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For special tail sitter configurations such as the ducted fan tail sitter unmanned aerial vehicle (UAV), the widely used trajectory planning methodology based on differential flatness might not be applicable due to complex aerodynamic coupling effects. As a result, the flight mode transition remains a challenging task. In this paper, we address the time optimal altitude-hold flight mode transition issue for a class of ducted fan tail sitter UAV. The foundation of the framework is the dynamic transition corridor in which the limitation of jerk is particularly considered, aiming to thoroughly reflect the dynamic feature of aggressive maneuvers. Based on this, we propose a time optimal strategy to generate feasible altitude-hold transition trajectories. Simultaneous, by fully utilizing the manifestation of time optimal altitude-hold flight behavior revealed by the transition corridor, we try to tackle the time optimal altitude-hold transition by means of a novel model-free control scheme. Comparative simulations show that both of the transition strategies achieve satisfactory performance on time optimal altitude-hold transition in the absence of disturbance, while the model-free control scheme exhibits better robustness under external disturbance.

Bacteria-mediated anti-tumor therapy has received widespread attention due to its natural tumor-targeting ability and specific immune-activation characteristics. It has made significant progress in breaking the limitations of monotherapy and effectively eradicating tumors, especially when combined with traditional therapy, such as radiotherapy. According to their different biological characteristics, bacteria and their derivatives can not only improve the sensitivity of tumor radiotherapy but also protect normal tissues. Moreover, genetically engineered bacteria and bacteria-based biomaterials have further expanded the scope of their applications in radiotherapy. In this review, we have summarized relevant researches on the application of bacteria and its derivatives in radiotherapy in recent years, expounding that the bacteria, bacterial derivatives and bacteria-based biomaterials can not only directly enhance radiotherapy but also improve the anti-tumor effect by improving the tumor microenvironment (TME) and immune effects. Furthermore, some probiotics can also protect normal tissues and organs such as intestines from radiation via anti-inflammatory, anti-oxidation and apoptosis inhibition. In conclusion, the prospect of bacteria in radiotherapy will be very extensive, but its biological safety and mechanism need to be further evaluated and studied.

Medical image diagnosis using deep learning has shown significant promise in clinical medicine. However, it often encounters two major difficulties in real-world applications: (1) domain shift, which invalidates the trained model on new datasets, and (2) class imbalance problems leading to model biases towards majority classes. To address these challenges, this paper proposes a transfer learning solution, named Dynamic Weighting Translation Transfer Learning (DTTL), for imbalanced medical image classification. The approach is grounded in information and entropy theory and comprises three modules: Cross-domain Discriminability Adaptation (CDA), Dynamic Domain Translation (DDT), and Balanced Target Learning (BTL). CDA connects discriminative feature learning between source and target domains using a synthetic discriminability loss and a domain-invariant feature learning loss. The DDT unit develops a dynamic translation process for imbalanced classes between two domains, utilizing a confidence-based selection approach to select the most useful synthesized images to create a pseudo-labeled balanced target domain. Finally, the BTL unit performs supervised learning on the reassembled target set to obtain the final diagnostic model. This paper delves into maximizing the entropy of class distributions, while simultaneously minimizing the cross-entropy between the source and target domains to reduce domain discrepancies. By incorporating entropy concepts into our framework, our method not only significantly enhances medical image classification in practical settings but also innovates the application of entropy and information theory within deep learning and medical image processing realms. Extensive experiments demonstrate that DTTL achieves the best performance compared to existing state-of-the-art methods for imbalanced medical image classification tasks.

In the human genome, 98% of genes can be transcribed into non-coding RNAs (ncRNAs), among which lncRNAs and their encoded peptides play important roles in regulating various aspects of cellular processes and may serve as crucial factors in modulating the biological effects induced by ionizing radiation and microgravity. Unfortunately, there are few reports in space radiation biology on lncRNA-encoded peptides below 10kD due to limitations in detection techniques. To fill this gap, we integrated a variety of methods based on genomics and peptidomics, and discovered 22 lncRNA-encoded small peptides that are sensitive to space radiation and microgravity, which have never been reported before. We concurrently validated the transmembrane helix, subcellular localization, and biological function of these small peptides using bioinformatics and molecular biology techniques. More importantly, we found that these small peptides function independently of the lncRNAs that encode them. Our findings have uncovered a previously unknown human proteome encoded by 'non-coding' genes in response to space conditions and elucidated their involvement in biological processes, providing valuable strategies for individual protection mechanisms for astronauts who carry out deep space exploration missions in space radiation environments.

Deep learning has achieved remarkable progress in medical image analysis, but its effectiveness heavily relies on large-scale and well-annotated datasets. However, assembling a large-scale dataset of annotated histopathological images is challenging due to their unique characteristics, including various image sizes, multiple cancer types, and staining variations. Moreover, strict data privacy in medicine severely restricts data sharing and poses significant challenges in acquiring large-scale and well-annotated histopathological images. To tackle these constraints, Transfer Learning (TL) provides a promising solution by exploiting knowledge from another domain. This study proposes the Uncertainty-guided asymmetric Consistency Domain Adaptation (UCDA), which does not require accessing the source data and is composed of two essential components, e.g., Uncertainty-guided Source-free Transfer Learning (USTL) and Asymmetric Consistency Learning (ACL). In detail, USTL facilitates a secure mapping of the source domain model’s feature space onto the target domain, eliminating the dependency on source domain data to protect data privacy. At the same time, the ACL module measures the symmetry and asymmetry between the source and target domains, bridging the information gap and preserving inter-domain differences among medical images. We comprehensively evaluate the effectiveness of UCDA on three widely recognized and publicly available datasets, namely NCTCRC-HE-100K, PCam, and LC25000. Impressively, our proposed method achieves remarkable performance on accuracy and F1-scores. Additionally, feature visualizations effectively demonstrate the exceptional generalizability and discriminative power of the learned representations. These compelling results underscore the significant potential of UCDA in driving the advancement of deep learning techniques within the realm of histopathological image analysis.

In the near future, urban air mobility (UAM) will let an old dream of human society come true: affordable and fast air transportation for almost everyone. Among the various existing designs, the multicopter configuration best combines the advantages of compactness, simplicity, and maturity. These aspects are important for actual use, particularly during the early stage of this market. This study elaborates on the design principles of UAM multicopters by examining existing models in terms of their configuration, weight, and range specifications. In particular, the weights of the different components are estimated based on empirical models, aerodynamic fundamentals for the analysis of UAM multicopters are derived from momentum theory, and the power and energy requirements for hovering and cruise flight are evaluated, thereby enabling estimation of the maximum hovering time and flight range. Finally, a sizing method is introduced and validated against an actual UAM design.

Abstract Long noncoding RNAs (lncRNAs) in eukaryotic transcripts have long been believed to regulate various aspects of cellular processes, including carcinogenesis. Herein, it is found that lncRNA AFAP1‐AS1 encodes a conserved 90‐amino acid peptide located on mitochondria, named lncRNA AFAP1‐AS1 translated mitochondrial‐localized peptide (ATMLP), and it is not the lncRNA but the peptide that promotes the malignancy of nonsmall cell lung cancer (NSCLC). As the tumor progresses, the serum level of ATMLP increases. NSCLC patients with high levels of ATMLP display poorer prognosis. Translation of ATMLP is controlled by m6A methylation at the 1313 adenine locus of AFAP1‐AS1. Mechanistically, ATMLP binds to the 4‐nitrophenylphosphatase domain and non‐neuronal SNAP25‐like protein homolog 1 (NIPSNAP1) and inhibits its transport from the inner to the outer mitochondrial membrane, which antagonizes the NIPSNAP1‐mediated regulation of cell autolysosome formation. The findings uncover a complex regulatory mechanism of NSCLC malignancy orchestrated by a peptide encoded by a lncRNA. A comprehensive judgment of the application prospects of ATMLP as an early diagnostic biomarker for NSCLC is also made.

This paper is concerned with the attitude tracking problem of quadrotor unmanned aerial vehicles (UAVs) with respect to endogenous uncertainties, exogenous disturbances and actuator failures. Two different control methods are proposed to solve this problem. First, an adaptive extended state observer (AESO)-based control framework is devised to tackle the difficulties caused by model uncertainties and external disturbances. A fault-tolerant control method is proposed to cope with the occurrence of actuator failure, which is modeled as a constant loss of effectiveness. Another method employs AESOs to compensate for lumped disturbances, which include endogenous uncertainties, exogenous disturbances and actuator failures. Then, the error can exponentially converge to a bounded set. Finally, simulations are performed to ensure the feasibility of the designed technique.

Abstract Mechanotransduction sensing of tissue architecture and cellular microenvironment is a fundamental regulator of cell fate, including cancer. Meanwhile, long noncoding RNAs (lncRNAs) play multifunctions during cancer development and treatment. However, the link between lncRNAs and cellular mechanotransduction in the context of cancer progression has not yet been elucidated. In this study, using atomic force microscopy (AFM), we find that ionizing radiation reduces tumor stiffness. Ionizing radiation-induced lncRNA CRYBG3 can blunt YAP/TAZ activity through interference with mechanotransduction, resulting in the inhibition of cell proliferation, invasion, and metastasis of lung cancer cells. In vivo, we found that loss of lncRNA CRYBG3 could power the tumor initiation and metastasis ability, but this was abolished by concomitant deplete TAZ. At the molecular level, lncRNA CRYBG3 that in turn dysregulates F-actin organization, activates the LATS1/2 kinase, all in all resulting in YAP/TAZ nuclear exclusion. Our research proposes that lncRNA CRYBG3 is a mediator of radiotherapy through its control of cancer-tissue mechanotransduction and wiring YAP/TAZ activity to control tumor growth and metastasis.

Diabetic retinopathy (DR) is a common complication of diabetes, which can lead to vision loss. Early diagnosis is crucial to prevent the progression of DR. In recent years, deep learning approaches have shown promising results in the development of an intelligent and efficient system for DR classification. However, one major drawback is the need for expert-annotated datasets, which are both time-consuming and costly. To address these challenges, this paper proposes a novel dynamic graph clustering learning (DGCL) method for unsupervised classification of DR, which innovatively deploys the Euclidean and topological features from fundus images for dynamic clustering. Firstly, a multi-structural feature fusion (MFF) module extracts features from the structure of the fundus image and captures topological relationships among multiple samples, generating a fused representation. Secondly, another consistency smoothing clustering (CSC) module combines network updates and deep clustering to ensure stability and smooth performance improvement during model convergence, optimizing the clustering process by iteratively updating the network and refining the clustering results. Lastly, dynamic memory storage is utilized to track and store important information from previous iterations, enhancing the training stability and convergence. During validation, the experimental results with public datasets demonstrated the superiority of our proposed DGCL network.

Abstract 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

In this paper, we propose a novel affine iterative closest point algorithm based on color information and correntropy, which can effectively deal with the registration problems with a large number of noise and outliers and small deformations in RGB-D datasets. Firstly, to alleviate the problem of low registration accuracy for data with weak geometric structures, we consider introducing color features into traditional affine algorithms to establish more accurate and reliable correspondences. Secondly, we introduce the correntropy measurement to overcome the influence of a large amount of noise and outliers in the RGB-D datasets, thereby further improving the registration accuracy. Experimental results demonstrate that the proposed registration algorithm has higher registration accuracy, with error reduction of almost 10 times, and achieves more stable robustness than other advanced algorithms.

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.

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.

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, 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.

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.

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.

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.

Background/Aims: TGF-β1 mediated radiation-induced bystander effects (RIBE) have been linked with malignant transformation and tumorigenesis. However, the underlying mechanisms are not fully understood. Methods: To reveal new molecules of regulatory functions in this process, lncRNA microarray was performed to profile both lncRNA and mRNA expression patterns in human lung bronchial epithelial BEAS-2B cells treated with TGF-β1 at a concentration measured in the medium conditioned by directly irradiated BEAS-2B cells. The potential functions of the differentially expressed lncRNAs were predicted by GO and KEGG pathway analyses of their co-expressed mRNAs. Cis- and trans-regulation of the lncRNAs were analyzed and the interaction networks were constructed using Cytoscape. qRT-PCR was conducted to validate the results of microarray profiling. CCK-8 assay was employed for functional validation of 3 identified lncRNAs. Results: 224 lncRNAs were found to be dysregulated, among which 6 lncRNAs were chosen for expression validation by qRT-PCR assay. Pathway analyses showed that differentially expressed lncRNAs are highly correlated with cell proliferation, transformation, migration, etc. Trans-regulation analyses showed that the differentially expressed lncRNAs most likely participate in the pathways regulated by four transcriptional factors, FOS, STAT3, RAD21 and E2F1, which have been identified to be involved in the modulation of oncogenic transformation, cell cycle progression, genomic instability, etc. lnc-THEMIS-2 and lnc-ITGB6-4, predicted to be regulated by STAT3 and E2F1 respectively, were found to rescue the decrease of cell viability induced by TGF-β1 treatment. Conclusion: Our findings suggest that the differentially expressed lncRNAs induced by TGF-β1 play crucial roles in the oncogenic transformation and tumorigenesis, which provide a better understanding of the underlying mechanisms related to tumorigensis induced by LD/LDR radiations.

Multiple objects tracking is a challenging task. This article presents an algorithm which can detect and track multiple objects, and update target model automatically. The contributions of this paper as follow: Firstly,we also use color histogram(CH) and histogram of orientated gradients(HOG) to represent the objects, model update is realized by kalman filter and gaussian model; secondly we use Gaussian Mixture Model(GMM) and Bhattacharyya distance to detect object appearance. Particle filter with combined features and model update mechanism can improve tracking results. Experiments on video sequences demonstrate that the method presented in this paper can realize multiple objects detection and tracking.