Abstract-This paper studies existing direct transcription methods for trajectory optimization applied to robot motion planning. There are diverse alternatives for the implementation of direct transcription. In this study we analyze the effects of such alternatives when solving a robotics problem. Different parameters such as integration scheme, number of discretization nodes, initialization strategies and complexity of the problem are evaluated. We measure the performance of the methods in terms of computational time, accuracy and quality of the solution. Additionally, we compare two optimization methodologies frequently used to solve the transcribed problem, namely Sequential Quadratic Programming (SQP) and Interior Point Method (IPM). As a benchmark, we solve different motion tasks on an underactuated and non-minimal-phase ball-balancing robot with a 10 dimensional state space and 3 dimensional input space. Additionally, we validate the results on a simulated 3D quadrotor. Finally, as a verification of using direct transcription methods for trajectory optimization on real robots, we present hardware experiments on a motion task including path constraints and actuation limits.
CRISPR-Cas induced homology-directed repair (HDR) enables the installation of a broad range of precise genomic modifications from an exogenous donor template. However, applications of HDR in human cells are often hampered by poor efficiency, stemming from a preference for error-prone end joining pathways that yield short insertions and deletions. Here, we describe Recursive Editing, an HDR improvement strategy that selectively retargets undesired indel outcomes to create additional opportunities to produce the desired HDR allele. We introduce a software tool, named REtarget, that enables the rational design of Recursive Editing experiments. Using REtarget-designed guide RNAs in single editing reactions, Recursive Editing can simultaneously boost HDR efficiencies and reduce undesired indels. We also harness REtarget to generate databases for particularly effective Recursive Editing sites across the genome, to endogenously tag proteins, and to target pathogenic mutations. Recursive Editing constitutes an easy-to-use approach without potentially deleterious cell manipulations and little added experimental burden.
Identifying druggable ligand‐binding sites on the surface of the macromolecular targets is an important process in structure‐based drug discovery. Deep‐learning models have been shown to successfully predict ligand‐binding sites of proteins. As a step toward predicting binding sites in RNA and RNA‐protein complexes, we employ three‐dimensional convolutional neural networks. We introduce a dataset splitting approach to minimize structure‐related bias in training data, and investigate the influence of protein‐based neural network pre‐training before fine‐tuning on RNA structures. Models that were pre‐trained on proteins considerably outperformed the models that were trained exclusively on RNA structures. Overall, 71 % of the known RNA binding sites were correctly located within 4 Å of their true centres.
We consider a mean field game among a large population of noncooperative agents divided into two categories: leaders and followers. Each agent is subject to heterogeneous convex constraints and minimizes a quadratic cost function; the cost of each leader is affected by the leaders' aggregate strategy, while the cost of each follower is affected by both the leaders' and followers' aggregate strategy. We propose a decentralized scheme in which the agents update their strategies optimally with respect to a global incentive signal, possibly different for leaders and followers, broadcast by a central coordinator. We propose several incentive update rules that, under different conditions on the problem data, are guaranteed to steer the population to an ε-Nash equilibrium, with ε decreasing linearly to zero as the number of players increases. We illustrate our theoretical results on a demand-response program between electricity consumers and producers in the day-ahead market
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