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Aimed at improving the nonlinear integrated navigation solution performance of multiple low-cost sensors fusion, this paper presents a multilayer loosely-coupled, local-global, and step-optimized MF5DCKF (Multisensor Federated fifth-degree Cubature Kalman filter) state estimation algorithm for the small unmanned aerial vehicle (UAV). This method establishes a multilayer nonlinear integrated navigation model composed of the nonlinear attitude and heading reference system (AHRS) error model, strapdown inertial navigation system/global positioning system (SINS/GPS) error model, and strapdown inertial navigation system/barometer (SINS/BARO) error model to enhance the robustness and richness of the navigation module. Further, based on the above navigation models, a loosely-coupled error state fusion frame is designed to obtain the local convergent state vector. Simultaneously, a three-layer fifth-degree Cubature Kalman filter is proposed to improve the local state estimation accuracy. Subsequently, to optimize the estimated local state, this paper presents a novel distributed MF5DCKF scheme fusing the local state vector to calculate the global optimal state parameters in a step-optimized process. The experimental flight test results show that the proposed algorithm achieves a higher state solution accuracy and a better convergent performance compared with some conventional multisensor fusion algorithms. The new algorithm framework can provide applicability and reliability for the small UAV during the flight.

In this article, we focus on addressing the nonlinear filtering problem with unknown measurement noise covariance and measurement outliers, which may be encountered in the application in strapdown inertial navigation system/global positioning system integrated navigation system. Although the existing methods, such as the adaptive Kalman filter, are widely used in the integrated navigation system, their estimation accuracy is poor, this paper proposes a variational Bayesian-based robust cubature Kalman filter to address this problem, which not only retains the adaptivity when the measurement noise is unknown but also exhibits robustness in the presence of measurement outliers, First, the variational Bayesian method is applied to the estimation of measurement noise, then the maximum correntropy criterion is introduced to replace the minimum mean square error criterion, which improves the robust performance of the filter. The numerical simulation demonstrates that the proposed filter outperforms the existing filters both in estimation accuracy and robustness, and the effectiveness of the proposed filter is verified on the integrated navigation system.

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.

Aiming at the attitude solution accuracy and robustness for small UAVs in complex flight conditions, this paper proposes a dynamic adaptive attitude and heading systems(AHRS) estimator with singular value decomposition Cubature Kalman filter(SVDCKF). Considering the problem of random bias for the low-cost attitude sensor, this paper designs a method that the sensor random bias is used as the state vector to eliminate the effect of the sensor random bias. Due to the non-linearity of small UAVs AHRS model and the non-positive definite phenomenon of the covariance matrix, a nonlinear AHRS filter combined with the Cubature Kalman filter and singular value decomposition is designed to improve the attitude solution accuracy. In addition, when the UAV flies in the different flight conditions, the three-axis acceleration of the attitude sensor will affect the attitude solution. Thus, a dynamic adaptive factor based on adaptive filtering is used to adjust continuously the acceleration noise variance to improve the robustness of the AHRS. The experimental results show that the method and algorithm proposed not only improve the attitude solution accuracy, and satisfy the flight requirements of small UAVs, but also eliminate the influence of the attitude sensor random bias and three-axis acceleration for the attitude solution to improve the proposed algorithm robustness and anti-interference.

In order to improve the accuracy of attitude of unmanned aerial vehicle (UAV) navigation system in dynamic environment, an attitude calculation algorithm based on acceleration correction model is proposed. First, the acceleration correction model is established to calculate the estimated non-gravitational acceleration and external non-gravitational acceleration to modify the output value of the accelerometer, which reduces the influence of non-gravitational acceleration on the attitude calculation in dynamic environment. Then, the attitude calculation model based on Kalman filter is built, attitude angle calculated by corrected acceleration and magnetometer as measurement of filtering model, and the attitude calculation algorithm based on the acceleration correction model is designed. The experimental results show that the algorithm can reduce the interference of non-gravitational acceleration to attitude calculation, which avoids attitude angle divergence of UAV navigation system in dynamic environment, and improves the accuracy and anti-interference ability of UAV navigation system in 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.

Aimed at nonlinear attitude and heading reference and system(AHRS) solution accuracy, this paper presents a novel attitude model of the Singular Value Decomposition (SVD) Cubature Kalman Filter (SVDCKF) fusion algorithm. Firstly, this paper designs the attitude integrated model of small UAV, and uses quaternion to update attitude. Then, in order to solve the nonlinear attitude model, this paper present the SVDCKF to improve the attitude solution accuracy. Finally, the results of numerical simulation and experimental test verify the proposed algorithm has the high attitude accuracy and can meet the flying attitude requirements of small UAV.

Asphalt aging occurring during the mixing and transportation of a mixture will decrease the performance. In this study, a compound modified rubber asphalt containing crumb rubber modifier (...

Stress-induced cleavage of transfer RNAs (tRNAs) into tRNA-derived fragments (tRFs) occurs across organisms from yeast to humans; yet, its mechanistic underpinnings and pathological consequences remain poorly defined. Small RNA profiling revealed increased abundance of a cysteine tRNA fragment (5'-tRF

SUMMARYStress-induced cleavage of transfer RNAs (tRNAs) into tRNA-derived fragments (tRFs) occurs across organisms from yeast to human, yet its mechanistic bases and pathological consequences remain poorly defined. By performing genome-wide small RNA profiling, we detected increased abundance of a Cysteine tRNA fragment (5’-tRFCys) during breast cancer metastatic progression. 5’’-tRFCys is required for efficient breast cancer metastatic lung colonization and metastatic cell survival. We identified Nucleolin as the direct binding partner of 5’-tRFCys. 5’-tRFCys binding enhanced the stability of Nucleolin’s associated pro-metastatic transcripts encoding metabolic enzymes Mthfd1l and Pafah1b1. 5’-tRFCys stabilized these transcripts by promoting Nucleolin oligomerization and the assembly of Nucleolin and its bound transcripts into a higher-order ribonucleoprotein complex. Our findings reveal that a tRF can promote oligomerization of an RNA binding protein into a stabilizing ribonucleoprotein complex containing specific target transcripts, thereby driving specific metabolic pathways underlying cancer progression.

Blood vessels support tumours by providing nutrients and oxygen, while also acting as conduits for the dissemination of cancer(1). 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.

Carbon nanofiber (CNF) is a nanomaterial with unique mechanical properties, which can improve the properties of composite materials effectively. Types of research focused on the impact of CNF on asphalt, asphalt binder, and mixture. Traditional emulsified asphalt presents a limited performance at both high and low temperature. Meanwhile, emulsified asphalt with better performance is required in engineering. Referring to the research of CNF-asphalt, CNF is considerable 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 improved high-temperature performance of the residue significantly 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.

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.

Considering the collision-prone problem near the initial point, goal non-reachable with obstacle nearby (GNWON) and the chattering problem of the traditional artificial potential field algorithm(TAPF), the attractive potential field function and the repulsive potential field function are improved respectively, and an improved adaptive artificial potential field algorithm(AAPF) is proposed. The algorithm can calculate the different potential field of each path point on-line in real time. On this basis, a 2D improved adaptive artificial potential field algorithm is extended to 3D space for autonomous obstacle avoidance planning of quadrotor UAV. A 3D obstacle model is established, and the principle of obstacle avoidance at the nearest contact point is designed to calculate the repulsive potential field. Then the obstacle avoidance planning strategy of Four-rotor UAV based on 3D adaptive potential field algorithm is designed. The simulation results show that the improved adaptive algorithm effectively compensates for the shortcomings of the traditional artificial potential field method and can be applied in 3D.

To solve the problem of autonomous position and navigation for UAV(Unmanned aerial vehicle) in complex environment, this paper has proposed a Multi-level variable dimension extended Kalman filter fusion algorithm(ML-VDEKF). The motion and observation equations of the filter system have been established, and some low-cost sensors such as IMU(Inertial measurement unit), magnetometer, GPS and barometer have been combined to estimate state information during the flight. The actual experimental data shows that the proposed algorithm can provide accurate attitude, velocity and position. At the same time, the algorithm greatly reduces the computation when running in real-time operation of embedded devices, and can meet the flight requirement.

In the UAV (Unmanned Aerial Vehicle) integrated navigation system, considering the poor fault tolerance, inaccurate system model and robustness of the classic GPS/INS (Inertial Navigation System) integrated navigation algorithm, an improved Federated Filter navigation algorithm is put forward in this paper. Firstly, the GPS/INS/MAG (magnetometer) Federated Filter algorithm based on the theory of federated filter was designed. Secondly, the Adaptive Fading Kalman Filter algorithm and Robust Kalman Filter algorithm are introduced into the two sub-filters of the federated filter algorithm to suppress the interference of inaccurate system model and sensor errors on the filter results. The simulation results show that the Federated Filter model is effective and the improved Federated Filter algorithm can effectively enhance the attitude angle accuracy and suppress the influence of sensor error on the filter result.

Aimed at the problem of small unmanned aerial vehicle (UAV) attitude solution accuracyand real-time performance in short-range navigation flight, in this paper, we propose a fastweakly-coupled double-layer error-state Kalman filter (DL-ESKF) attitude estimation algorithm.Considering the application of short-range navigation, we designed an improved attitude errormodel for low-cost gyroscope/accelerometer/magnetometer devices. In addition, we reasonablysimplified certain factors that affect the attitude solution to reduce the filtering calculation burden.For the data coupling phenomenon caused by the different sampling frequencies of the attitudesensor data in the filtering process, we designed a new attitude algorithm combined with the ESKFand hierarchical filter. The first layer of filters used an accelerometer and the second layer useda magnetometer to correct the attitude error. We also built an offline and real-time test platformto verify the performance of the proposed algorithm in a simulation and flight test environmentcompared with the classic attitude algorithms. The experimental results demonstrated that theproposed algorithm not only improved the attitude solution accuracy and stability but also reducedthe filter running time.

N(6)-methyladenosine (m(6)A) is the most abundant internal modification of messenger RNA. While the presence of m(6)A on transcripts can impact nuclear RNA fates, a reader of this mark that mediates processing of nuclear transcripts has not been identified. We find that the RNA-binding protein HNRNPA2B1 binds m(6)A-bearing RNAs in vivo and in vitro and its biochemical footprint matches the m(6)A consensus motif. HNRNPA2B1 directly binds a set of nuclear transcripts and elicits similar alternative splicing effects as the m(6)A writer METTL3. Moreover, HNRNPA2B1 binds to m(6)A marks in a subset of primary miRNA transcripts, interacts with the microRNA Microprocessor complex protein DGCR8, and promotes primary miRNA processing. Also, HNRNPA2B1 loss and METTL3 depletion cause similar processing defects for these pri-miRNA precursors. We propose HNRNPA2B1 to be a nuclear reader of the m(6)A mark and to mediate, in part, this mark's effects on primary microRNA processing and alternative splicing. PAPERCLIP.

Upon exposure to stress, tRNAs are enzymatically cleaved, yielding distinct classes of tRNA-derived fragments (tRFs), yielding distinct classes of tRFs. We identify a novel class of tRFs derived from tRNA(Glu), tRNA(Asp), tRNA(Gly), and tRNA(Tyr) that, upon induction, suppress the stability of multiple oncogenic transcripts in breast cancer cells by displacing their 3' untranslated regions (UTRs) from the RNA-binding protein YBX1. This mode of post-transcriptional silencing is sequence specific, as these fragments all share a common motif that matches the YBX1 recognition sequence. Loss-of-function and gain-of-function studies, using anti-sense locked-nucleic acids (LNAs) and synthetic RNA mimetics, respectively, revealed that these fragments suppress growth under serum-starvation, cancer cell invasion, and metastasis by breast cancer cells. Highly metastatic cells evade this tumor-suppressive pathway by attenuating the induction of these tRFs. Our findings reveal a tumor-suppressive role for specific tRNA-derived fragments and describe a molecular mechanism for their action. This transcript displacement-based mechanism may generalize to other tRNA, ribosomal-RNA, and sno-RNA fragments.

Mammalian Argonaute 2 (Ago2) protein associates with microRNAs (miRNAs) or small interfering RNAs (siRNAs) forming RNA-induced silencing complexes (RISCs/miRNPs). In the present work, we characterize the RNA-binding and nucleolytic activity of recombinant mouse Ago2. Our studies show that recombinant mouse Ago2 binds efficiently to miRNAs forming active RISC. Surprisingly, we find that recombinant mouse Ago2 forms active RISC using pre-miRNAs or long unstructured single stranded RNAs as guides. Furthermore, we demonstrate that, in vivo, endogenous human Ago2 binds directly to pre-miRNAs independently of Dicer, and that Ago2:pre-miRNA complexes are found both in the cytoplasm and in the nucleus of human cells.

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 approximately 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.

MicroRNAs (miRNAs) are an important class of small RNAs that regulate gene expression posttranscriptionally through the microRNP (miRNP)/RNA-induced silencing complex (RISC). The core component of miRNPs is an Argonuate protein that directly binds to a miRNA. In mammals, most miRNPs are assembled through the miRNA loading complex (miRLC), which is composed of Dicer, Ago, and TRBP. miRLC processes miRNA precursors (pre-miRNAs) into miRNA duplexes and loads miRNA duplexes to Ago. Here, we describe a native gel analysis system for detecting miRNPs assembled with pre-miRNAs from mammalian lysates that ectopically express Ago2. The methods presented here provide a powerful tool for further dissecting miRNP assembly pathways in mammals.

MicroRNAs (miRNAs) are an important class of small RNAs that regulate gene expression posttranscriptionally through the microRNP (miRNP)/RNA-induced silencing complex (RISC). The core component of miRNPs is an Argonuate protein that directly binds to a miRNA. In mammals, most miRNPs are assembled through the miRNA loading complex (miRLC), which is composed of Dicer, Ago, and TRBP. miRLC processes miRNA precursors (pre-miRNAs) into miRNA duplexes and loads miRNA duplexes to Ago. Here, we describe a native gel analysis system for detecting miRNPs assembled with pre-miRNAs from mammalian lysates that ectopically express Ago2. The methods presented here provide a powerful tool for further dissecting miRNP assembly pathways in mammals.

MicroRNAs (miRNAs) are essential for regulation of gene expression. Though numerous miRNAs have been identified by high-throughput sequencing, few precursor miRNAs (pre-miRNAs) are experimentally validated. Here we report a strategy for constructing high-throughput sequencing libraries enriched for full-length pre-miRNAs. We find widespread and extensive uridylation of Argonaute (Ago)-bound pre-miRNAs, which is primarily catalyzed by two terminal uridylyltransferases: TUT7 and TUT4. Uridylation by TUT7/4 not only polishes pre-miRNA 3' ends, but also facilitates their degradation by the exosome, preventing clogging of Ago with defective species. We show that the exosome exploits distinct substrate preferences of DIS3 and RRP6, its two catalytic subunits, to distinguish productive from defective pre-miRNAs. Furthermore, we identify a positive feedback loop formed by the exosome and TUT7/4 in triggering uridylation and degradation of Ago-bound pre-miRNAs. Our study reveals a pre-miRNA surveillance system that comprises TUT7, TUT4, and the exosome in quality control of miRNA synthesis.

Background Argonaute, the core component of the RNA induced silencing complex (RISC), binds to mature miRNAs and regulates gene expression at transcriptional or post-transcriptional level. We recently reported that Argonaute 2 (Ago2) also assembles into complexes with miRNA precursors (pre-miRNAs). These Ago2:pre-miRNA complexes are catalytically active in vitro and constitute non-canonical RISCs. Results The use of pre-miRNAs as guides by Ago2 bypasses Dicer activity and complicates in vitro RISC reconstitution. In this work, we characterized Ago2:pre-miRNA complexes and identified RNAs that are targeted by miRNAs but not their corresponding pre-miRNAs. Using these target RNAs we were able to recapitulate in vitro pre-miRNA processing and canonical RISC loading, and define the minimal factors required for these processes. Conclusions Our results indicate that Ago2 and Dicer are sufficient for processing and loading of miRNAs into RISC. Furthermore, our studies suggest that Ago2 binds primarily to the 5'- and alternatively, to the 3'-end of select pre-miRNAs.