fusion is an important precondition of realizing target perception for unmanned aerial vehicles (UAVs), then UAV can perform various given missions. Information of texture and color in visible images are abundant, while target information in infrared images is more outstanding. The conventional fusion methods are mostly based on region segmentation; as a result, the fused image for target recognition could not be actually acquired. In this paper, a novel fusion method of airborne infrared and visible image based on target region segmentation and discrete wavelet transform (DWT) is proposed, which can gain more target information and preserve more background information. The fusion experiments are done on condition that the target is unmoving and observable both in visible and infrared images, targets are moving and observable both in visible and infrared images, and the target is observable only in an infrared image. Experimental results show that the proposed method can generate better fused image for airborne target perception.

Cooperative standoff tracking of moving targets is an important application of fixed-wing unmanned aerial vehicles (UAVs). To cope with the problem of long convergence time and unstable tracking in cooperative target tracking, traditional Lyapunov vector field guidance (LVFG) is modified. The guidance parameter $c$ is discussed, and the gradient descent method is utilized to develop the optimal guidance parameter search algorithm. As for tracking moving targets, an interacting multiple model-based unscented Kalman filter (IMM-UKF) estimator is built for predicting the target state, and the result is used for correcting the guidance law. Meanwhile, a speed-based controller is developed for faster convergence to the desired intervehicle phase, and the stability of the controller is proved using the Lyapunov stability theory. Numerical simulation results indicate the proposed guidance converges faster to the standoff circle without intersecting the orbit. The state estimator reduces the estimate error and the intervehicle phase converges faster to the desired phase than the traditional control method. Furthermore, extensive hardware-in-the-loop simulations are carried out to verify the feasibility of the algorithm.

Multi-UAV cooperative systems are highly regarded in the field of cooperative multi-target localization and tracking due to their advantages of wide coverage and multi-dimensional perception. However, due to the similarity of target visual characteristics and the limitation of UAV sensor resolution, it is difficult for UAVs to correctly distinguish targets that are visually similar to their associations. Incorrect correlation matching between targets will result in incorrect localization and tracking of multiple targets by multiple UAVs. In order to solve the association problem of targets with similar visual characteristics and reduce the localization and tracking errors caused by target association errors, based on the relative positions of the targets, the paper proposes a globally consistent target association algorithm for multiple UAV vision sensors based on triangular topological sequences. In contrast to Siamese neural networks and trajectory correlation, the relative position relationship between targets is used to distinguish and correlate targets with similar visual features and trajectories. The sequence of neighboring triangles of targets is constructed using the relative position relationship, and the feature is a specific triangular network. Moreover, a method for calculating topological sequence similarity with similar transformation invariance is proposed, as well as a two-step optimal association method that considers global objective association consistency. The results of flight experiments indicate that the algorithm achieves an association accuracy of 84.63%, and that two-step association is 12.83% more accurate than single-step association. Through this work, the multi-target association problem with similar or even identical visual characteristics can be solved in the task of cooperative surveillance and tracking of suspicious vehicles on the ground by multiple UAVs.