Search

Adaptability and robustness are the important expressions of intelligent walking ability of humanoid robots. However, they may be in an unstable state due to the huge impact contact forces produced by foot instant landing. This paper is concerned with the problem of dynamical biped walking and robust control of humanoid robots under ground reaction forces (GRF). In order to imitate human’s muscles to absorb the landing force, the robotic system is modeled as a mass–damp–spring model. The novelty of the article lies in the use of impedance control based on ground reaction forces, which deals with the complicated optimization problem subjected to both equality and inequality constraints. A feedback controller is designed to utilize inertial damping to generate the desired motion trajectory of the robot. The constructing autonomous evolution mechanism is mentioned to realize adaptive optimization of walking model. It ensures that the impact of GRF and reinforce stability during transition from single support phase to double support phase. Finally, the effectiveness of the proposed method is verified by simulations.

Stability and robustness are the important expressions of intelligent walking ability of biped robots. The Zeno behavior caused by the frictional impact of knee joints affects the stability during the dynamic walking, which has greatly limited robot’s application and efficiency. Based on the analysis of the intrinsic mechanism of Zeno behavior, this paper aims to explore biped walking control methods to provide theoretical basis and key technologies for suppressing Zeno behavior. The internal relationship between Zeno behavior and robot knee joint collision is built by studying the cause of Zeno behavior. An event-based feedback controller is proposed to deal with the problem of stabilization of Zeno periodic orbit. It is achieved adaptive periodic stable walking in complex environment based on event-based and hybrid zero dynamic control strategy, which proposes the stability analysis method based on Poincare return map. Meanwhile, the identify parameters of dynamic equations with Zeno behavior is utilized with genetic algorithm and particle swarm optimization. Finally, the effectiveness of the proposed method is verified by simulations.

In the emerging field of urban digital twins (UDTs), there are extensive and captivating opportunities for leveraging cutting-edge deep learning techniques. Particularly within the specialized area of intelligent road inspection (IRI), a noticeable gap exists, underscored by the current dearth of dedicated research efforts and the lack of large-scale well-annotated datasets. To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI. Along with this article, we introduce the road pothole detection task, the first online competition published within this benchmark suite. This task provides a well-annotated dataset, comprising 1,000 RGB images and their pixel/instance-level ground-truth annotations, captured in diverse real-world scenarios under different illumination and weather conditions. Our benchmark provides a systematic and thorough evaluation of state-of-the-art object detection, semantic segmentation, and instance segmentation networks, developed based on either convolutional neural networks or Transformers. We anticipate that our benchmark suite will serve as a catalyst for the integration of advanced UDT techniques into IRI. By providing algorithms with a more comprehensive understanding of diverse road conditions, we seek to unlock their untapped potential and foster innovation in this critical domain.

Recent advancements in nanomaterials have enabled the application of nanotechnology to the development of cutting-edge sensing and actuating devices. For instance, nanostructures’ collective and predictable responses to various stimuli can be monitored to determine the physical environment of the nanomaterial, such as temperature or applied pressure. To achieve optimal sensing and actuation capabilities, the nanostructures should be controllable. However, current applications are limited by inherent challenges in controlling nanostructures that counteract many sensing mechanisms that are reliant on their area or spacing. This work presents a technique utilizing the piezo-magnetoelectric properties of nanoparticles to enable strain sensing and actuation in a flexible and wearable patch. The alignment of nanoparticles has been achieved using demagnetization fields with computational simulations confirming device characteristics under various types of deformation followed by experimental demonstrations. The device exhibits favorable piezoelectric performance, hydrophobicity, and body motion-sensing capabilities, as well as machine learning-powered touch-sensing/actuating features.

Semantic segmentation and stereo matching are two essential components of 3D environmental perception systems for autonomous driving. Nevertheless, conventional approaches often address these two problems independently, employing separate models for each task. This approach poses practical limitations in real-world scenarios, particularly when computational resources are scarce or real-time performance is imperative. Hence, in this article, we introduce S$^{3}$M-Net, a novel joint learning framework developed to perform semantic segmentation and stereo matching simultaneously. Specifically, S$^{3}$M-Net shares the features extracted from RGB images between both tasks, resulting in an improved overall scene understanding capability. This feature sharing process is realized using a feature fusion adaption (FFA) module, which effectively transforms the shared features into semantic space and subsequently fuses them with the encoded disparity features. The entire joint learning framework is trained by minimizing a novel semantic consistency-guided (SCG) loss, which places emphasis on the structural consistency in both tasks. Extensive experimental results conducted on the vKITTI2 and KITTI datasets demonstrate the effectiveness of our proposed joint learning framework and its superior performance compared to other state-of-the-art single-task networks. Our project webpage is accessible at mias.group/S3M-Net.

Artemisia annuais an anti-fever herbal medicine first described in traditional Chinese medicine 1,000 years ago. Artemisinin, the extract of A. annua, and its derivatives (dihydroartemisinin (DHA), artemether, and artesunate) have been used for the treatment of malaria with substantial efficacy. Recently, DHA has also been tested for the treatment of lupus erythematosus, indicating that it may function to balance the immune response in immunocompromised individuals. In the present study, the regulatory effect of artemisinin on the murine immune system was systematically investigated in mice infected with two different protozoan parasites (Toxoplasma gondiiand Plasmodium berghei). Our results revealed that the mouse spleen index significantly increased (spleen enlargement) in the healthy mice after DHA administration primarily due to the generation of an extra number of lymphocytes and CD8+T lymphocytes in both the spleen and circulation. DHA could increase the proportion of T helper cells and CD8+T cells, as well as decrease the number of splenic and circulatory B cells. Further, DHA could reduce the production of proinflammatory cytokines. Our study revealed that apart from their anti-parasitic activity, artemisinin and its derivatives can also actively modulate the immune system to directly benefit the host.

Optical flow and disparity are two informative visual features for autonomous driving perception. They have been used for a variety of applications, such as obstacle and lane detection. The concept of “U-V-Disparity” has been widely explored in the literature, while its counterpart in optical flow has received relatively little attention. Traditional motion analysis algorithms estimate optical flow by matching correspondences between two successive video frames, which limits the full utilization of environmental information and geometric constraints. Therefore, we propose a novel strategy to model optical flow in the collision-freespace (also referred to as drivable area or simply freespace) for intelligent vehicles, with the full utilization of geometry information in a 3-D driving environment. We provide explicit representations of optical flow and deduce the quadratic relationship between the optical flow component and the vertical coordinate. Through extensive experiments on several public datasets, we demonstrate the high accuracy and robustness of our model. In addition, our proposed freespace optical flow model boasts a diverse array of applications within the realm of automated driving, providing a geometric constraint in freespace detection, vehicle localization, and more. We have made our source code publicly available at https://mias.group/FSOF.

As a safety-critical application, autonomous driving requires high-quality semantic segmentation and real-time performance for deployment. Existing method commonly suffers from information loss and massive computational burden due to high-resolution input-output and multiscale learning scheme, which runs counter to the real-time requirements. In contrast to channelwise information modeling commonly adopted by modern networks, in this article, we propose a novel real-time driving scene parsing framework named NDNet from a novel perspective of spacewise neighbor decoupling (ND) and neighbor coupling (NC). We first define and implement the reversible operations called ND and NC, which realize lossless resolution conversion for complementary thumbnails sampling and collation to facilitate spatial modeling. Based on ND and NC, we further propose three modules, namely, local capturer and global dependence builder (LCGB), spacewise multiscale feature extractor (SMFE), and high-resolution semantic generator (HSG), which form the whole pipeline of NDNet. The LCGB serves as a stem block to preprocess the large-scale input for fast but lossless resolution reduction and extract initial features with global context. Then the SMFE is used for dense feature extraction and can obtain rich multiscale features in spatial dimension with less computational overhead. As for high-resolution semantic output, the HSG is designed for fast resolution reconstruction and adaptive semantic confusion amending. Experiments show the superiority of the proposed method. NDNet achieves the state-of-the-art performance on the Cityscapes dataset which reports 76.47% mIoU at 240+ frames/s and 78.8% mIoU at 150+ frames/s on the benchmark. Codes are available at https://github.com/LiShuTJ/NDNet.

Accurate acquisition of 3-D human joint poses holds significant implications for tasks such as human action recognition. Monocular single-frame 2-D -to-3-D pose estimation focuses on establishing the correspondence between 2-D human pose in a single image and their 3-D spatial pose, delegating the preliminary task of 2-D pose estimation to models better suited for processing pixel information. The intricacy of 2-D -to-3-D pose estimation resides in modeling the spatial constraints among joints. To better learn the structure between joints, this article proposes the SPGformer algorithm, constructed with stacked serial–parallel GCN-encoder (SPGEncoder) modules. This module forms a dual-branch framework composed of transformer encoders (Encoders) and graph-oriented encoders (GraEncoders). We recover concealed depth values from the 2-D coordinates of joints, inputting them into the joint branch of the SPGEncoder. In parallel, we take the connection features of joints in the image as vector branch input. The proposed GraEncoder module integrates a learnable graph convolutional network (GCN) prior to the Encoder, enabling the learning of a broader spectrum of joint connections within the confines of skeletal linkage. Furthermore, this article presents a methodology for calculating the 3-D absolute pose of the root node, filling a research gap for applications requiring precise human position. This nonlearnable, plug-and-play method has been validated on the Human3.6M dataset. The SPGformer algorithm outperforms state-of-the-art methods on both the Human3.6M and MPI-INF-3DHP datasets.

The flexibility of simultaneous localization and mapping (SLAM) algorithms in various environments has consistently been a significant challenge. To address the issue of LiDAR odometry drift in high-noise settings, integrating clustering methods to filter out unstable features has become an effective module of SLAM frameworks. However, reducing the amount of point cloud data can lead to potential loss of information and possible degeneration. As a result, this research proposes a LiDAR odometry that can dynamically assess the point cloud’s reliability. The algorithm aims to improve adaptability in diverse settings by selecting important feature points with sensitivity to the level of environmental degeneration. First, a fast adaptive Euclidean clustering algorithm based on range image is proposed, which, combined with depth clustering, extracts the primary structural points of the environment defined as ambient skeleton points. Then, the environmental degeneration level is computed through the dense normal features of the skeleton points, and the point cloud cleaning is dynamically adjusted accordingly. The algorithm is validated on the KITTI benchmark and real environments, demonstrating higher accuracy and robustness in different environments.

The sparsity of plane point clouds remains human detection based on 2-D LiDAR a challenging task. Recent deep-learning-based methods have made significant progress, but their unidimensional feature modeling hinders the flow of information across different axes (i.e., temporal, spatial, and batched). Besides, convolution operators limit the effective receptive field (ERF) of the model. To promote multidimensional interaction, we propose an omni-dimensional aggregation (ODA) module composed of a main aggregation stream and an auxiliary feature stream to assist in the temporal-spatial joint encoding as well as implicitly models the potential relationships between samples. By cascading multiple ODA blocks, the semantic gaps between different features are gradually bridged. In addition, to improve network convergence and generalization, an adaptive focus mechanism (AFM) is designed to guide the model to optimize more on difficult samples instead of simple or low-quality samples. Extensive experiments demonstrate the effectiveness of the proposed pipeline, achieving 76.4% and 81.4% AP on the DROW and JRDB benchmark datasets, respectively, surpassing the existing state-of-the-art (SOTA) results. Competitive results are also achieved in inference speed. Furthermore, we successfully validate the proposed model through real-world environments. Our code is available at https://github.com/ai-winter/Li2Former-v1.

Recently, thanks to the introduction of human feedback, Chat Generative Pre-trained Transformer (ChatGPT) has achieved remarkable success in the language processing field. Analogically, human drivers are expected to have great potential in improving the performance of autonomous driving under real-world traffic. Therefore, this study proposes a novel framework for evolutionary decision-making and planning by developing a hybrid augmented intelligence (HAI) method to introduce human feedback into the learning process. In the framework, a decision-making scheme based on interactive reinforcement learning (Int-RL) is first developed. Specifically, a human driver evaluates the learning level of the ego vehicle in real-time and intervenes to assist the learning of the vehicle with a conditional sampling mechanism, which encourages the vehicle to pursue human preferences and punishes the bad experience of conflicts with the human. Then, the longitudinal and lateral motion planning tasks are performed utilizing model predictive control (MPC), respectively. The multiple constraints from the vehicle’s physical limitation and driving task requirements are elaborated. Finally, a safety guarantee mechanism is proposed to ensure the safety of the HAI system. Specifically, a safe driving envelope is established, and a safe exploration/exploitation logic based on the trial-and-error on the desired decision is designed. Simulation with a high-fidelity vehicle model is conducted, and results show the proposed framework can realize an efficient, reliable, and safe evolution to pursue higher traffic efficiency of the ego vehicle in both multi-lane and congested ramp scenarios.

Existing end-to-end depth representation in embodied AI is often task-specific and lacks the benefits of emerging pre-training paradigm due to limited datasets and training techniques for RGB-D videos. To address the challenge of obtaining robust and generalized depth representation for embodied AI, we introduce a unified RGB-D video dataset (UniRGBD) and a novel time-aware contrastive (TAC) pre-training approach. UniRGBD addresses the scarcity of large-scale depth pre-training datasets by providing a comprehensive collection of data from diverse sources in a unified format, enabling convenient data loading and accommodating various data domains. We also design an RGB-Depth alignment evaluation procedure and introduce a novel Near-K accuracy metric to assess the scene understanding capability of the depth encoder. Then, the TAC pre-training approach fills the gap in depth pre-training methods suitable for RGB-D videos by leveraging the intrinsic similarity between temporally proximate frames. TAC incorporates a soft label design that acts as valid label noise, enhancing the depth semantic extraction and promoting diverse and generalized knowledge acquisition. Furthermore, the adjustments in perspective between temporally proximate frames facilitate the extraction of invariant and comprehensive features, enhancing the robustness of the learned depth representation. Additionally, the inclusion of temporal information stabilizes training gradients and enables spatio-temporal depth perception. Comprehensive evaluation of RGB-Depth alignment demonstrates the superiority of our approach over state-of-the-art methods. We also conduct uncertainty analysis and a novel zero-shot experiment to validate the robustness and generalization of the TAC approach. Moreover, our TAC pre-training demonstrates significant performance improvements in various embodied AI tasks, providing compelling evidence of its efficacy across diverse domains.

The biology of Toxoplasma gondii, the causative pathogen of one of the most widespread parasitic diseases (toxoplasmosis), remains poorly understood. Lactate, which is derived from glucose metabolism, is not only an energy source in a variety of organisms,including T. gondii, but also a regulatory molecule that participates in gene activation and protein function. Lysine lactylation(Kla) is a type of post-translational modifications (PTMs) that has been recently associated with chromatin remodeling; however, Kla of histone and non-histone proteins has not yet been studied in T. gondii. To examine the prevalence and function of lactylation in T. gondiiparasites, we mapped the lactylome of proliferating tachyzoite cells and identified 1964 Kla sites on 955 proteins in the T. gondiiRH strain. Lactylated proteins were distributed in multiple subcellular compartments and were closely related to a wide variety of biological processes, including mRNA splicing, glycolysis, aminoacyl-tRNA biosynthesis, RNA transport,and many signaling pathways. We also performed a chromatin immunoprecipitation sequencing (ChIP-seq) analysis using a lactylation-specific antibody and found that the histones H4K12la and H3K14la were enriched in the promoter and exon regions of T. gondiiassociated with microtubule-based movement and cell invasion. We further confirmed the delactylase activity of histone deacetylases TgHDAC2–4, and found that treatment with anti-histone acetyltransferase (TgMYST-A) antibodies profoundly reduced protein lactylation in T. gondii. This study offers the first dataset of the global lactylation proteome and provides a basis for further dissecting the functional biology of T. gondii.

As a critical part of the 3D human pose estimation (HPE), establishing the 2D-to-3D lifting mapping is limited by depth ambiguity. Most current works generally lack the quantitative analysis of the relative depth expression and the depth ambiguity error expression in lifting mapping, resulting in low prediction efficiency and poor interpretability. To this end, this paper mines and leverages prior geometric knowledge of these expressions based on the pinhole imaging principle, decoupling the 2D-to-3D lifting mapping and simplifying the model training. Specifically, this paper proposes a prior geometric knowledge oriented pose estimation model with two-branch transformer architectures, explicitly introducing high-dimensional prior geometric features to improve model efficiency and interpretability. It converts the regression of spatial coordinates into the prediction of spatial direction vectors between joints to generate multiple feasible solutions further alleviate the depth ambiguity. Moreover, this paper raises a novel non-learning-based absolute depth estimation algorithm based on prior geometric relationship decoupling from relative depth expression for the first time. It establishes multiple independent depth mapping from non-root nodes to the root node to calculate the absolute depth candidate, which is parameter-free, plug-and-play, and interpretable. Experiments show that the proposed pose estimation model achieves state-of-the-art performance on Human 3.6M and MPI-INF-3DHP benchmarks with lower parameters and faster inference speed, and the proposed absolute depth estimation algorithm achieves similar performance to traditional methods without any network parameters. The source code are available at https://github.com/Humengxian/GKONet.

With high-definition sensors and sophisticated machine vision algorithms, the visual perception capability of autonomous vehicle (AV) has largely advanced. However, the visual perception performance of AVs may still be unstable in complex traffic environment. To improve the robustness and capability of risk detection of AV visual perception system, this work proposes a framework to fuse human gaze and the object detection results from vehicle vision based on the Laplacian Pyramid algorithm. We evaluate the proposed method on a level-2 AV to perceive the interactive vehicles at unsignalized intersections. Using Extended Kalman Filter, the trajectory of the human drivers’ gaze and the anchor boxes from AV object detection are fused. Results reveal that with human-vehicle visual fusion, the actual trajectory of interactive vehicles can be predicted more accurately than separately using human gaze or object detection algorithm. The findings show that human-vehicle visual fusion improves the perception accuracy and robustness of interactive objects in complex traffic environment. The method has the potential to enhance the attention mechanism of AV vision.

Predicting 6-D object pose is an essential task in vision measurement for robotic manipulation. RGB-based methods have a natural disadvantage due to the lack of 3-D information, thus leading to inferior results. Therefore, exploiting the geometry information in depth images is crucial to achieve accurate predictions. To this end, we propose a multiscale point cloud transformer (MSPCT) to better learn point cloud feature representations. MSPCT mainly consists of three types of modules: local transformer (LT), DownSampling (DS) module, and global transformer (GT). Specifically, the LT is designed to dynamically divide a local region centered on each point and further extract point-level feature with local context awareness. The DS module is utilized to decrease the resolution and enlarge the receptive field. GT is employed to capture global-range dependencies between extracted local features. Based on the proposed transformer blocks, we design a network architecture for object pose estimation, where we further obtain multiscale features by fusing the local features from LT and global features from GT to predict object’s pose. Extensive experiments verify the effectiveness of LT and GT, and our pose estimation pipeline achieves promising results on three benchmark datasets.

It is a rather practical but difficult task to find a specified target object via autonomous exploration based on natural language descriptions in an unstructured environment. Since the human-annotated data is expensive to gather for the goal-oriented vision-language navigation (GVLN) task, the size of the standard dataset is inadequate, which has significantly limited the accuracy of previous techniques. In this work, we aim to improve the robustness and generalization of the navigator by dynamically providing high-quality pseudo-instructions using a proposed RES-StS paradigm. Specifically, we establish a referring expression speaker (RES) to predict descriptive instructions for the given path to the goal object. Based on an environment-and-object fusion (EOF) module, RES derives spatial representations from the input trajectories, which are subsequently encoded by a number of transformer layers. Additionally, given that the quality of the pseudo labels is important for data augmentation while the limited dataset may also hinder RES learning, we propose to equip RES with a more effective generation ability by using the self-training approach. A trajectory-instruction matching scorer (TIMS) network based on contrastive learning is proposed to selectively use rehearsal of prior knowledge. Finally, all network modules in the system are integrated by suggesting a multi-stage training strategy, allowing them to assist one another and thus enhance performance on the GVLN task. Experimental results demonstrate the effectiveness of our approach. Compared with the SOTA methods, our method improves SR, SPL, and RGS by 4.72%, 2.55%, and 3.45% respectively, on the REVERIE dataset, and 4.58%, 3.75% and 3.14% respectively, on the SOON dataset.

Degeneracy detection and pose estimation of mobile robots have long been difficult task for laser scan matching techniques. Since the information perceived by lidar is underconstrained or deficient in degeneration scenes such as long corridors and lobbies, it is challenging for common hardware-limited lidar odometry to operate safely and stably. In this article, we propose a novel degeneration-aware correlative scan matching (CSM) algorithm for such situations. First, we carry out a low-dependency degeneration scene detection and analysis approach to determine the current degeneration situation accurately and instantly. Then, an environmentally sensitive CSM approach is designed based on real-time results, which dynamically adjusts the motion weight to achieve the autonomous adaptability to a variety of environments. Extensive experiments on both datasets and real-world scenarios demonstrate the superiority of the proposed algorithm in both degeneration scene detection and lidar odometry. In comparison to the CSM algorithm, the proposed algorithm not only minimizes the need of manually weights setup under dynamic environments but also ameliorates the accuracy of lidar odometry.

Existing real-time semantic segmentation models usually pursue lower inference costs by compressing spatial resolution, reducing the number of feature extraction layers, or simplifying the high-resolution generation process. This inevitably leads to huge information loss and ultimately affects segmentation accuracy. To address the problem, a HoloParser is proposed to learn and parse the input image from a holistic perspective of the entire segmentation pipeline. First, in the image preprocessing part, the often-overlooked problem of structural information loss in fast downsampling is highlighted and studied. For this, a nonstrided fast downsampling (NFD) module is devised for holo-structure retention. Second, for the resolution change processes in segmentation networks, the spatial correlation between up/down-samplings is revisited and focused. And a lossless sample pair (LSP) is proposed for holo-correlation modeling and retrieving. Third, in feature extraction, after reconsidering the necessity and redundancy of multiscale learning, a distributed pyramid learning (DPL) module is proposed for lightweight holo-feature extraction with composite receptive fields (RFs). Finally, for the high-resolution output generation, a parallel resolution maintenance (PRM) module is put forward via high-low semantics aggregation across stages for holo-semantics utilization. Extensive experiments demonstrate the effectiveness and superiority of the proposed modules. The HoloParser achieves state-of-the-art performance in real-time semantic segmentation, which reports 76.45% mIoU at 280+ frames/s on the Cityscapes dataset.

This article describes an approach for extracting multiple planar regions in 3-D point clouds from spinning multibeam LiDARs. This technique benefits from the intrinsic structure of LiDARs and projective geometry, which allows us to extract line segments efficiently in 2-D space and then cluster those line segments to form planes. To extract planes from line primitives, we introduce a novel line segment grouping approach by alternatively searching candidate plane seeds of adjacent line segments and breadth-first searching for neighboring lines fallen on the seeded plane. Exhaustive experiments have been conducted with simulation, realistic data, and a public plane segmentation evaluation benchmark. Experimental results show that our method works well on sparse point clouds with the fastest running speed compared to state-of-the-art methods.

Autonomous driving necessitates ensuring safety across diverse environments, particularly in challenging conditions like low-light or nighttime scenarios. As a fundamental task in autonomous driving, monocular depth estimation has garnered significant attention and discussion. However, current monocular depth estimation methods primarily rely on daytime images, which limits their applicability to nighttime scenarios due to the substantial domain shift between daytime and nighttime styles. In this article, we propose a novel Day-Night Adaptation method (DNA-Depth) to realize monocular depth estimation in a night environment. Specifically, we simply use Fourier Transform to address the domain alignment problem. Our method does not require extra adversarial optimization but is quite effective. The simplicity of our method makes it easy to guide day-to-night domains. To the best of our knowledge, we are the first to utilize fast Fourier transformation for nighttime monocular depth estimation. Furthermore, to alleviate the problem of mobile light sources, we utilize an unsupervised joint learning framework for depth, optical flow, and ego-motion estimation in an end-to-end manner, which is coupled by 3-D geometry cues. Our model can simultaneously reason about the camera motion, the depth of a static background, and the optical flow of moving objects. Extensive experiments on the Oxford RobotCar, nuScenes, and Synthia datasets demonstrate the accuracy and precision of our method by comparing it with those state-of-the-art algorithms in depth estimation, both qualitatively and quantitatively.

During the process of high-speed railway online maintenance, anomaly detection of targets in grease injection facilities is extremely challenging. Environmental uncertainty (noise disturbance, various illumination conditions, changeable camera perspective, etc.) limits the performance of existing unsupervised anomaly detection methods, which tend to struggle in such complex backgrounds without prior knowledge of anomalies. Therefore, we propose a novel semi-supervised anomaly detection algorithm that only learns from anomaly-free samples and annotations while can recognize both anomaly-free and anomaly targets (key areas) and detecting anomalous targets simultaneously. We first propose a key-area recognition module (KRM) to segment anomaly-free targets and predict coordinates of both anomaly-free and anomalous key areas’ center points (key points). By using the predicted coordinates, we generated a circular mask to erase features within the key areas. Based on the erased features, a local feature reconstruction and segmentation module (LFRSM) is proposed to restore the latent embeddings within the key areas and segment both anomaly-free and anomalous key areas. The final anomaly results are obtained by analyzing the original segmentation results of KRM and the reconstructed segmentation results of LFRSM. To validate the effectiveness of the proposed algorithm, we develop a dataset, called BOLT-DET, for bolt anomaly detection in complex scenes. The proposed algorithm outperforms other existing state-of-the-art (SOTA) anomaly detection algorithms (SPADE, DFR, CFLOW, Student-Teacher, PatchCore, and DRAEM) with mean intersection over union (mIoU) of 0.66, area under the receiver operating characteristic curve (AUROC) of 0.92, and F1-Measure of 0.69 on BOLT-DET. It can accurately detect anomalies and meet the requirement of practical industrial applications.

A correction to this paper has been published: https://doi.org/10.1007/s13238-021-00833-y

The immunomodulatory potential of dihydroartemisinin (DHA) has recently been highlighted; however, the potential mechanism remains to be clarified. Single-cell RNA sequencing was explored in combination with cellular and biochemical approaches to elucidate the immunomodulatory mechanisms of DHA. In this study, we found that DHA induced both spleen enlargement and rearrangement of splenic immune cell subsets in mice. It was revealed that DHA promoted the reversible expansion of effective regulatory T cells and interferon-γ+cytotoxic CD8+T cells in the spleen via induction of superoxide dismutase 3 (SOD3) expression and increased phosphorylation of c-Jun N-terminal kinases (JNK) and its downstream activator protein 1 (AP-1) transcription factors. Further, SOD3 knockout mice were resistant to the regulatory effect of DHA. Thus, DHA, through the activation of the SOD3-JNK-AP-1 axis, beneficially regulated immune cell heterogeneity and splenic immune cell homeostasis to treat autoimmune diseases.

This article investigates weak diagnosability of discrete-event systems. Although diagnosability has been extensively investigated in the literature, weak diagnosability is a new concept introduced in this article. While diagnosability requires that a fault can be diagnosed along all trajectories after its occurrence, weak diagnosability requires that there always exists at least one trajectory along which the fault can be diagnosed at any time after its occurrence. Weak diagnosability ensures that any fault can be diagnosed eventually (with probability 1) but requires less sensors. We develop algorithms to check weak diagnosability. We further investigate weak diagnosability under communication delays and packet losses. We show that while packet losses may impact negatively on weak diagnosability, communication delays will not. We illustrate the results using a practical example.

It is desired to develop self-healing gel electrolytes for flexible electrochromic devices (ECDs) due to the demand of healing damages caused during operations. We here report a hydrogel electrolyte with remarkable self-healing capability, excellent stretchability, and ionic conductivity. The hydrogel electrolyte was synthesized via one-step copolymerization of glycerol monomethacrylate (GMA) and acrylamide (AAm) in the presence of borate. Within the hydrogel electrolyte, dynamic cross-linking is expected to be formed due to the borate-didiol complexation and hydrogen-bonding interactions. As a result, the hydrogel electrolyte demonstrates an excellent self-healing efficiency of up to 97%, a fracture strain of 1155%, a fracture toughness of 136.6 kJ m–3, and a fracture stress of 13.0 kPa. Additionally, a flexible ECD based on the hydrogel electrolyte and an electrochromic layer of poly(3,4-(2,2-dimethyl-propylenedioxy)thiophene) (PProDOT-Me2) was assembled and evaluated. The device is found to be stable in both mechanical and optical properties over 1000 operation cycles. This study may provide a promising way for self-healing electrolyte gels to be utilized in a variety of flexible electrochemical devices, including ECDs, supercapacitors, and batteries.

Metabolic regulation has been proven to play a critical role in T cell antitumor immunity. However, cholesterol metabolism as a key component of this regulation remains largely unexplored. Herein, we found that the low-density lipoprotein receptor (LDLR), which has been previously identified as a transporter for cholesterol, plays a pivotal role in regulating CD8+T cell antitumor activity. Besides the involvement of cholesterol uptake which is mediated by LDLR in T cell priming and clonal expansion, we also found a non-canonical function of LDLR in CD8+T cells: LDLR interacts with the T-cell receptor (TCR) complex and regulates TCR recycling and signaling, thus facilitating the effector function of cytotoxic T-lymphocytes (CTLs). Furthermore, we found that the tumor microenvironment (TME) downregulates CD8+T cell LDLR level and TCR signaling via tumor cell-derived proprotein convertase subtilisin/kexin type 9 (PCSK9) which binds to LDLR and prevents the recycling of LDLR and TCR to the plasma membrane thus inhibits the effector function of CTLs. Moreover, genetic deletion or pharmacological inhibition of PCSK9 in tumor cells can enhance the antitumor activity of CD8+T cells by alleviating the suppressive effect on CD8+T cells and consequently inhibit tumor progression. While previously established as a hypercholesterolemia target, this study highlights PCSK9/LDLR as a potential target for cancer immunotherapy as well.

Malaria is one of the deadliest infectious diseases threatening half of the world population. With the deterioration of the parasiticidal effect of the current antimalarials, novel approaches such as screening of more specific inhibitors and targeted delivery of drugs have been under intensive research. Herein, we prepare hollow mesoporous ferrite nanoparticles (HMFNs) of 200 nm with ferromagnetic properties using a one-pot hydrothermal reaction. A magnetically targeted drug-delivery system coloaded with artemisinin in the inner magnetite shell and heparin on the outer mesoporous shell (HMFN@ART@HEP) is developed. Specific targeting of the magnetic nanoparticles to the parasite-infected erythrocytes is achieved by the attraction between the HMFNs and hemozoin (paramagnetic), a vital metabolite of plasmodium in the erythrocytic stage. With the hemozoin production reaching the maximum during the schizont period of the parasite, HMFN@ART@HEPs are adsorbed to the infected red blood cells (iRBCs), which not only interferes with the release of merozoites but also significantly enhances the inhibitory efficacy due to the increased local concentration of artemisinin. Subsequently, the heparin coated on the surface of the nanoparticles can efficiently interfere with the invasion of freshly released merozoites to new RBCs through the specific interaction between the parasite-derived ligands and heparin, which further increases the inhibitory effect on malaria. As a cluster of heparin, heparin-coated nanoparticles provide stronger blocking capability than free heparin, resulting from multivalent interactions with surface receptors on merozoite. Thus, we have developed a HMFN-based delivery system with considerable antimalarial efficacy, which is a promising platform for treatment against malaria.

Toxoplasma gondii is an obligate intracellular protozoan parasite, which can infect almost all warm-blooded animals, including humans, leading to toxoplasmosis. Currently, the effective treatment for human toxoplasmosis is the combination of sulphadiazine and pyrimethamine. However, both drugs have serious side-effects and toxicity in the host. Therefore, there is an urgent need for the discovery of new anti-T. gondii drugs with high potency and less or no side-effects. Our findings suggest that lumefantrine exerts activity against T. gondii by inhibiting its proliferation in Vero cells in vitro without being toxic to Vero cells (P ≤ 0.01). Lumefantrine prolonged mice infected with T. gondii from death for 3 days at the concentration of 50 μg L−1 than negative control (phosphate-buffered saline treated only), and reduced the parasite burden in mouse tissues in vivo (P ≤ 0.01; P ≤ 0.05). In addition, a significant increase in interferon gamma (IFN-γ) production was observed in high-dose lumefantrine-treated mice (P ≤ 0.01), whereas interleukin 10 (IL-10) and IL-4 levels increased in low-dose lumefantrine-treated mice (P ≤ 0.01). The results demonstrated that lumefantrine may be a promising agent to treat toxoplasmosis, and more experiments on the protective mechanism of lumefantrine should be undertaken in further studies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a weaker property for failure diagnosis called weak diagnosability. Weak diagnosability says that, after the occurrence of a fault event, for any continuation, there always exists at least one trajectory of sufficient length along which we can diagnose the occurrence of the fault event. We derive a necessary and sufficient condition for weak diagnosability and develop an algorithm to check the condition. We then extend weak diagnosability to networked discrete event systems which have packet losses and communication delays and call the extension weak network diagnosability. We adopt the transformed automaton technique to deal with packet losses and develop a method for checking weak network diagnosability with respect to packet losses. For communication delays, our results show that they do not change weak network diagnosability when delays are first-in-first-out, but may add time to diagnose the occurrences of fault events.

A foot positioning compensator is developed in this paper for a full-body humanoid to retrieve its balance during continuous walking. An online Foot Position Compensator (FPC) is designed to improve the robustness of biped walking, which can modify predefined step position and step duration online with sensory feedback. Foot placement parameters are learned by the FPC based on the Policy Gradient Reinforcement Learning (PGRL) method. Moreover, the FPC assists the humanoid robot in rejecting external disturbances and recovering the walking position by re-planning the trajectories of walking pattern and the Center of Mass (CoM). An upper body pose control strategy is also presented to further enhance the performance of humanoid robots to overcome strong external disturbances. The advantages of this proposed method are that it neither requires prior information about the walking terrain conditions, nor relies on range sensor information for surface topology measurement. The effectiveness of the proposed method is verified viaWebots simulation and real experiments on a full-body humanoid NAO robot.

A 32-year-old virgin presented with psychosexual dysfunction and a mass in the vulva. The mass arose from the cervix with a thin pedicle and it was 6 × 1.5 cm. The mass protruded outside of the hymen, it had a canal inside and an opening at the distal end, and it had a genital-like appearance. A mucosal fold was found in the tubular lumen. Fibrous smooth muscle was observed in the tubal wall, which was covered by squamous and endometrial glandular epithelium on each side. A cervical giant polyp with a canal inside the polyp might be differentially diagnosed from prolapsed genitals and abnormal sexual development. Polypectomy by mini-hysteroscopy was effective for alleviating psychosexual dysfunction caused by the mass in this patient.

The proteomes of the extracellular stage of the two phenotypically different Toxoplasma gondiistrains, RH strain and ME49 strain, were systematically analyzed using an LC-MS/MS approach, and the global lysine crotonylation and 2-hydroxyisobutyrylation of the two parasite strains were deeply investigated. The global maps of lysine crotonylation and 2-hydroxyisobutyrylation of the proteomes of the two parasite strains will provide a valuable resource to facilitate the illumination of the biology of the zoonotic T. gondiiparasite.

Trypanosoma evansiis the causative agent of the animal trypanosomiasis surra, a disease with serious economic burden worldwide. The availability of the genome of its closely related parasite Trypanosoma bruceiallows us to compare their genetic and evolutionarily shared and distinct biological features. The complete genomic sequence of the T. evansiYNB strain was obtained using a combination of genomic and transcriptomic sequencing, de novoassembly, and bioinformatic analysis. The genome size of the T. evansiYNB strain was 35.2 Mb, showing 96.59% similarity in sequence and 88.97% in scaffold alignment with T. brucei. A total of 8,617 protein-coding genes, accounting for 31% of the genome, were predicted. Approximately 1,641 alternative splicing events of 820 genes were identified, with a majority mediated by intron retention, which represented a major difference in post-transcriptional regulation between T. evansiand T. brucei. Disparities in gene copy number of the variant surface glycoprotein, expression site-associated genes, microRNAs, and RNA-binding protein were clearly observed between the two parasites. The results revealed the genomic determinants of T. evansi, which encoded specific biological characteristics that distinguished them from other related trypanosome species.

This work concerns biped adaptive walking control on slope terrains with online trajectory generation. In terms of the lack of satisfactory performances of the traditional simplified single-layered Central Pattern Generator (CPG) model in engineering applications where robots face unknown environments and access feedback, this paper presents a Multi-Layered CPG (ML-CPG) model based on a half-center CPG model. The proposed ML-CPG model is used as the underlying low-level controller for a quadruped robot to generate adaptive walking patterns. Rhythm-generation and pattern formation interneurons are modeled to promptly generate motion rhythm and patterns for motion sequence control, while motoneurons are modeled to control the output strength of the joint in real time according to feedback. Referring to the motion control mechanisms of animals, a control structure is built for a quadruped robot. Multi-sensor models abstracted from the neural reflexes of animals are involved in all the layers of neurons through various feedback paths to achieve adaptability as well as the coordinated motion control of a robot’s limbs. The simulation experiments verify the effectiveness of the presented ML-CPG and multi-layered reflexes strategy.

Antibody therapeutics, though representing a most used biomedicine, suffers from poor in vivo stability, rapid degradation, and frequent injections. Here, we report that fluorescent hyaluronic acid microgels (HMGs) tailor-made by combining microfluidics and “tetrazole–alkene” photoclick chemistry enable sustained and localized delivery of Herceptin in ovarian tumors. HMGs were obtained with a defined size (25–50 μm), narrow size distribution, high stability, and strong green fluorescence. Notably, HMGs exhibited a remarkably high loading of proteins such as Herceptin and IgG with a loading efficiency exceeding 90% at a theoretical protein-loading content of 30 wt %. In vitro protein release experiments revealed a sustained and hyaluronidase (HAase)-dependent release of Herceptin from HMGs, in which 80.6% of Herceptin was released at 1 U/mL HAase in 10 days. The released Herceptin maintained its secondary structure and antitumor activity. In vivo imaging results demonstrated obviously better tumoral retention for Cy5-labeled Herceptin-loaded HMGs following subcutaneous (sc) injection than for the free-protein counterpart. Interestingly, sc injection of the Herceptin-loaded HMGs into SKOV-3 human ovarian tumor-bearing nude mice at a dose of 30 mg Herceptin equiv/kg induced nearly complete tumor suppression, which was significantly more effective than the sc or systemic injection of free Herceptin. These tailor-made fluorescent HMGs appeared as a robust injectable platform for sustained and localized delivery of therapeutic proteins.

Protein drugs with intracellular targets like Granzyme B (GrB) have demonstrated great proliferative inhibition activity in cancer cells. Their clinical translation, however, relies on the development of safe, efficient, and selective protein-delivery vehicles. Here, we report that epidermal growth factor receptor (EGFR) and CD44 dual-targeted multifunctional hyaluronic acid nanogels (EGFR/CD44-NGs) boost protein delivery to ovarian and breast cancers in vitro and in vivo. EGFR/CD44-NGs obtained via nanoprecipitation and photoclick chemistry from hyaluronic acid derivatives with tetrazole, GE11 peptide/tetrazole, and cystamine methacrylate groups had nearly quantitative loading of therapeutic proteins like cytochrome C (CC) and GrB, a small size of ca. 165 nm, excellent stability in serum, and fast protein release under a reductive condition. Flow cytometry assays showed that EGFR/CD44-NGs exhibited over 6-fold better uptake in CD44 and EGFR-positive SKOV-3 ovarian cancer cells than CD44-NGs. In accordance, GrB-loaded EGFR/CD44-NGs (GrB-EGFR/CD44-NGs) displayed enhanced caspase activity and growth inhibition in SKOV-3 cells as compared to GrB-loaded CD44-NGs (GrB-CD44-NGs) control. Intriguingly, the therapeutic studies in SKOV-3 human ovarian carcinoma and MDA-MB-231 human breast tumor xenografted in nude mice revealed that GrB-EGFR/CD44-NGs at a low dose of 3.85 nmol GrB equiv/kg induced nearly complete growth suppression of both tumors, which was obviously more effective than GrB-CD44-NGs, without causing any adverse effects. EGFR and CD44 dual-targeted multifunctional hyaluronic acid nanogels have appeared as a safe and efficacious platform for cancer protein therapy.

This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass (CoM) online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the generated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on irregular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.

Patients with dyskeratosis congenita (DC) suffer from stem cell failure in highly proliferative tissues, including the intestinal epithelium. Few therapeutic options exist for this disorder, and patients are treated primarily with bone marrow transplantation to restore hematopoietic function. Here, we generate isogenic DC patient and disease allele-corrected intestinal tissue using clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-mediated gene correction in induced pluripotent stem cells and directed differentiation. We show that DC tissue has suboptimal Wnt pathway activity causing intestinal stem cell failure and that enhanced expression of the telomere-capping protein TRF2, a Wnt target gene, can alleviate DC phenotypes. Treatment with the clinically relevant Wnt agonists LiCl or CHIR99021 restored TRF2 expression and reversed gastrointestinal DC phenotypes, including organoid formation in vitro, and maturation of intestinal tissue and xenografted organoids in vivo. Thus, the isogenic DC cell model provides a platform for therapeutic discovery and identifies Wnt modulation as a potential strategy for treatment of DC patients.

The digestive systems of mammals harbor a complex gut microbiome, comprising bacteria and other microorganisms that confer metabolic and immunological benefits to the host. Ruminants that digest plant-based foods have a four-compartment stomach consisting of the rumen, reticulum, omasum, and abomasum. The microorganisms in the stomach are essential for providing the host with critical nutrients. However, the majority of these microorganisms are unknown species. The microbiome of the stomach is diverse, and the majority of these organisms cannot be cultured. Next-generation sequencing (NGS) combined with bioinformatic analysis tools have allowed the dissection of the composition of the microbiome in samples collected from a specific environment. In this study, for the first time, the bacterial composition in two compartments, the reticulum and the omasum, of bovine were analyzed using a metagenomic approach and compared to the bacterial composition of the rumen. These data will assist in understanding the biology of ruminants and benefit the agricultural industry. The diversity and composition of the bacterial community in samples collected from the rumen, reticulum, and omasum of bovines in the Changchun Region of Northeast China were analyzed by sequencing the V3 region of the 16S rRNA gene using a barcoded Illumina paired-end sequencing technique, and the primary composition of the microbiome in the rumen, reticulum, and omasum of the bovines was determined. These microbiomes contained 17 phyla and 107 genera in all three samples. Five phyla, Bacteroidetes, Firmicutes, Proteobacteria, Spirochaetes, and Lentisphaerae, were the most abundant taxonomic groups. Additionally, the different stomach compartments harbored different compositions of the microorganisms.

ABSTRACTT cell immunoglobulin- and mucin-domain-containing molecule 3 (Tim-3) has been regarded as an important regulatory factor in both adaptive and innate immunity. Recently, Tim-3 was reported to be involved in Th2-biased immune responses in mice infected with Schistosoma japonicum, but the exact mechanism behind the involvement of Tim-3 remains unknown. The present study aims to understand the role of Tim-3 in the immune response against S. japonicuminfection. Tim-3 expression was determined by flow cytometry, and increased Tim-3 expression was observed on CD4+and CD8+T cells, NK1.1+cells, and CD11b+cells from the livers of S. japonicum-infected mice. However, the increased level of Tim-3 was lower in the spleen than in the liver, and no increase in Tim-3 expression was observed on splenic CD8+T cells or CD11b+cells. The schistosome-induced upregulation of Tim-3 on natural killer (NK) cells was accompanied by reduced NK cell numbers in vitroand in vivo. Tim-3 antibody blockade led to upregulation of inducible nitric oxide synthase and interleukin-12 (IL-12) mRNA in CD11b+cells cocultured with soluble egg antigen and downregulation of Arg1 and IL-10, which are markers of M2 macrophages. In summary, we observed schistosome-induced expression of Tim-3 on critical immune cell populations, which may be involved in the Th2-biased immune response and alternative activation of macrophages during infection.

Expression of type I interferons (IFNs) can be induced by DNA-damaging agents, but the mechanisms and significance of this regulation are not completely understood. We found that the transcription factor IRF3, activated in an ATM-IKKα/β-dependent manner, stimulates cell-autonomous IFN-β expression in response to double-stranded DNA breaks. Cells and tissues with accumulating DNA damage produce endogenous IFN-β and stimulate IFN signaling in vitro and in vivo. In turn, IFN acts to amplify DNA-damage responses, activate the p53 pathway, promote senescence, and inhibit stem cell function in response to telomere shortening. Inactivation of the IFN pathway abrogates the development of diverse progeric phenotypes and extends the lifespan of Tercknockout mice. These data identify DNA-damage-response-induced IFN signaling as a critical mechanism that links accumulating DNA damage with senescence and premature aging.

A major medical challenge in the elderly is osteoporosis and the high risk of fracture. Telomere dysfunction is a cause of cellular senescence and telomere shortening, which occurs with age in cells from most human tissues, including bone. Telomere defects contribute to the pathogenesis of two progeroid disorders characterized by premature osteoporosis, Werner syndrome and dyskeratosis congenital. It is hypothesized that telomere shortening contributes to bone aging. We evaluated the skeletal phenotypes of mice with disrupted telomere maintenance mechanisms as models for human bone aging, including mutants in Werner helicase (Wrn−/−), telomerase (Terc−/−) and Wrn−/−Terc−/− double mutants. Compared with young wild-type (WT) mice, micro-computerized tomography analysis revealed that young Terc−/− and Wrn−/−Terc−/− mice have decreased trabecular bone volume, trabecular number and trabecular thickness, as well as increased trabecular spacing. In cortical bone, young Terc−/− and Wrn−/−Terc−/− mice have increased cortical thinning, and increased porosity relative to age-matched WT mice. These trabecular and cortical changes were accelerated with age in Terc−/− and Wrn−/−Terc−/− mice compared with older WT mice. Histological quantification of osteoblasts in aged mice showed a similar number of osteoblasts in all genotypes; however, significant decreases in osteoid, mineralization surface, mineral apposition rate and bone formation rate in older Terc−/− and Wrn−/−Terc−/− bone suggest that osteoblast dysfunction is a prominent feature of precocious aging in these mice. Except in the Wrn−/− single mutant, osteoclast number did not increase in any genotype. Significant alterations in mechanical parameters (structure model index, degree of anistrophy and moment of inertia) of the Terc−/− and Wrn−/−Terc−/− femurs compared with WT mice were also observed. Young Wrn−/−Terc−/− mice had a statistically significant increase in bone-marrow fat content compared with young WT mice, which remained elevated in aged double mutants. Taken together, our results suggest that Terc−/− and Wrn−/−Terc−/− mutants recapitulate the human bone aging phenotype and are useful models for studying age-related osteoporosis.

This paper studies the consensus problem of general linear multi-agent systems via self-triggered control. Two distributed self-triggered control schemes based on state feedback and output feedback are developed respectively. It is shown that under the proposed control protocols, consensus can be reached if the communication graph of the multi-agent system is connected. An example is presented to illustrate the effectiveness of the proposed control methods.

The malaria parasite Plasmodium falciparumutilizes host glycosaminoglycans (GAGs) as receptors for erythrocyte invasion and intravascular sequestration. Heparin and heparan sulfate (HS) are GAGs which can block erythrocyte invasion of the P. falciparmmerozoite, albeit the molecular mechanisms remain poorly understood. Characterization of these heparin-binding merozoite proteins and key ligands in the host–parasite interplay will lead to a better understanding of the mechanism of erythrocyte invasion by malaria parasites. Here, schizont-derived proteins that bind heparin were enriched by affinity chromatography, and 6062 peptides from 811 P. falciaprum-derived proteins were identified by two-dimensional liquid chromatography–mass spectrometry (LC/LC–MS/MS). The proteins were categorized into 14 functional groups ranging from pathogenesis, protein catabolic process to signal transduction. Proteins with predominant peptide counts were found to mainly originate from the rhoptry organelle of merozoites and the parasitized erythrocyte membrane. The profile of the heparin/HS-binding proteome of P. falciparumsuggests they have important functions in the biology of the parasite.

Age‐related osteoporosis is characterized by a decrease in bone‐forming capacity mediated by defects in the number and function of osteoblasts. An important cellular mechanism that may in part explain osteoblast dysfunction that occurs with aging is senescence of mesenchymal progenitor cells (MPCs). In the telomere‐based Wrn−/−Terc−/−model of accelerated aging, the osteoporotic phenotype of these mice is also associated with a major decline in MPC differentiation into osteoblasts. To investigate the role of MPC aging as a cell‐autonomous mechanism in senile bone loss, transplantation of young wild‐type whole bone marrow into Wrn−/−Terc−/−mutants was performed and the ability of engrafted cells to differentiate into cells of the osteoblast lineage was assessed. We found that whole bone marrow transplantation in Wrn−/−Terc−/−mice resulted in functional engraftment of MPCs up to 42 weeks, which was accompanied by a survival advantage as well as delays in microarchitectural features of skeletal aging. STEMCELLS2013;31:607–611