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Summary Bacteria use rapid contraction of a long sheath of the Type VI secretion system (T6SS) to deliver effectors into a target cell. Here we present an atomic resolution structure of a native contracted Vibrio cholerae sheath determined by cryo-electron microscopy. The sheath subunits, composed of tightly interacting proteins VipA and VipB, assemble into a six-start helix. The helix is stabilized by a core domain assembled from four β-strands donated by one VipA and two VipB molecules. The fold of inner and middle layers is conserved between T6SS and phage sheaths. However, the structure of the outer layer is distinct and suggests a mechanism of interaction of the bacterial sheath with an accessory ATPase, ClpV, that facilitates multiple rounds of effector delivery. Our results provide a mechanistic insight into assembly of contractile nanomachines that bacteria and phages use to translocate macromolecules across membranes.

Inflammasomes are multiprotein complexes that control the innate immune response by activating caspase-1, thus promoting the secretion of cytokines in response to invading pathogens and endogenous triggers. Assembly of inflammasomes is induced by activation of a receptor protein. Many inflammasome receptors require the adapter protein ASC [apoptosis-associated speck-like protein containing a caspase-recruitment domain (CARD)], which consists of two domains, the N-terminal pyrin domain (PYD) and the C-terminal CARD. Upon activation, ASC forms large oligomeric filaments, which facilitate procaspase-1 recruitment. Here, we characterize the structure and filament formation of mouse ASC in vitro at atomic resolution. Information from cryo-electron microscopy and solid-state NMR spectroscopy is combined in a single structure calculation to obtain the atomic-resolution structure of the ASC filament. Perturbations of NMR resonances upon filament formation monitor the specific binding interfaces of ASC-PYD association. Importantly, NMR experiments show the rigidity of the PYD forming the core of the filament as well as the high mobility of the CARD relative to this core. The findings are validated by structurebased mutagenesis experiments in cultured macrophages. The 3D structure of the mouse ASC-PYD filament is highly similar to the recently determined human ASC-PYD filament, suggesting evolutionary conservation of ASC-dependent inflammasome mechanisms.inflammation | protein structure | protein filament | ASC speck | innate immune response

We present a system for the detection of small and potentially obscured obstacles in vegetated terrain. The key novelty of this system is the coupling of a volumetric occupancy map with a 3D Convolutional Neural Network (CNN), which to the best of our knowledge has not been previously done. This architecture allows us to train an extremely efficient and highly accurate system for detection tasks from raw occupancy data. We apply this method to the problem of detecting safe landing zones for autonomous helicopters from LiDAR point clouds. Current methods for this problem rely on heuristic rules and use simple geometric features. These heuristics break down in the presence of low vegetation, as they do not distinguish between vegetation that may be landed on and solid objects that should be avoided. We evaluate the system with a combination of real and synthetic range data. We show our system outperforms various benchmarks, including a system integrating various hand-crafted point cloud features from the literature.

We present a challenging dataset, the TartanAir, for robot navigation task and more. The data is collected in photo-realistic simulation environments in the presence of various light conditions, weather and moving objects. By collecting data in simulation, we are able to obtain multimodal sensor data and precise ground truth labels, including the stereo RGB image, depth image, segmentation, optical flow, camera poses, and LiDAR point cloud. We set up a large number of environments with various styles and scenes, covering challenging viewpoints and diverse motion patterns, which are difficult to achieve by using physical data collection platforms. In order to enable data collection in such large scale, we develop an automatic pipeline, including mapping, trajectory sampling, data processing, and data verification. We evaluate the impact of various factors on visual SLAM algorithms using our data. Results of state-of-the-art algorithms reveal that the visual SLAM problem is far from solved, methods that show good performance on established datasets such as KITTI don't perform well in more difficult scenarios. Although we use the simulation, our goal is to push the limits of Visual SLAM algorithms in the real world by providing a challenging benchmark for testing new methods, as well as large diverse training data for learning-based methods. Our dataset is available at http://theairlab.org/tartanair-dataset.

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Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and largescale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and computationally efficient and bounded for large scale environments. We utilize the CNN segmentation as prior prediction and further optimize 3D grid labels through a novel CRF model. Superpixels are utilized to enforce smoothness and form robust P N high order potential. An efficient mean field inference is developed for the graph optimization. We evaluate our system on the KITTI dataset and improve the segmentation accuracy by 10% over existing systems.