Osteoporosis (OP) is one of the major public health problems in the world. However, the biomarkers between the peripheral blood mononuclear cells (PBMs) and bone tissue for prognosis of OP have not ...been well characterized. This study aimed to explore the similarities and differences of the gene expression profiles between the PBMs and bone tissue and identify potential genes, transcription factors (TFs) and hub proteins involved in OP. The patients were enrolled as an experimental group, and healthy subjects served as normal controls. Human whole-genome expression chips were used to analyze gene expression profiles from PBMs and bone tissue. And the differentially expressed genes (DEGs) were subsequently studied using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. The above DEGs were constructed into protein-protein interaction network. Finally, TF-DEGs regulation networks were constructed. Microarray analysis revealed that 226 DEGs were identified between OP and normal controls in the PBMs, while 2295 DEGs were identified in the bone tissue. And 13 common DEGs were obtained by comparing the 2 tissues. The Gene Ontology analysis indicated that DEGs in the PBMs were more involved in immune response, while DEGs in bone were more involved in renal response and urea transmembrane transport. And the Kyoto Encyclopedia of Genes and Genomes analysis indicated almost all of the pathways in the PBMs were overlapped with those in the bone tissue. Furthermore, protein-protein interaction network presented 6 hub proteins: PI3K1, APP, GNB5, FPR2, GNG13, and PLCG1. APP has been found to be associated with OP. Finally, 5 key TFs were identified by TF-DEGs regulation networks analysis (CREB1, RUNX1, STAT3, CREBBP, and GLI1) and were supposed to be associated with OP. This study enhanced our understanding of the pathogenesis of OP. PI3K1, GNB5, FPR2, GNG13, and PLCG1 might be the potential targets of OP.
Scanning electron microscopy (SEM) is a widely used method for the analysis of concrete micro structure. To quantitatively analyze the SEM images with high efficiency and accuracy, an automatic ...segmentation framework is proposed in this paper. The deep segmentation algorithm is purposely optimized from PointRend based on the characteristic of SEM images to improve prediction accuracy, especially the performance around boundaries. Moreover, the SEM images can be segmented without additional treatment. Cement paste samples with 0.2 and 0.4 water-to-cement ratios are prepared and cured for 1, 3, 7, 14, and 28 days. Totally SEM images with 2267 labeled cement particles are included to build the dataset. From the results of intersection over union and pixel accuracy, the proposed algorithm outperforms the trainable waikato environment for knowledge analysis (WEKA) segmentation, Fully Convolutional Networks (FCN), and the original PointRend method. The segmentation results are used to calculate the hydration degree of two cement paste samples. Good agreement is obtained with the hydration degree calculated by using nonevaporable water in the samples for the 5 curing durations. At last, the shape of the cement particles is analyzed. Irregularity and roundness of the cement particles do not change significantly with an increase in curing duration.
This paper is concerned with solving a large category of convex optimization problems using a group of agents, each only being accessible to its individual convex cost function. The optimization ...problems are modeled as minimizing the sum of all the agents' cost functions. The communication process between agents is described by a sequence of time-varying yet balanced directed graphs which are assumed to be uniformly strongly connected. Taking into account the fact that the communication channel bandwidth is limited, for each agent we introduce a vector-valued quantizer with finite quantization levels to preprocess the information to be exchanged. We exploit an event-triggered broadcasting technique to guide information exchange, further reducing the communication cost of the network. By jointly designing the dynamic event-triggered encoding-decoding schemes and the event-triggered sampling rules (to analytically determine the sampling time instant sequence for each agent), a distributed subgradient descent algorithm with constrained information exchange is proposed. By selecting the appropriate quantization levels, all the agents' states asymptotically converge to a consensus value which is also the optimal solution to the optimization problem, without committing saturation of all the quantizers. We find that one bit of information exchange across each connected channel can guarantee that the optimiztion problem can be exactly solved. Theoretical analysis shows that the event-triggered subgradient descent algorithm with constrained data rate of networks converges at the rate of <inline-formula> <tex-math notation="LaTeX">{O}( {\ln t/{\sqrt {t}}}) </tex-math></inline-formula>. We supply a numerical simulation experiment to demonstrate the effectiveness of the proposed algorithm and to validate the correctness of theoretical results.
As an initial factor, sepsis and multiple organ dysfunction syndrome (MODS) caused by sepsis are the principal causes of death in burned patients. In this report, we measured the levels of tumor ...necrosis factor (TNF)-α, interleukin (IL)-6 and IL-8 in severely burned patients with sepsis after the initiation of continuous vein-vein hemodiafiltration (CVVHDF) to evaluate the clinical usefulness of CVVHDF on the removal of key mediators. The vital sign indices, such as the heart rate (HR), respiration (R) and central venous pressure (CVP), were recorded at 0 and 42 h in each group. Further, the laboratory examinations indexes, such as the white blood cell count, blood sugar, serum sodium, blood urea nitrogen and serum creatinine, were detected in venous blood samples. Twenty-two severely burned patients suffering from sepsis were randomized into the control group (A, n = 11) and the experimental group (B, n = 11). The patients in group A underwent conventional treatment, and those in group B received conventional+CVVHDF treatment. The vital signs, such as the HR, R, and CVP, and laboratory examination indices, such as the blood cell count, blood sugar, serum sodium, blood urea nitrogen, and serum creatinine, dropped significantly in group B compared with those in group A at 42 h (P < 0.05). The plasma levels of TNF-α, IL-6 and IL-8 were measured at 0, 12, 18, 24, 36 and 42 h after the start of CVVHDF and at the same time points after the patients were diagnosed with sepsis in group A. The plasma levels of TNF-α in group B decreased by 32% at 18 h after the start of CVVHDF and decreased by 43% at 42 h after the start of CVVHDF; however, these levels were increased compared with the normal values (P < 0.01). The plasma levels of IL-6 decreased at 18 h after the start of CVVHDF (0.274 ± 0.137 ng/ml). Following a brief increase at 24 h, the plasma levels of IL-6 again decreased continuously until the end of the investigation (0.192 ± 0.119 ng/ml). The plasma levels of IL-8 in group B decreased by 56% at 18 h after the start of CVVHDF, but they were increased compared with the normal values (P < 0.01). The plasma levels of IL-8 in group B decreased by 70% at 42 h after the start of CVVHDF, but they were increased compared with the normal values (P < 0.01). The MODS incident was 4 of 11 in group A compared with 1 of 11 in group B (P < 0.01). In conclusion, CVVHDF can effectively reduce the levels of TNF-α, IL-6 and IL-8 as well as the MODS incidence in patients with serious burns.
Despite recent progress in trajectory planning for multiple robots and a single tethered robot, trajectory planning for multiple tethered robots to reach their individual targets without ...entanglements remains a challenging problem. In this article, a complete approach is presented to address this problem. First, a multirobot tether-aware representation of homotopy is proposed to efficiently evaluate the feasibility and safety of a potential path in terms of 1) the cable length required to reach a target following the path, and 2) the risk of entanglements with the cables of other robots. Then the proposed representation is applied in a decentralized and online planning framework, which includes a graph-based kinodynamic trajectory finder and an optimization-based trajectory refinement, to generate entanglement-free, collision-free, and dynamically feasible trajectories. The efficiency of the proposed homotopy representation is compared against the existing single and multiple tethered robot planning approaches. Simulations with up to eight UAVs show the effectiveness of the approach in entanglement prevention and its real-time capabilities. Flight experiments using three tethered UAVs verify the practicality of the presented approach. The software implementation is publicly available online. 1
Compact wearable mapping system (WMS) has gained significant attention due to their convenience in various applications. Specifically, it provides an efficient way to collect prior maps for 3D ...structure inspection and robot-based “last-mile delivery” in complex environments. However, vibrations in human motion and the uneven distribution of point cloud features in complex environments often lead to rapid drift, which is a prevalent issue when applying existing LiDAR Inertial Odometry (LIO) methods on low-cost WMS. To address these limitations, we propose a novel LIO for WMSs based on Hybrid Continuous Time Optimization (HCTO) considering the optimality of Lidar correspondences. First, HCTO recognizes patterns in human motion (high-frequency part, low-frequency part, and constant velocity part) by analyzing raw IMU measurements. Second, HCTO constructs hybrid IMU factors according to different motion states, which enables robust and accurate estimation against vibration-induced noise in the IMU measurements. Third, the best point correspondences are selected using optimal design to achieve real-time performance and better odometry accuracy. We conduct experiments on head-mounted WMS datasets to evaluate the performance of our system, demonstrating significant advantages over state-of-the-art methods. Video recordings of experiments can be found on the project page of HCTO: https://github.com/kafeiyin00/HCTO.
In this paper, a safety control scheme for quadrotor is proposed to guarantee collision resilience like flying insects. The direction and magnitude of contact wrench are quantitatively analyzed ...subject to the compliant contact wrench model. A nonlinear disturbance observer (NDO) is developed to estimate the contact wrench exerted on the quadrotor, and effective collision detection can be guaranteed based on the observer. Subsequently, a tilt-torsion decomposition based attitude controller is developed to prioritize the correction of horizontal posture over yaw error. The attitude error is separated into roll-pitch portion and yaw portion. Reasonable roll and pitch torques can be generated by allocating a higher gain for roll-pitch portion, allowing the quadrotor to recover from collisions promptly. Simulations and flight experiments are carried out to demonstrate the effectiveness of the proposed collision resilience control scheme.
A stochastic self-triggered model predictive control (SSMPC) algorithm is proposed for linear systems subject to exogenous disturbances and probabilistic constraints. The main idea behind the ...self-triggered framework is that at each sampling instant, an optimization problem is solved to determine both the next sampling instant and the control inputs to be applied between the two sampling instants. Although the self-triggered implementation achieves communication reduction, the control commands are necessarily applied in open-loop between sampling instants. To guarantee probabilistic constraint satisfaction, necessary and sufficient conditions are derived on the nominal systems by using the information on the distribution of the disturbances explicitly. Moreover, based on a tailored terminal set, a multi-step open-loop MPC optimization problem with infinite prediction horizon is transformed into a tractable quadratic programming problem with guaranteed recursive feasibility. The closed-loop system is shown to be stable. Numerical examples illustrate the efficacy of the proposed scheme in terms of performance, constraint satisfaction, and reduction of both control updates and communications with a conventional time-triggered scheme.
WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since ...it is device free, cost effective and privacy preserving. Though numerous WiFi sensing methods have been developed, most of them only consider single smart home scenario. Without the connection of powerful cloud server and massive users, large-scale WiFi sensing is still difficult. In this article, we first analyze and summarize these obstacles, and propose an efficient large-scale WiFi sensing framework, namely, EfficientFi. The EfficientFi works with edge computing at WiFi access points and cloud computing at center servers. It consists of a novel deep neural network that can compress fine-grained WiFi channel state information (CSI) at edge, restore CSI at cloud, and perform sensing tasks simultaneously. A quantized autoencoder and a joint classifier are designed to achieve these goals in an end-to-end fashion. To the best of our knowledge, the EfficientFi is the first Internet of Things-cloud-enabled WiFi sensing framework that significantly reduces communication overhead while realizing sensing tasks accurately. We utilized human activity recognition (HAR) and identification via WiFi sensing as two case studies, and conduct extensive experiments to evaluate the EfficientFi. The results show that it compresses CSI data from 1.368 Mb/s to 0.768 kb/s with extremely low error of data reconstruction and achieves over 98% accuracy for HAR.
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