Nonenzymatic loop-closing ligation generates RNA hairpins and is a template-free way to assemble functional RNAs

RNA hairpin loops are the predominant element of secondary structure in functional RNAs. The emergence of primordial functional RNAs, such as ribozymes that fold into complex structures that contain multiple hairpin loops, is generally thought to have been supported by template-directed ligation. However, template inhibition and RNA misfolding problems impede the emergence of function. Here we demonstrate that RNA hairpin loops can be synthesized directly from short RNA duplexes with single-stranded overhangs by nonenzymatic loop-closing ligation chemistry. We show that loop-closing ligation allows full-length functional ribozymes containing a hairpin loop to be assembled free of inhibitory template strands. This approach to the assembly of structurally complex RNAs suggests a plausible pathway for the emergence of functional RNAs before a full-length RNA copying process became available.

OverlapNet: a siamese network for computing LiDAR scan similarity with applications to loop closing and localization

Localization and mapping are key capabilities of autonomous systems. In this paper, we propose a modified Siamese network to estimate the similarity between pairs of LiDAR scans recorded by autonomous cars. This can be used to address both, loop closing for SLAM and global localization. Our approach utilizes a deep neural network exploiting different cues generated from LiDAR data. It estimates the similarity between pairs of scans using the concept of image overlap generalized to range images and furthermore provides a relative yaw angle estimate. Based on such predictions, our method is able to detect loop closures in a SLAM system or to globally localize in a given map. For loop closure detection, we use the overlap prediction as the similarity measurement to find loop closure candidates and integrate the candidate selection into an existing SLAM system to improve the mapping performance. For global localization, we propose a novel observation model using the predictions provided by OverlapNet and integrate it into a Monte-Carlo localization framework. We evaluate our approach on multiple datasets collected using different LiDAR scanners in various environments. The experimental results show that our method can effectively detect loop closures surpassing the detection performance of state-of-the-art methods and that it generalizes well to different environments. Furthermore, our method reliably localizes a vehicle in typical urban environments globally using LiDAR data collected in different seasons.