Explore the vast range of titles available.

Background Growing evidence indicates that inflammasome-mediated inflammation plays important roles in the pathophysiology of amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD). Pyroptosis induced by inflammasome, and Gasdermin D (GSDMD) is involved in several neurodegenerative disorders. However, it is not clear whether peripheral inflammasome and pyroptosis are activated in aMCI and AD patients, influencing on neuroinflammation. The aim of this study was to examine the association between systemic inflammasome-induced pyroptosis and clinical features in aMCI and AD. Methods A total of 86 participants, including 33 subjects with aMCI, 33 subjects with AD, and 20 cognitively normal controls, in this study. The Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) scale were used for cognitive assessment. Levels of inflammasome-related genes/proteins in peripheral blood mononuclear cells (PBMCs) were determined using quantitative polymerase chain reaction and Western blotting. The levels of IL-1β, Aβ1-42, Aβ1-40, p-tau, and t-tau in cerebrospinal fluid (CSF), as well as the plasma IL-1β level, were measured by enzyme-linked immunosorbent assay. Finally, lipopolysaccharides (LPS) were used to investigate the effects of systemic inflammasome-induced pyroptosis in an AD mice model. Results Several genes involved in the inflammatory response were enriched in PBMCs of AD patients. The mRNA and protein levels of NLRP3, caspase-1, GSDMD, and IL-1β were increased in PBMCs of aMCI and AD patients. The IL-1β level in plasma and CSF of aMCI and AD patients was significantly higher than that in controls and negatively correlated with the CSF Aβ1-42 level, as well as MMSE and MoCA scores. Furthermore, there was a positive correlation between the IL-1β level in plasma and CSF of aMCI or AD patients. In vivo experiments showed that systemic inflammasome-induced pyroptosis aggravated neuroinflammation in 5 × FAD mice. Conclusions Our findings showed that canonical inflammasome signaling and GSDMD-induced pyroptosis were activated in PBMCs of aMCI and AD patients. In addition, the proinflammatory cytokine IL-1β was strongly associated with the pathophysiology of aMCI and AD. As such, targeting inflammasome-induced pyroptosis may be a new approach to inhibit neuroinflammation in aMCI and AD patients.

Uveal melanoma(UVM) is the most common intraocular malignancy and has a poor prognosis. The clinical significance of necroptosis(NCPS) in UVM is unclear. We first identified necroptosis genes in UVM by single-cell analysis of the GSE139829 dataset from the GEO database and weighted co-expression network analysis of TCGA data. COX regression and Lasso regression were used to construct the prognostic model. Then survival analysis, immune microenvironment analysis, and mutation analysis were carried out. Finally, cell experiments were performed to verify the role of ITPA in UVM. By necroptosis-related prognostic model, UVM patients in both TCGA and GEO cohorts could be classified as high-NCPS and low-NCPS groups, with significant differences in survival time between the two groups (P<0.001). Besides, the high-NCPS group had higher levels of immune checkpoint-related gene expression, suggesting that they might be more likely to benefit from immunotherapy. The cell experiments confirmed the role of ITPA, the most significant gene in the model, in UVM. After ITPA was knocked down, the activity, proliferation, and invasion ability of the MuM-2B cell line were significantly reduced. Our study can provide a reference for the diagnosis and treatment of patients with UVM.

This paper investigates the attitude tracking issue of the fixed-wing unmanned aerial vehicle (UAV) with model uncertainty and external disturbance using contemporary predefined-time stability theory and disturbance observer. Firstly, by using a time-varying tuning function with the expected settling time, composite state tracking errors are introduced to the design process of dynamic surface control, which is named dynamic surface based predefined time control (DSPTC). Then, the lumped disturbances are estimated by finite time extended states observer (FTESO). It is proved that all of signals in the aircraft attitude system are globally uniformly ultimately bounded. Finally, a set of simulations is implemented to validate the availability of the proposed controller.

Interval-valued linguistic neutrosophic sets (IVLNSs), Z-numbers, and the trapezium cloud model are powerful tools for expressing uncertainty and randomness. This paper aims to combine these methodologies. First, we review relevant concepts and operators, introducing a novel combination of IVLNSs and Z-numbers, which establishes a new form of expression. Subsequently, we propose the Z-interval-valued linguistic neutrosophic set-trapezium–trapezium cloud (Z-IVLNS-TTC) model, designed to minimize information loss and distortion during quantification. A novel method for calculating the objective weight vector is then developed using multi-objective programming (MOP). Drawing inspiration from the TOPSIS method, we propose a new approach for calculating the distance between Z-IVLNS-TTCs based on the p-norm. Finally, a group decision-making problem is presented to demonstrate the practical application of the proposed method. To validate the effectiveness and feasibility of the method, sensitivity analysis and comparisons with existing approaches are conducted.

Alzheimer's disease (AD) is characterized by disrupted brain connectivity, but the network changes across disease stages remain poorly understood. This observational cross-sectional study investigated alterations in functional brain networks across the AD continuum using minimum spanning tree (MST) analysis of resting-state EEG (rsEEG) data. We analyzed rsEEG data from 65 participants (30 healthy controls, 14 mild cognitive impairment due to AD [MCI-AD], 21 AD). Phase Lag Index (PLI)-based connectivity and MST metrics (such as diameter, eccentricity, and maximum degree) were computed across five frequency bands. Group differences were assessed using Kruskal-Wallis tests, and correlations with cognitive measures, disease severity, and cerebrospinal fluid (CSF) biomarkers were examined. Significant alterations in rsEEG network topology were observed across HC, MCI-AD, and AD groups. AD patients showed increased theta band connectivity (higher mean PLI, diameter, and eccentricity) and decreased beta band connectivity (lower mean PLI and eccentricity) compared to HC. MCI-AD group exhibited higher delta band maximum degree and altered beta band network organization compared to HC and AD. These network changes correlated with cognitive performance and disease severity. Beta band mean PLI and theta band eccentricity effectively discriminated between AD/MCI-AD and HC. Significant correlations were also found between specific MST metrics and CSF biomarkers (t-Tau, p-Tau, Aβ ). AD progression is characterized by frequency-specific alterations in brain network topology, particularly in theta and beta bands, detectable through rsEEG-based MST analysis. These findings suggest EEG-derived network measures may serve as potential biomarkers for early AD diagnosis and monitoring disease progression.

Unmanned aerial vehicles (UAVs) are becoming more widely used in various industries, raising growing concerns about their safety and reliability. The flight data of UAVs can directly reflect their flight health status; however, the rarity of abnormal flight data and the spatiotemporal characteristics of these data represent a significant challenge for constructing accurate and reliable anomaly detectors. To address this, this study proposes an anomaly detection framework that fully considers the temporal correlations and distribution characteristics of flight data. This framework first combines a one-dimensional convolutional neural network (1DCNN) with an autoencoder (AE) to establish a feature extraction model. This model leverages the feature extraction capabilities of the 1DCNN and the reconstruction capabilities of the AE to thoroughly extract the spatiotemporal features from UAV flight data. Then, to address the challenge of adaptive anomaly detection thresholds, this research proposes a nonlinear model of support vector data description (SVDD) utilizing a 0/1 soft-margin loss, referred to as L0/1-SVDD. This model replaces the traditional hinge loss function in SVDD with a 0/1 loss function, with the goal of enhancing the accuracy and robustness of anomaly detection. Since the 0/1 loss function is a bounded, non-convex, and non-continuous function, this paper proposes the Bregman ADMM algorithm to solve the L0/1-SVDD. Finally, the difference between the reconstructed and the actual value is employed to train the L0/1-SVDD, resulting in a hypersphere classifier that is capable of detecting UAV anomaly data. The experimental results using real flight data show that, compared with methods such as AE, LSTM, and LSTM-AE, the proposed method exhibits superior performance across five evaluation metrics.

Support vector data description (SVDD) is widely regarded as an effective technique for addressing anomaly detection problems. However, its performance can significantly deteriorate when the training data are affected by outliers or mislabeled observations. This study introduces a universal truncated loss function framework into the SVDD model to enhance its robustness and employs the fast alternating direction method of multipliers (ADMM) algorithm to solve various truncated loss functions. Moreover, the convergence of the fast ADMM algorithm is analyzed theoretically. Within this framework, we developed the truncated generalized ramp, truncated binary cross entropy, and truncated linear exponential loss functions for SVDD. We conducted extensive experiments on synthetic and real-world datasets to validate the effectiveness of these three SVDD models in handling data with different noise levels, demonstrating their superior robustness and generalization capabilities compared to other SVDD models.

The pathophysiology of keloid formation is not yet understood, so the identification of biomarkers for kelod can be one step towards designing new targeting therapies which will improve outcomes for patients with keloids or at risk of developing keloids. In this study, we performed single-cell RNA sequencing analysis, weighted co-expression network analysis, and differential expression analysis of keloids based on public databases. And 3 RNA sequencing data from keloid patients in our center were used for validation. Besides, we performed QRT-PCR on keloid tissue and adjacent normal tissues from 16 patients for further verification. We identified the sensitive biomarker of keloid: Tenascin-C (TNC). Then, Pseudotime analysis found that the expression level of TNC decreased first, then stabilized and finally increased with fibroblast differentiation, suggesting that TNC may play an potential role in fibroblast differentiation. In addition, there were differences in the infiltration level of macrophages M0 between the TNC-high group and the TNC-low group. Macrophages M0 had a higher infiltration level in low TNC- group (P<0.05). Our results can provide a new idea for the diagnosis and treatment of keloid.

With the improvement of UAV performance and intelligence in recent years, it is particularly important for unmanned aerial vehicles (UAVs) to improve the ability of autonomous air combat. Aiming to solve the problem of how to improve the autonomous air combat maneuver decision ability of UAVs so that it can be close to manual manipulation, this paper proposes an autonomous air combat maneuvering decision method based on the combination of simulated operation command and the final reward value deep deterministic policy gradient (FRV-DDPG) algorithm. Firstly, the six-degree-of-freedom (6-DOF) model is established based on the air combat process, UAV motion, and missile motion. Secondly, a prediction method based on the Particle swarm optimization radial basis function (PSO-RBF) is designed to simulate the operation command of the enemy aircraft, which makes the training process more realistic, and then an improved DDPG strategy is proposed, which returns the final reward value to the previous reward value in a certain proportion of time for offline training, which can improve the convergence speed of the algorithm. Finally, the effectiveness of the algorithm is verified by building a simulation environment. The simulation results show that the algorithm can improve the autonomous air combat maneuver decision-making ability of UAVs.

Unmanned aerial vehicles (UAVs) are prone to losing their targets when tracking moving objectives. A tracking strategy is proposed herein that enables the standoff tracking of a moving target using a vision system, which significantly reduces the occurrence of target loss. The strategy combines the Gimbal Control Algorithm based on Motion Compensation (GCAMC) with the Improved Reference Point Guidance Method (IRPGM). The GCAMC utilizes the attitude of the UAV and the deviation of the target from image center as the feedback. The target can be kept within the field-of-view (FOV) of the camera when the gimbal model is unknown. The IRPGM generates straight or circular paths according to the speed and potition of the target, while the UAV will continuously track the generated trajectory to achieve the objective of target tracking. To validate and demonstrate the tracking performance of the proposed strategy, a closed-loop visual simulation platform was devised and implemented to simulate the process of target tracking. The results of the simulation demonstrate that by using the proposed approach, the UAV can enter the desired trajectory quickly when its initial position and flight direction are arbitrary.