Off-tissue effects are persistent issues of modern inhibition-based therapies. By merging the strategies of photopharmacology and small-molecule degraders, we introduce a novel concept for persistent spatiotemporal control of induced protein degradation that potentially prevents off-tissue toxicity. Building on the successful principle of bifunctional all-small-molecule Proteolysis Targeting Chimeras (PROTACs), we designed photoswitchable PROTACs (photoPROTACs) by including ortho-F4-azobenzene linkers between both warhead ligands. This highly bistable yet photoswitchable structural component leads to reversible control over the topological distance between both ligands. The azo-cis-isomer is observed to be inactive because the distance defined by the linker is prohibitively short to permit complex formation between the protein binding partners. By contrast, the azo-trans-isomer is active since it can engage both protein partners to form the necessary and productive ternary complex. Importantly, due to the bistable nature of the ortho-F4-azobenzene moiety employed, the photostationary state of the photoPROTAC is persistent, with no need for continuous irradiation. This technique offers reversible on/off switching of protein degradation that is compatible with an intracellular environment and, therefore, could be useful in experimental exploration of biological signaling pathways—such as those crucial for oncogenic signal transduction. Additionally, this strategy may be suitable for therapeutic intervention to address a variety of diseases. By enabling reversible activation and deactivation of protein degradation, photoPROTACs offer advantages over conventional photocaging strategies that irreversibly release active agents.
Elevation maps are a popular data structure for representing the environment of a mobile robot operating outdoors or on not-flat surfaces. Elevation maps store in each cell of a discrete grid the height of the surface at the corresponding place in the environment. However, the use of this 2 1 2-dimensional representation, is disadvantageous when utilized for mapping with mobile robots operating on the ground, since vertical or overhanging objects cannot be represented appropriately. Furthermore, such objects can lead to registration errors when two elevation maps have to be matched. In this paper, an approach is proposed that allows a mobile robot to deal with vertical and overhanging objects in elevation maps. The approach classifies the points in the environment according to whether they correspond to such objects or not. Also presented is a variant of the ICP algorithm that utilizes the classification of cells during the data association. Additionally, it is shown how the constraints computed by the ICP algorithm can be applied to determine globally consistent alignments. Experiments carried out with a real robot in an outdoor environment demonstrate that the proposed approach yields highly accurate elevation maps even in the case of loops. Experimental results are presented demonstrating that that the proposed classification increases the robustness of the scan matching process.
Abstract-Learning maps is one of the fundamental tasks of mobile robots. In the past, numerous efficient approaches to map learning have been proposed. Most of them, however, assume that the robot lives on a plane. In this paper, we consider the problem of learning maps with mobile robots that operate in non-flat environments and apply maximum likelihood techniques to solve the graph-based SLAM problem. Due to the non-commutativity of the rotational angles in 3D, major problems arise when applying approaches designed for the two-dimensional world. The non-commutativity introduces serious difficulties when distributing a rotational error over a sequence of poses. In this paper, we present an efficient solution to the SLAM problem that is able to distribute a rotational error over a sequence of nodes. Our approach applies a variant of gradient descent to solve the error minimization problem. We implemented our technique and tested it on large simulated and real world datasets. We furthermore compared our approach to solving the problem by LU-decomposition. As the experiments illustrate, our technique converges significantly faster to an accurate map with low error and is able to correct maps with bigger noise than existing methods.
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