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Using a live cell imaging system showed cytokinesis and cell movement after overexpress of LNC CRYBG3.

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Using a live cell imaging system showed cytokinesis and cell movement after overexpress of LNC NC.

The dynamic interchange between monomeric globular actin (G-actin) and polymeric filamentous actin filaments (F-actin) is fundamental and essential to many cellular processes, including cytokinesis and maintenance of genomic stability. Here, we report that the long noncoding RNA LNC CRYBG3 directly binds G-actin to inhibit its polymerization and formation of contractile rings, resulting in M-phase cell arrest. Knockdown of LNC CRYBG3 in tumor cells enhanced their malignant phenotypes. Nucleotide sequence 228-237 of the full-length LNC CRYBG3 and the ser14 domain of β-actin is essential for their interaction, and mutation of either of these sites abrogated binding of LNC CRYBG3 to G-actin. Binding of LNC CRYBG3 to G-actin blocked nuclear localization of MAL, which consequently kept serum response factor (SRF) away from the promoter region of several immediate early genes, including JUNB and Arp3, which are necessary for cellular proliferation, tumor growth, adhesion, movement, and metastasis. These findings reveal a novel lncRNA-actin-MAL-SRF pathway and highlight LNC CRYBG3 as a means to block cytokinesis and to treat cancer by targeting the actin cytoskeleton.Significance: Identification of the long noncoding RNA LNC CRYBG3 as a mediator of microfilament disorganization marks it as a novel therapeutic antitumor strategy. Cancer Res; 78(16); 4563–72. ©2018 AACR.

Supplement Figure 1: Cell cycle synchronization validation and cell cycle results. Supplement Figure 2: LNC CRYBG3 localization and proportion of binucleated cells in different cell lines. Supplement Figure 3: Western blot and immunofluorescence analysis determined the actin expression and microfilaments morphology. Supplement Figure 4: LNC CRYBG3 expression after overexpressed K-ras G12V and EGFR T790M / L858R .

In this paper, the chronotoxicity of radiofrequency fields (RF) in the pubertal testis development and the involved molecular pathways were investigated by exposing four-week-old mice to RF (1800 MHz, SAR, 0.50 W/kg) in the morning and evening of each day for three weeks. Then, pathological changes and functional indices within the testis were determined. We also used a long non-coding RNA (lncRNA) microarray and GO/KEGG pathway analyses to determine lncRNA expression profiles and predict their potential functions. The cis and trans regulation of lncRNAs were investigated, and an interaction network was constructed using Cytoscape software. RF exposure led to a range of pathological changes in the testes of adolescent mice, as testicular weights and daily sperm productions decreased, and the testosterone secretion reduced. Furthermore, RF induced dysregulation in the expression of testicular lncRNAs. We identified 615 and 183 differentially expressed lncRNAs that were associated with morning and evening exposure to RF, respectively. From 15 differential expression lncRNAs both in morning RF group and evening RF group, we selected 6 lncRNAs to be validated by quantitative reverse transcription PCR (qRT-PCR). The differentially expressed lncRNAs induced by morning RF exposure were highly correlated with many different pathways, including Fanconi syndrome, metabolic processes, cell cycle, DNA damage, and DNA replication. Trans-regulation analyses further showed that differentially expressed lncRNAs were involved in multiple transcription factor-regulated pathways, such as TCFAP4, NFkB, HINFP, TFDP2, FoxN1, and PAX5. These transcription factors have all been shown to be involved in the modulation of testis development, cell cycle progression, and spermatogenesis. These findings suggest that the extent to which 1800 MHz RF induced toxicity in the testes and changed the expression of lncRNAs showed differences between morning exposure and evening exposure. These data indicate that differentially expressed lncRNAs play crucial roles in the RF exposure damage to the developing pubertal testis. Collectively, our findings provide a better understanding of the mechanisms underlying the toxic effects of RF exposure on testicular development.

As an increasing number of unmanned aerial vehicles (UAVs) have been widely applied in many aspects, controllers with higher performance are preferred. In this paper, a new varying-parameter adaptive multi-layer neural dynamic based controller (termed as VP-AMND controller) design method is proposed and applied to controllers of multi-rotor UAVs. First, a varying-parameter convergent neural dynamic (VP-CND) based controller is proposed and its convergence and robustness are theoretically proven. Second, by incorporating the adaptive control method into the VP-CND controller, the VP-AMND controller design method is proposed, of which the global stability, fast convergence speed and strong robustness can be guaranteed. Different from traditional triple zeroing dynamic (TZD) and VP-CND controllers, the proposed VP-AMND controller with self-tuning rates can estimate the unknown disturbances and enhance the stability of the system in the face of uncertainty. Third, computer simulation results verify that the multi-rotor UAVs with VP-AMND controllers can track time-varying trajectories quickly and solve the parameter uncertainty and disturbances problems effectively.

Aneuploidy is a hallmark of genomic instability that leads to tumor initiation, progression, and metastasis. CDC20, Bub1, and Bub3 form the mitosis checkpoint complex (MCC) that binds the anaphase-promoting complex or cyclosome (APC/C), a crucial factor of the spindle assembly checkpoint (SAC), to ensure the bi-directional attachment and proper segregation of all sister chromosomes. However, just how MCC is regulated to ensure normal mitosis during cellular division remains unclear. In the present study, we demonstrated that LNC CRYBG3, an ionizing radiation-inducible long noncoding RNA, directly binds with Bub3 and interrupts its interaction with CDC20 to result in aneuploidy. The 261–317 (S3) residual of the LNC CRYBG3 sequence is critical for its interaction with Bub3 protein. Overexpression of LNC CRYBG3 leads to aneuploidy and promotes tumorigenesis and metastasis of lung cancer cells, implying that LNC CRYBG3 is a novel oncogene. These findings provide a novel mechanistic basis for the pathogenesis of NSCLC after exposure to ionizing radiation as well as a potential target for the diagnosis, treatment, and prognosis of an often fatal disease.

Two common methods exist for solving indoor autonomous navigation and obstacle-avoidance problems using monocular vision: the traditional simultaneous localization and mapping (SLAM) method, which requires complex hardware, heavy calculations, and is prone to errors in low texture or dynamic environments; and deep-learning algorithms, which use the fully connected layer for classification or regression, resulting in more model parameters and easy over-fitting. Among the latter ones, the most advanced indoor navigation algorithm divides a single image frame into multiple parts for prediction, resulting in doubled reasoning time. To solve these problems, we propose a multi-task deep network based on feature map region division for monocular indoor autonomous navigation. We divide the feature map instead of the original image to avoid repeated information processing. To reduce model parameters, we use convolution instead of the fully connected layer to predict the navigable probability of the left, middle, and right parts. We propose that the linear velocity is determined by combining three prediction probabilities to reduce collision risk. Experimental evaluation shows that the proposed method is nine times smaller than the previous state-of-the-art methods; further, its processing speed and navigation capability increase more than five and 1.6 times, respectively.

SMADs, a family of proteins that function as signal transducers and transcriptional regulators to regulate various signaling pathways, including the transforming growth factor-β signaling pathway, are similar to the mothers against decapentaplegic family of genes and the sma gene family in Caenorhabditis elegans. SMADs generate context-dependent modulation by interacting with various sequence-specific transcription factors, such as E2F4/5, c-Fos, GATA3, YY1 and SRF, which have been found to serve a key role in lung carcinoma oncogenesis and progression. However, the prognostic values of the eight SMADs in lung cancer have not been fully understood. In the present study, the expression levels and survival data of SMADs in patients with lung carcinoma from the Oncomine, Gene Expression Profiling Interactive Analysis, Kaplan-Meier plotter and cBioPortal databases were downloaded and analyzed. It was found that the mRNA expression levels of SMAD-6, −7 and −9 were decreased in lung adenocarcinoma and squamous cell carcinoma compared with that in adjacent normal tissues, while there was no significant difference in SMADs 1–5. Survival analysis revealed that not only were low transcriptional levels of SMAD-6, −7 and −9 associated with low overall survival but they also had prognostic role for progression-free survival and post-progression survival (P

In this paper, a feedback control law based on two time‐scale assignment and state extension is implemented for the nonlinear non‐minimum phase dynamics of three degree‐of‐freedom autonomo...

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer with high recurrence and metastasis rates, and more than half of the patients diagnosed with NSCLC receive local radiotherapy. However, the intrinsic radio-resistance of cancer cells is a major barrier to effective radiotherapy for NSCLC. CRYBG3 is a long noncoding RNA (lncRNA) that was originally identified to be upregulated in NSCLC and enhanced metastasis of NSCLC cells by interacting with eEF1A1 to promote murine double minute 2 (MDM2) expression. The aims of this study were to reveal the contribution of CRYBG3 to the radioresistance of NSCLC and determine whether that is associated with MDM2-p53 pathway. Therefore, CRYBG3 was stably downregulated in A549 (wild-type p53) and H1299 (deficient p53) cells by infecting short hairpin RNA (shRNA) lentiviral particles. The results showed that downregulation of CRYBG3 increased DNA damage, enhanced apoptosis and pro-apoptotic protein expression in A549 or p53-overexpressed H1299 cells but not in H1299 or p53-silenced A549 cells after X-ray irradiation. However, the contribution of CRYBG3 to radioresistance was abolished by eEF1A1 or MDM2 knockdown in A549 cells. Thus, we concluded that downregulation of CRYBG3 enhanced radiosensitivity by reducing MDM2 expression then leading to decreased MDM2-mediated degradation of p53 in wild-type p53 expressing NSCLC cells. These findings suggested that CRYBG3 can be a potential target for therapeutic intervention of certain lung cancer subtypes.

At present, most feature-based SLAM methods are based on feature points. But in indoor environments where texture is scarce and light varies greatly, these methods will reduce the accuracy of pose estimation or even lead to pose tracking failure. To tackle the above problem, we propose an improved RGB-D SLAM method which employs point-line-plane feature extraction and matching as well as Manhattan Frames-based rotation estimation. Finally, the proposed method is proven to have good reliability and robustness in open datasets and real indoor scenes.

In this paper, we extended video stitching to nearshore bathymetry for videos that were captured for the same coastal field simultaneously by two unmanned aerial vehicles (UAVs). In practice, a video captured by a single UAV often shows a limited coastal zone with a lack of a wide field of view. To solve this problem, we proposed a framework in which video stitching and bathymetric mapping were performed in sequence. Specifically, our method listed the video acquisition strategy and took two overlapping videos captured by two UAVs as inputs. Then, we adopted a unified video stitching and stabilization optimization to compute the stitching and stabilization of one of the videos separately. In this way, we can obtain the best stitching result. At the same time, background feature points identification on the shore plays the role of short-time visual odometry. Through the obtained panoramic video in Shuang Yue Bay, China, we used the temporal cross-correlation analysis based on the linear dispersion relationship to estimate the water depth. We selected the region of interest (ROI) area from the panoramic video, performed an orthorectification transformation and extracted time-stack images from it. The wave celerity was then estimated from the correlation of the signal through filtering processes. Finally, the bathymetry results were compared with the cBathy. By applying this method to two UAVs, a wider FOV was created and the surveying area was expanded, which provided effective input data for the bathymetry algorithms.

Research on batteries’ State of Charge (SOC) estimation for equivalent circuit models based on the Kalman Filter (KF) framework and machine learning algorithms remains relatively limited. Most studies are focused on a few machine learning algorithms and do not present comprehensive analysis and comparison. Furthermore, most of them focus on obtaining the state space parameters of the Kalman filter frame algorithm models using machine learning algorithms and then substituting the state space parameters into the Kalman filter frame algorithm to estimate the SOC. Such algorithms are highly coupled, and present high complexity and low practicability. This study aims to integrate machine learning with the Kalman filter frame algorithm, and to estimate the final SOC by using different combinations of the input, output, and intermediate variable values of five Kalman filter frame algorithms as the input of the machine learning algorithms of six main streams. These are: linear regression, support vector Regression, XGBoost, AdaBoost, random forest, and LSTM; the algorithm coupling is lower for two-way parameter adjustment and is not applied between the machine learning and Kalman filtering framework algorithms. The results demonstrate that the integrated learning algorithm significantly improves the estimation accuracy when compared to the pure Kalman filter framework or the machine learning algorithms. Among the various integrated algorithms, the random forest and Kalman filter framework presents the highest estimation accuracy along with good real-time performance. Therefore, it can be implemented in various engineering applications.

Yaw control is significant to the attitude control of small unmanned helicopters (SUHs). Since the existing robust control method cannot be applied to the SUH with unknown dynamics and disturbances, this paper proposes an improved active disturbance rejection control (IADRC) to solve the problem. The IADRC obtains the optimal solution of the actuator gain ([Formula: see text]) by gradient descent. Besides, this paper summarizes some experiences during the tuning process of ADRC, which significantly reduces the difficulty of designing ADRC. Finally, the experimental results show that the proposed method is better than the traditional PID in robust and tracking control performance.

This paper proposes a model predictive control (MPC) approach for ducted fan aerial robots using physics-informed machine learning (ML), where the task is to fully exploit the capabilities of the predictive control design with an accurate dynamic model by means of a hybrid modeling technique. For this purpose, an indigenously developed ducted fan miniature aerial vehicle with adequate flying capabilities is used. The physics-informed dynamical model is derived offline by considering the forces and moments acting on the platform. On the basis of the physics-informed model, a data-driven ML approach called adaptive sparse identification of nonlinear dynamics is utilized for model identification, estimation, and correction online. Thereafter, an MPC-based optimization problem is computed by updating the physics-informed states with the physics-informed ML model at each step, yielding an effective control performance. Closed-loop stability and recursive feasibility are ensured under sufficient conditions. Finally, a simulation study is conducted to concisely corroborate the efficacy of the presented framework.

Background Pancreatic ductal adenocarcinoma (PDAC) is a devastatingly malignant tumor with a high mortality. However, current strategies to treat PDAC generally have low efficacy and high side-effects, therefore, effective treatment against PDAC remains an urgent need. Results We report a semiconducting polymer nano-radiopharmaceutical with intrinsic photothermal capability and labeling with therapeutic radioisotope 177Lu (177Lu-SPN-GIP) for combined radio- and photothermal therapy of pancreatic tumor. 177Lu-SPN-GIP endowed good stability at physiological conditions, high cell uptake, and long retention time in tumor site. By virtue of combined radiotherapy (RT) and photothermal therapy (PTT), 177Lu-SPN-GIP exhibited enhanced therapeutic capability to kill cancer cells and xenograft tumor in living mice compared with RT or PTT alone. More importantly, 177Lu-SPN-GIP could suppress the growth of the tumor stem cells and reverse epithelial mesenchymal transition (EMT), which may greatly reduce the occurrence of metastasis. Conclusion Such strategy we developed could improve therapeutic outcomes over traditional RT as it is able to ablate tumor with relatively lower doses of radiopharmaceuticals to reduce its side effects. Graphical abstract

Background Exposure to the ionizing radiation (IR) encountered outside the magnetic field of the Earth poses a persistent threat to the reproductive functions of astronauts. The potential effects of space IR on the circadian rhythms of male reproductive functions have not been well characterized so far. Methods Here, we investigated the circadian effects of IR exposure (3 Gy X-rays) on reproductive functional markers in mouse testicular tissue and epididymis at regular intervals over a 24-h day. For each animal, epididymis was tested for sperm motility, and the testis tissue was used for daily sperm production (DSP), testosterone levels, and activities of testicular enzymes (glucose-6-phosphate dehydrogenase (G6PDH), sorbitol dehydrogenase (SDH), lactic dehydrogenase (LDH), and acid phosphatase (ACP)), and the clock genes mRNA expression such as Clock, Bmal1, Ror-α, Ror-β, or Ror-γ. Results Mice exposed to IR exhibited a disruption in circadian rhythms of reproductive markers, as indicated by decreased sperm motility, increased daily sperm production (DSP), and reduced activities of testis enzymes such as G6PDH, SDH, LDH, and ACP. Moreover, IR exposure also decreased mRNA expression of five clock genes (Clock, Bmal1, Ror-α, Ror-β, or Ror-γ) in testis, with alteration in the rhythm parameters. Conclusion These findings suggested potential health effects of IR exposure on reproductive functions of male astronauts, in terms of both the daily overall level as well as the circadian rhythmicity.

Long noncoding RNAs (lncRNAs) were identified rapidly due to their important role in many biological processes and human diseases including cancer. 1α,25-dihydroxyvitamin D3 [1α,25(OH)2D3] and its analogues are widely applied as preventative and therapeutic anticancer agents. However, the expression profile of lncRNAs regulated by 1α,25(OH)2D3 in ovarian cancer remains to be clarified. In the present study, we found 606 lncRNAs and 102 mRNAs that showed differential expression (DE) based on microarray data. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the DE genes were mainly enriched in TGF-β, MAPK, Ras, PI3K-Akt, and Hippo signaling pathways, as well as the vitamin D-related pathway. We further assessed the potential lncRNAs that linked vitamin D signaling with EMT, and lncBCAS1-4_1 was identified in the first time. Moreover, we found that the most upregulated lncBCAS1-4_1 showed 75% same transcripts with CYP24A1 (metabolic enzyme of 1α,25(OH)2D3). Finally, the lncBCAS1-4_1 gain-of-function cell model was established, which demonstrated that the knockdown of lncBCAS1-4_1 inhibited the proliferation and migration of ovarian cancer cells. Furthermore, lncBCAS1-4_1 could resist the antitumor effect of 1α,25(OH)2D3, which was associated with upregulated ZEB1. These data provide new evidences that lncRNAs served as a target for the antitumor effect of 1α,25(OH)2D3.

In this paper, we focus on the transition control of a ducted fan vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). To achieve a steady transition from hover to high-speed flight, a neural-networks-based controller is proposed to learn the system dynamics and compensate for the tracking error between the aircraft dynamics and the desired dynamic performance. In prior, we derive the nonlinear system model of the aircraft full-envelope dynamics. Then, we propose a novel neural-networks-based control scheme and apply it on the underactuated aircraft system. Key features of the proposed controller consist of projection operator, state predictor and dynamic-formed adaptive input. It is proved and guaranteed that the tracking errors of both state predictor and neural-networks weights are upper bounded during the whole neural-networks learning procedure. The very adaptive input is formed into a dynamic structure that helps achieve a reliable fast convergence performance of the proposed controller, especially in high-frequency disturbance conditions. Consequently, the closed-loop system of the aircraft is able to track a certain trajectory with desired dynamic performance. Satisfactory results are obtained from both simulations and practical flight test in accomplishing the designed flight course.

In this paper, a novel disturbance-observer-based approximate dynamic inversion (ADI) approach is developed for pure-feedback nonaffine-in-control nonlinear systems (PFNNSs) in the presence of both high-order mismatched disturbances and actuator saturation. Finite-time disturbance observers (FTDOs) are utilized to estimate the disturbances and their derivatives. Then, we rebuild the system with the outputs of FTDOs. Thereafter, ADI is employed to derive the desired virtual and actual control of the nominal system, where no disturbances and saturation are presented. Furthermore, an augmented intermediate subsystem is constructed for the reduced slow subsystem to compensate for the difference between inputs with and without saturation by approximating its inversion. The stability of the closed-loop system is studied using Tikhonov's theorem. The proposed method is applied to a numerical example and a one-link robotic system with a brush DC motor. The simulation results demonstrate the validity of the presented approach.

Relative pose estimation refers to estimate the relative attitude and translation between multiple platforms. For mobile platforms, tracking the relative pose with pairwise range is challenging for highly nonlinear associations between measurement and state. This article proposes a promising framework using pairwise range to estimate the relative pose parameterized with Lie algebra. It is compatible with the existing Gauss-Newton method and the Levenberg–Marquardt method. We analyze the existence of the optimal solution based on the rank of the Hessian matrix, which turns into a discussion of sensors placement. The associated unconstrained Cramer-Rao Lower Bound with fewer variables is presented. To track moving platforms, we derived a novel and accurate relative kinematics without angular accelerations. An extended Kalman filter incorporating the measurement of an IMU is designed to generate smooth poses. A simplified version of the optimizer with less dimension is introduced to the application of aerobridge, which is also compatible with other multilink devices. Simulations verify the proposed algorithm and the comparisons with the existing popular methods prove its novelty.

Introduction: The ratio of Ce3+/Ce4+ in their structure confers unique functions on cerium oxide nanoparticles (CeO2NPs) containing rare earth elements in scavenging free radicals and protecting against oxidative damage. The potential of CeO2NPs to protect testosterone synthesis in primary mouse Leydig cells during exposure to 1,800 MHz radiofrequency (RF) radiation was examined in vitro. Methods: Leydig cells were treated with different concentrations of CeO2NPs to identify the optimum concentration for cell proliferation. The cells were pretreated with the optimum dose of CeO2NPs for 24 hrs and then exposed to 1,800 MHz RF at a power density of 200.27 µW/cm2 (specific absorption rate (SAR), 0.116 W/kg) for 1 hr, 2 hrs, or 4 hrs. The medium was used to measure the testosterone concentration. The cells were collected to determine the antioxidant indices (catalase [CAT], malondialdehyde [MDA], and total antioxidant capacity [T-AOC]), and the mRNA expression of the testosterone synthase genes (Star, Cyp11a1, and Hsd-3β) and clock genes (Clock, Bmal1, and Rorα). Results: Our preliminary result showed that 128 μg/mL CeO2NPs was the optimum dose for cell proliferation. Cells exposed to RF alone showed reduced levels of testosterone, T-AOC, and CAT activities, increased MDA content, and the downregulated genes expression of Star, Cyp11a1, Hsd-3β, Clock, Bmal1, and Rorα. Pretreatment of the cells with 128 μg/mL CeO2NPs for 24 hrs followed by RF exposure significantly increased testosterone synthesis, upregulated the expression of the testosterone synthase and clock genes, and increased the resistance to oxidative damage in Leydig cells compared with those in cells exposed to RF alone. Conclusion: Exposure to 1,800 MHz RF had adverse effects on testosterone synthesis, antioxidant levels, and clock gene expression in primary Leydig cells. Pretreatment with CeO2NPs prevented the adverse effects on testosterone synthesis induced by RF exposure by regulating their antioxidant capacity and clock gene expression in vitro. Further studies of the mechanism underlying the protective function of CeO2NPs against RF in the male reproductive system are required.

Aim Autophagy is a self-protective process, and it confers cancer cells resistance against radio-chemotherapeutics. To induce cancer cell death, a series of compounds of 3-((4-((7-chloroquinolin-4-yl)amino)butyl)amino)-7-substituted benzo[e][1,2,4]triazine 1-oxide or CQBTO containing two critical chemical groups were designed and synthesized. One compound, benzo[e][1,2,4]triazine 1-oxide, yielded free radicals to trigger autophagy, and the other one, chloroquine (CQ), was an inhibitor of autophagy. We hypothesized that the compounds could kill cancer cells effectively by inducing incomplete autophagy. Methods In vitro cultured non-small cell lung carcinoma cells and primary lung tumors in mice in vivo were used to test the lethal effects of CQBTO on cancer cells and toxicity to normal tissues. Cell viability was examined using the CCK8 assay. Genomic instability was determined with the cytochalasin B-blocked micronucleus assay. Cell cycle distribution was analyzed by propidium iodide staining and flow cytometry. Western blotting and immunofluorescence were used to detect the induction and localization of LC3, a biomarker for autophagy. Results Compared with CQ, three CQBTO compounds were lethal to lung cancer cells, and CQBTO-3 was the most effective. The LD50 for CQBTO-3 was 21 μΜ in A549 cells and 21.5 μΜ in Calu-1 cells, which was lower than that of CQBTO-2 or CQBTO-1. Induction of LC3 foci and an increase in the LC3II/LC3I ratio demonstrated the induction of autophagy by CQBTO-3 in A549 cells, whereas no obvious micronuclei or cell cycle arrest was observed. No detectable toxicity to normal mice was observed. CQBTO-3 improved the quality of mouse life, reduced the number and size of existing tumors, and suppressed tumor formation. Conclusion CQBTO-3 is a potential chemical compound for lung cancer treatment.

In this paper, we focus on the hover/high-speed-cruising transition flight control of a ducted fan unmanned aerial vehicle (UAV), in which saturation on control surfaces is taken into consideration. To achieve full-envelope flight control, we propose an adaptive cascaded structure on the velocity tracking control for the nonlinear aircraft system. By introducing adaptive neural network, the closed-loop system tracks a prescribed reference system with all the nonlinear tracking errors well canceled. In the meanwhile, to tackle the control surface saturation, we propose a control effectiveness enhancement (CEE) algorithm in attaining sufficient control moment to the ducted fan vehicle. Under the proposed cascaded adaptive control scheme in conjunction with the CEE algorithm, the state tracking performance is proved to be bounded when encountering surface saturation. Finally, comparison experiments in terms of the forward and backward flight transition are conducted on the ducted fan UAV. The results are satisfactory and convincing in exhibiting the flight transition course that demonstrates the effectiveness of the proposed method.

To handle the problems of point cloud data quantity explosion and urgent need for real-time access to massive point cloud data in collecting and storing phase, this paper proposes an index technology with internal and external storage interactions, which is based on extended 3D grid in memory layer and B-tree in external storage layer. The experiment shows that it is efficient to retrieve massive point cloud data by using this double-layer index structure.

The autopilot of aerobridge has attracted considerable attention recently since human error and high labor costs. To estimate the pose of an aerobridge, this paper proposes a navigation algorithm with the aid of pairwise distances and inertial measurements. We first locate the joints of the aerobridge with the distance measurement. Then the pose of the aerobridge is calculated using its kinematic in closed-form and the associated covariance is derived. A dual quaternion based multiplicative extended Kalman filter is designed to fuse inertial measurements with the estimated pose, which produces a smooth pose. To verify the precision of this algorithm, a simulation is constructed based on Robotics Toolbox and the experiment results are positive.

In this paper, we generalize the Extended Kalman Filter algorithm on Lie Groups for the attitude propagation and update of the unmanned helicopters. The data noises obtained by sensors are assumed to be independent as Gaussian distributions, and the posterior distribution of the estimated state is also Gaussian distribution. The attitude of the drone can be obtained from the accelerometers, the gyroscopes, and the magnetometers. These three sensors need to be fused to obtain the optimal estimated value. Among them, the method of multiple sub-samples is used to compensate for the error generated by the long sampling period of the gyroscope, and we use a Chi-square detection method to determine whether the observation information is valid during the updating process.

At present, the main methods of solving the monocular depth estimation for indoor drones are the simultaneous localization and mapping (SLAM) algorithm and the deep learning algorithm. SLAM requires the construction of a depth map of the unknown environment, which is slow to calculate and generally requires expensive sensors, whereas current deep learning algorithms are mostly based on binary classification or regression. The output of the binary classification model gives the decision algorithm relatively rough control over the unmanned aerial vehicle. The regression model solves the problem of the binary classification, but it carries out the same processing for long and short distances, resulting in a decline in short-range prediction performance. In order to solve the above problems, according to the characteristics of the strong order correlation of the distance value, we propose a non-uniform spacing-increasing discretization-based ordinal regression algorithm (NSIDORA) to solve the monocular depth estimation for indoor drone tasks. According to the security requirements of this task, the distance label of the data set is discretized into three major areas&mdash, the dangerous area, decision area, and safety area&mdash, and the decision area is discretized based on spacing-increasing discretization. Considering the inconsistency of ordinal regression, a new distance decoder is produced. Experimental evaluation shows that the root-mean-square error (RMSE) of NSIDORA in the decision area is 33.5% lower than that of non-uniform discretization (NUD)-based ordinal regression methods. Although it is higher overall than that of the state-of-the-art two-stream regression algorithm, the RMSE of the NSIDORA in the top 10 categories of the decision area is 21.8% lower than that of the two-stream regression algorithm. The inference speed of NSIDORA is 3.4 times faster than that of two-stream ordinal regression. Furthermore, the effectiveness of the decoder has been proved through ablation experiments.

In this paper, we address the hover-to-cruise transition control problem for high-speed level flight of a ducted fan UAV. Firstly, the nonlinear aircraft dynamic system is derived and relevant key aerodynamic characteristics are presented. Then, we divide the dynamic system into known parts and uncertain parts. In order to track a reference system formed by the known parts, we propose a neural-network-based control strategy to learn the error dynamics and cancel the tracking error with proved stability. Finally, simulations and experiments are conducted to perform the transition process at fixed altitude and constant horizontal acceleration, with satisfactory results accomplishing the designed flight course.

In this paper, it is supposed that the linearized water wave equation (LWWE) can describe the movement of water waves. Utilizing the identification algorithm we put forward in this paper, the depth of water, the elevation of the wave surface, and the velocity potential can be calculated from the measured velocity of surface water waves at the same time. We show the estimability by proving the mapping from the depth of water, the elevation of wave surface and the velocity potential to the measurement is injective. Moreover, we verify our proposed algorithm, which is on the basis of the classical gradient descent method, with some numerical simulations.

This paper presents new techniques for construction of dynamic output feedback controllers with a static anti-windup compensator for a class of saturated systems with sector-bounded and slope-restricted nonlinearities. To handle the slope restriction, a so-called differential compensator is incorporated into the traditional nonlinear controller. Considering its sector-bounded property and convex-hull representation, respectively, two LMI-based synthesis methods are proposed to address the problems of the estimates of the region of attraction (ROA) and ${{{\mathcal{L}}}_2}$ performance analysis of the closed-loop system. A numerical example illustrates the effectiveness of the proposed approaches.

Battery management is the key technical link for electric vehicles. A good battery management system can realize the balanced charge and discharge of batteries, reducing the capacity degradation and the loss of health caused by battery overcharge and discharge, which all depend on the real-time and accurate estimation of the battery’s state of charge (SOC). However, the battery’s SOC has highly complex nonlinear time-varying characteristics related to the complex chemical and physical state and dynamic environmental conditions, which are difficult to measure directly, and this has become a difficulty in design and research. According to the characteristics of ternary lithium-ion batteries of electric vehicles, a battery SOC dual estimation algorithm based on the Variable Forgetting Factor Recursive Least Square (VFFRLS) and Multi-Innovation Unscented Kalman Filter (MIUKF) is proposed in this paper. The VFFRLS algorithm is used to estimate battery model parameters, and the MIUKF algorithm is used to estimate the battery’s SOC in real time. The two algorithms are coupled to update battery model parameters and estimate the SOC. The experiment results show that the algorithm has high accuracy and stability.

Lung cancer is one of the highest health risks caused by ionizing radiation, which induces both direct effects and non-targeted effects. However, whether radiation-induced non-targeted effects result in epithelial-mesenchymal transition, a critical process during tumorigenesis, in non-targeted lung tissues remains unknown. In the present study, Kunming mice were subjected to whole-body, cranial or local abdominal irradiation of single-dose or fractionated 4 Gy X-rays, and the expressions of epithelial-mesenchymal transition markers in non-targeted lung tissues were assessed by both qRT-PCR and immunofluorescent staining. It was found that the epithelial marker was downregulated while the mesenchymal markers were upregulated significantly in non-targeted lung tissues of the irradiated mice. Local abdominal irradiation was more efficient in inducing epithelial-mesenchymal transition than whole-body or cranial irradiation when the fractionated irradiation method was adopted. In addition, the intraperitoneal administration of celecoxib suppressed epithelial-mesenchymal transition in the non-targeted lung tissues. In conclusion, our findings suggest that epithelial-mesenchymal transition is induced in non-targeted lung tissues, but can be suppressed by inhibition of cyclooxygenase-2 by celecoxib.

This paper presents a new recursive identification method which can efficiently estimate time-varying parameters in discrete time systems and has significant advantages over standard recursive leas...