RAG-1 Mutations Associated with B-Cell-Negative SCID Dissociate the Nicking and Transesterification Steps of V(D)J Recombination

Some patients with B-cell-negative severe combined immune deficiency (SCID) carry mutations in RAG-1 or RAG-2 that impair V(D)J recombination. Two recessive RAG-1 mutations responsible for B-cell-negative SCID, R621H and E719K, impair V(D)J recombination without affecting formation of single-site recombination signal sequence complexes, specific DNA contacts, or perturbation of DNA structure at the heptamer-coding junction. The E719K mutation impairs DNA cleavage by the RAG complex, with a greater effect on nicking than on transesterification; a conservative glutamine substitution exhibits a similar effect. When cysteine is substituted for E719, RAG-1 activity is enhanced in Mn2+ but remains impaired in Mg2+, suggesting an interaction between this residue and an essential metal ion. The R621H mutation partially impairs nicking, with little effect on transesterification. The residual nicking activity of the R621H mutant is reduced at least 10-fold upon a change from pH 7.0 to pH 8.4. Site-specific nicking is severely impaired by an alanine substitution at R621 but is spared by substitution with lysine. These observations are consistent with involvement of a positively charged residue at position 621 in the nicking step of the RAG-mediated cleavage reaction. Our data provide a mechanistic explanation for one form of hereditary SCID. Moreover, while RAG-1 is directly involved in catalysis of both nicking and transesterification, our observations indicate that these two steps have distinct catalytic requirements

Weak Topological Insulators in PbTe/SnTe Superlattices

It is desirable to realize topological phases in artificial structures by engineering electronic band structures. In this paper, we investigate $(PbTe)_m(SnTe)_{2n-m}$ superlattices along [001] direction and find a robust weak topological insulator phase for a large variety of layer numbers m and 2n-m. We confirm this topologically non-trivial phase by calculating Z2 topological invariants and topological surface states based on the first-principles calculations. We show that the folding of Brillouin zone due to the superlattice structure plays an essential role in inducing topologically non-trivial phases in this system. This mechanism can be generalized to other systems in which band inversion occurs at multiple momenta, and gives us a brand-new way to engineer topological materials in artificial structures.Comment: 6 pages, 4 figures, another author adde

Spin-filtered Edge States with an Electrically Tunable Gap in a Two-Dimensional Topological Crystalline Insulator

Three-dimensional topological crystalline insulators were recently predicted and observed in the SnTe class of IV-VI semiconductors, which host metallic surface states protected by crystal symmetries. In this work, we study thin films of these materials and expose their potential for device applications. We demonstrate that thin films of SnTe and Pb(1-x)Sn(x)Se(Te) grown along the (001) direction are topologically nontrivial in a wide range of film thickness and carry conducting spin-filtered edge states that are protected by the (001) mirror symmetry via a topological invariant. Application of an electric field perpendicular to the film will break the mirror symmetry and generate a band gap in these edge states. This functionality motivates us to propose a novel topological transistor device, in which charge and spin transport are maximally entangled and simultaneously controlled by an electric field. The high on/off operation speed and coupling of spin and charge in such a device may lead to electronic and spintronic applications for topological crystalline insulators.Comment: 6 pages, 5 figures, minor changes made, accepted to Nature Material

Sulfur-doped Nanographenes Containing Multiple Subhelicenes

In this work, we describe the synthesis and characterization of three novel sulfur-doped nanographenes (NGs) (1–3) containing multiple subhelicenes, including carbo[4]helicenes, thieno[4]helicenes, carbo[5]helicenes, and thieno[5]helicenes. Density functional theory calculations reveal that the helicene substructures in 1–3 possess dihedral angles from 15° to 34°. The optical energy gaps of 1–3 are estimated to be 2.67, 2.45, and 2.30 eV, respectively. These three sulfur-doped NGs show enlarged energy gaps compared to those of their pristine carbon analogues

Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders

Semantic segmentation of point clouds generates comprehensive understanding of scenes through densely predicting the category for each point. Due to the unicity of receptive field, semantic segmentation of point clouds remains challenging for the expression of multi-receptive field features, which brings about the misclassification of instances with similar spatial structures. In this paper, we propose a graph convolutional network DGFA-Net rooted in dilated graph feature aggregation (DGFA), guided by multi-basis aggregation loss (MALoss) calculated through Pyramid Decoders. To configure multi-receptive field features, DGFA which takes the proposed dilated graph convolution (DGConv) as its basic building block, is designed to aggregate multi-scale feature representation by capturing dilated graphs with various receptive regions. By simultaneously considering penalizing the receptive field information with point sets of different resolutions as calculation bases, we introduce Pyramid Decoders driven by MALoss for the diversity of receptive field bases. Combining these two aspects, DGFA-Net significantly improves the segmentation performance of instances with similar spatial structures. Experiments on S3DIS, ShapeNetPart and Toronto-3D show that DGFA-Net outperforms the baseline approach, achieving a new state-of-the-art segmentation performance.Comment: accepted to AAAI Workshop 202

Pave the Way to Grasp Anything: Transferring Foundation Models for Universal Pick-Place Robots

Improving the generalization capabilities of general-purpose robotic agents has long been a significant challenge actively pursued by research communities. Existing approaches often rely on collecting large-scale real-world robotic data, such as the RT-1 dataset. However, these approaches typically suffer from low efficiency, limiting their capability in open-domain scenarios with new objects, and diverse backgrounds. In this paper, we propose a novel paradigm that effectively leverages language-grounded segmentation masks generated by state-of-the-art foundation models, to address a wide range of pick-and-place robot manipulation tasks in everyday scenarios. By integrating precise semantics and geometries conveyed from masks into our multi-view policy model, our approach can perceive accurate object poses and enable sample-efficient learning. Besides, such design facilitates effective generalization for grasping new objects with similar shapes observed during training. Our approach consists of two distinct steps. First, we introduce a series of foundation models to accurately ground natural language demands across multiple tasks. Second, we develop a Multi-modal Multi-view Policy Model that incorporates inputs such as RGB images, semantic masks, and robot proprioception states to jointly predict precise and executable robot actions. Extensive real-world experiments conducted on a Franka Emika robot arm validate the effectiveness of our proposed paradigm. Real-world demos are shown in YouTube (https://www.youtube.com/watch?v=1m9wNzfp_4E ) and Bilibili (https://www.bilibili.com/video/BV178411Z7H2/ )

AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation

We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks, such as making a smiley face using building blocks. These tasks often involve complex multi-step reasoning, presenting significant challenges due to the limited paired data connecting human instructions (e.g., making a smiley face) and robot actions (e.g., end-effector movement). Existing approaches relieve this challenge by adopting an open-loop paradigm decomposing high-level instructions into simple sub-task plans, and executing them step-by-step using low-level control models. However, these approaches are short of instant observations in multi-step reasoning, leading to sub-optimal results. To address this issue, we propose to automatically collect a cognitive robot dataset by Large Language Models (LLMs). The resulting dataset AlphaBlock consists of 35 comprehensive high-level tasks of multi-step text plans and paired observation sequences. To enable efficient data acquisition, we employ elaborated multi-round prompt designs that effectively reduce the burden of extensive human involvement. We further propose a closed-loop multi-modal embodied planning model that autoregressively generates plans by taking image observations as input. To facilitate effective learning, we leverage MiniGPT-4 with a frozen visual encoder and LLM, and finetune additional vision adapter and Q-former to enable fine-grained spatial perception for manipulation tasks. We conduct experiments to verify the superiority over existing open and closed-loop methods, and achieve a significant increase in success rate by 21.4% and 14.5% over ChatGPT and GPT-4 based robot tasks. Real-world demos are shown in https://www.youtube.com/watch?v=ayAzID1_qQk