Aimed at improving upon the disadvantages of the single centralized Kalman filter for integrated navigation, including its fragile robustness and low solution accuracy, a nonlinear double model based ...on the improved decentralized federated extended Kalman filter (EKF) for integrated navigation is proposed. The multisensor error model is established and simplified in this paper according to the near-ground short distance navigation applications of small unmanned aerial vehicles (UAVs). In order to overcome the centralized Kalman filter that is used in the linear Gaussian system, the improved federated EKF is designed for multisensor-integrated navigation. Subsequently, because of the navigation requirements of UAVs, especially for the attitude solution accuracy, this paper presents a nonlinear double model that consists of the nonlinear attitude heading reference system (AHRS) model and nonlinear strapdown inertial navigation system (SINS)/GPS-integrated navigation model. Moreover, the common state parameters of the nonlinear double model are optimized by the federated filter to obtain a better attitude. The proposed algorithm is compared with multisensor complementary filtering (MSCF) and multisensor EKF (MSEKF) using collected flight sensors data. The simulation and experimental tests demonstrate that the proposed algorithm has a good robustness and state estimation solution accuracy.
Blood vessels support tumours by providing nutrients and oxygen, while also acting as conduits for the dissemination of cancer
. Here we use mouse models of breast and lung cancer to investigate ...whether endothelial cells also have active 'instructive' roles in the dissemination of cancer. We purified genetically tagged endothelial ribosomes and their associated transcripts from highly and poorly metastatic tumours. Deep sequencing revealed that metastatic tumours induced expression of the axon-guidance gene Slit2 in endothelium, establishing differential expression between the endothelial (high Slit2 expression) and tumoural (low Slit2 expression) compartments. Endothelial-derived SLIT2 protein and its receptor ROBO1 promoted the migration of cancer cells towards endothelial cells and intravasation. Deleting endothelial Slit2 suppressed metastatic dissemination in mouse models of breast and lung cancer. Conversely, deletion of tumoural Slit2 enhanced metastatic progression. We identified double-stranded RNA derived from tumour cells as an upstream signal that induces expression of endothelial SLIT2 by acting on the RNA-sensing receptor TLR3. Accordingly, a set of endogenous retroviral element RNAs were upregulated in metastatic cells and detected extracellularly. Thus, cancer cells co-opt innate RNA sensing to induce a chemotactic signalling pathway in endothelium that drives intravasation and metastasis. These findings reveal that endothelial cells have a direct instructive role in driving metastatic dissemination, and demonstrate that a single gene (Slit2) can promote or suppress cancer progression depending on its cellular source.
Small regulatory RNAs are essential and ubiquitous riboregulators that are the key mediators of RNA interference (RNAi). They include microRNAs (miRNAs) and short‐interfering RNAs (siRNAs), classes ...of ∼22 nucleotide RNAs. miRNAs and siRNAs bind to Argonaute proteins and form effector complexes that regulate gene expression; in animals, this regulation occurs primarily at the post‐transcriptional level. In this review, we will discuss our current understanding of how miRNA and siRNAs are generated and how they function to silence gene expression, focusing on animal and, in particular, mammalian miRNAs.
In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based ...Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning.
The real-time path planning of unmanned aerial vehicles (UAVs) in dynamic environments with moving threats is a difficult problem. To solve this problem, this paper proposes a time-based rapidly ...exploring random tree (time-based RRT*) algorithm, called the hierarchical rapidly exploring random tree algorithm based on potential function lazy planning and low-cost optimization (HPO-RRT*). The HPO-RRT* algorithm can guarantee path homotopy optimality and high planning efficiency. This algorithm uses a hierarchical architecture comprising a UAV perception system, path planner, and path optimizer. After the UAV perception system predicts moving threats and updates world information, the path planner obtains the heuristic path. First, the path planner uses the bias sampling method based on the artificial potential field function proposed in this paper to guide sampling to improve the efficiency and quality of sampling. Then, the tree is efficiently extended by the improved time-based lazy collision checking RRT* algorithm to obtain the heuristic path. Finally, a low-cost path optimizer quickly optimizes the heuristic path directly to optimize the path while avoiding additional calculations. Simulation results show that the proposed algorithm outperforms the three existing advanced algorithms in terms of addressing the real-time path-planning problem of UAVs in a dynamic environment.
To improve the precision and robustness of Unmanned Aerial Vehicle (UAV) integrated navigation systems, this paper presents an Interacting Multiple Model (IMM) navigation algorithm based on a Robust ...Cubature Kalman Filter (RCKF) with modified Zero Velocity Update (ZUPT) method assistance. This algorithm has a two-level fusion structure. At the bottom level, the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation model and the Dynamic Zero Velocity Update/Inertial Navigation System (DZUPT/INS) integrated navigation model are established by modifying the Zero Velocity Update (ZUPT) method. Subsequently, the RCKF algorithm adopts a robust factor to weaken the influence of measurement outliers on the filter solution. At the top level, the estimation results of the GPS/INS integrated navigation model and the DZUPT/INS integrated navigation model are fused by the IMM algorithm. In addition to enhancing the robustness of filter estimation in the presence of measurement outliers, the proposed navigation algorithm also corrects navigation errors with ZUPT method assistance. Simulation and experimental analyses demonstrate the performance of the proposed navigation algorithm for UAVs.
The human transcriptome contains many types of noncoding RNAs, which rival the number of protein‐coding species. From long noncoding RNAs (lncRNAs) that are over 200 nucleotides long to ...piwi‐interacting RNAs (piRNAs) of only 20 nucleotides, noncoding RNAs play important roles in regulating transcription, epigenetic modifications, translation, and cell signaling. Roles for noncoding RNAs in disease mechanisms are also being uncovered, and several species have been identified as potential drug targets. On May 11–14, 2021, the Keystone eSymposium “Noncoding RNAs: Biology and Applications” brought together researchers working in RNA biology, structure, and technologies to accelerate both the understanding of RNA basic biology and the translation of those findings into clinical applications.
Noncoding RNA comprise a diverse range of RNAs. Long noncoding RNAs (lncRNA) contain features of mRNAs; the most well‐studied localize to the nucleus, help form nuclear condensates, and affect transcription machinery. Short noncoding RNA species play roles in mRNA translation, alternative splicing, and RNA editing and silencing. As new technologies are developed, the prevalence and importance of the diverse array of small RNAs will come to light. On Demand: https://keysym.us/21EK44NYAS.
Assembly of microRNA ribonucleoproteins (miRNPs) or RNA-induced silencing complexes (RISCs) is essential for the function of miRNAs and initiates from processing of precursor miRNAs (pre-miRNAs) by ...Dicer or by Ago2. Here, we report an in vitro miRNP/RISC assembly assay programmed by pre-miRNAs from mammalian cell lysates. Combining in vivo studies in Dicer Knockout cells reconstituted with wild-type or catalytically inactive Dicer, we find that the miRNA loading complex (miRLC) is the primary machinery linking pre-miRNA processing to miRNA loading. We show that a miRNA precursor deposit complex (miPDC) plays a crucial role in Dicer-independent miRNA biogenesis and promotes miRNP assembly of certain Dicer-dependent miRNAs. Furthermore, we find that 5′-uridine, 3′-mid base pairing, and 5′-mid mismatches within pre-miRNAs promote their assembly into miPDC. Our studies provide a comprehensive view of miRNP/RISC assembly pathways in mammals, and our assay provides a versatile platform for further mechanistic dissection of such pathways in mammals.
► Development of in vitro miRNP/RISC assembly assay programmed by pre-miRNAs ► miRLC is the primary machinery that links pre-miRNA processing to miRNA loading ► Roles of miPDC in miRNA biogenesis ► 5′-U, 3′-mid base pairs, 5′-mid mismatches in pre-miRNAs promote miPDC assembly
Carbon nanofiber (CNF) is a nanomaterial with unique mechanical properties, which can improve the properties of composite materials effectively. Research focusses on the impact of CNF on asphalt, ...asphalt binders, and mixtures. Traditional emulsified asphalt presents a limited performance at both high and low temperatures. Emulsified asphalt with a better performance, is therefore required in engineering. Referring to research on CNF-asphalt, CNF is considered to improve the performance of emulsified asphalt. In this study, a preparation method for CNF modified emulsified asphalt with styrene-butadiene rubber (SBR) was proposed. Ultrasonication and surfactant were utilized to disperse the CNFs in water. The optimum dispersion surfactant percentages and ultrasonic energy density to disperse CNFs were determined through ultraviolet-visible spectra (UV-vis spectra). The modified emulsified asphalt was produced using CNFs suspension with SBR as a modifier, and the properties of the residue, with different percentages of CNFs, were tested. Gel permeation chromatography (GPC) was performed to analyze the molecular size distribution. The results indicated that CNFs significantly improved high-temperature performance of the residue but decreased low-temperature properties. The addition of SBR not only perfected storage stability but also improved low-temperature performance by introducing more small molecules.
Abstract
In recent years, the multi-state constraint Kalman filter has been widely used in the visual-inertial navigation of unmanned systems. However, in most previous studies, the measurement noise ...of the navigation system was assumed to be Gaussian noise, but this is not the case in practice. In this paper, the maximum correntropy criterion is introduced into the multi-state constraint Kalman filter to improve the robustness of the visual-inertial system. First, the new maximum correntropy criterion-based Kalman filter is introduced, it uses the maximum correntropy criterion to replace the minimum mean square error criterion to suppress the interference of measurement outliers on the filtering results, and it has no numerical problem in the presence of large measurements outliers. Then, an improved multi-state constraint Kalman filter is designed by applying the new maximum correntropy criterion-based Kalman filter to the multi-state constraint Kalman filter, which improved the robustness of the multi-state constraint Kalman filter. The results of numerical simulation and dataset experiments show that the proposed filter improves the accuracy and robustness of the visual-inertial system.
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