Reverse gyrase functions in genome integrity maintenance by protecting DNA breaks in vivo

Reverse gyrase introduces positive supercoils to circular DNA and is implicated in genome stability maintenance in thermophiles. The extremely thermophilic crenarchaeon Sulfolobus encodes two reverse gyrase proteins, TopR1 (topoisomerase reverse gyrase 1) and TopR2, whose functions in thermophilic life remain to be demonstrated. Here, we investigated the roles of TopR1 in genome stability maintenance in S. islandicus in response to the treatment of methyl methanesulfonate (MMS), a DNA alkylation agent. Lethal MMS treatment induced two successive events: massive chromosomal DNA backbone breakage and subsequent DNA degradation. The former occurred immediately after drug treatment, leading to chromosomal DNA degradation that concurred with TopR1 degradation, followed by chromatin protein degradation and DNA-less cell formation. To gain a further insight into TopR1 function, the expression of the enzyme was reduced in S. islandicus cells using a CRISPR-mediated mRNA interference approach (CRISPRi) in which topR1 mRNAs were targeted for degradation by endogenous III-B CRISPR-Cas systems. We found that the TopR1 level was reduced in the S. islandicus CRISPRi cells and that the cells underwent accelerated genomic DNA degradation during MMS treatment, accompanied by a higher rate of cell death. Taken together, these results indicate that TopR1 probably facilitates genome integrity maintenance by protecting DNA breaks from thermo-degradation in vivo

Nanobiomotors of archaeal DNA repair machineries:current research status and application potential

Nanobiomotors perform various important functions in the cell, and they also emerge as potential vehicle for drug delivery. These proteins employ conserved ATPase domains to convert chemical energy to mechanical work and motion. Several archaeal nucleic acid nanobiomotors, such as DNA helicases that unwind double-stranded DNA molecules during DNA damage repair, have been characterized in details. XPB, XPD and Hjm are SF2 family helicases, each of which employs two ATPase domains for ATP binding and hydrolysis to drive DNA unwinding. They also carry additional specific domains for substrate binding and regulation. Another helicase, HerA, forms a hexameric ring that may act as a DNA-pumping enzyme at the end processing of double-stranded DNA breaks. Common for all these nanobiomotors is that they contain ATPase domain that adopts RecA fold structure. This structure is characteristic for RecA/RadA family proteins and has been studied in great details. Here we review the structural analyses of these archaeal nucleic acid biomotors and the molecular mechanisms of how ATP binding and hydrolysis promote the conformation change that drives mechanical motion. The application potential of archaeal nanobiomotors in drug delivery has been discussed

Counting double cosets with application to generic 3-manifolds

We study the growth of double cosets in the class of groups with contracting elements, including relatively hyperbolic groups, CAT(0) groups and mapping class groups among others. Generalizing a recent work of Gitik and Rips about hyperbolic groups, we prove that the double coset growth of two Morse subgroups of infinite index is comparable with the orbital growth function. The same result is further obtained for a more general class of subgroups whose limit sets are proper subsets in the entire limit set of the ambient group. As an application, we confirm a conjecture of Maher that hyperbolic 3-manifolds are exponentially generic in the set of 3-manifolds built from Heegaard splitting using complexity in Teichm\"{u}ller metric.Comment: 22 pages, no figures, exposition improved and reference update

Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching

Cross-modality registration between 2D images from cameras and 3D point clouds from LiDARs is a crucial task in computer vision and robotic. Previous methods estimate 2D-3D correspondences by matching point and pixel patterns learned by neural networks, and use Perspective-n-Points (PnP) to estimate rigid transformation during post-processing. However, these methods struggle to map points and pixels to a shared latent space robustly since points and pixels have very different characteristics with patterns learned in different manners (MLP and CNN), and they also fail to construct supervision directly on the transformation since the PnP is non-differentiable, which leads to unstable registration results. To address these problems, we propose to learn a structured cross-modality latent space to represent pixel features and 3D features via a differentiable probabilistic PnP solver. Specifically, we design a triplet network to learn VoxelPoint-to-Pixel matching, where we represent 3D elements using both voxels and points to learn the cross-modality latent space with pixels. We design both the voxel and pixel branch based on CNNs to operate convolutions on voxels/pixels represented in grids, and integrate an additional point branch to regain the information lost during voxelization. We train our framework end-to-end by imposing supervisions directly on the predicted pose distribution with a probabilistic PnP solver. To explore distinctive patterns of cross-modality features, we design a novel loss with adaptive-weighted optimization for cross-modality feature description. The experimental results on KITTI and nuScenes datasets show significant improvements over the state-of-the-art methods. The code and models are available at https://github.com/junshengzhou/VP2P-Match.Comment: To appear at NeurIPS2023 (Spotlight). Code is available at https://github.com/junshengzhou/VP2P-Matc

Fast Learning Radiance Fields by Shooting Much Fewer Rays

Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more efficient data structures. However, these specially designed approaches do not work for most of radiance fields based methods. To resolve this issue, we introduce a general strategy to speed up the learning procedure for almost all radiance fields based methods. Our key idea is to reduce the redundancy by shooting much fewer rays in the multi-view volume rendering procedure which is the base for almost all radiance fields based methods. We find that shooting rays at pixels with dramatic color change not only significantly reduces the training burden but also barely affects the accuracy of the learned radiance fields. In addition, we also adaptively subdivide each view into a quadtree according to the average rendering error in each node in the tree, which makes us dynamically shoot more rays in more complex regions with larger rendering error. We evaluate our method with different radiance fields based methods under the widely used benchmarks. Experimental results show that our method achieves comparable accuracy to the state-of-the-art with much faster training.Comment: Accepted by lEEE Transactions on lmage Processing 2023. Project Page: https://zparquet.github.io/Fast-Learning . Code: https://github.com/zParquet/Fast-Learnin