In this paper, an anti-disturbance tracking control scheme with resilient guaranteed profiles is proposed for microelectromechanical system (MEMS) gyroscopes. First, a state observer is constructed for cases in which the velocity measurement is unavailable or severely affected by measurement noise. Based on the recovered velocity information and the invariant manifold principle, a filter-based unknown system dynamics estimator (USDE) is then developed to estimate the system uncertainties. Second, to meet the stringent time and accuracy requirements of MEMS gyroscopes in practical measurement tasks, a resilient quantitative guaranteed performance control (RQGPC) strategy with a dynamically adjustable envelope is proposed. By introducing an auxiliary submodule to monitor input saturation, the proposed RQGPC can accommodate saturation effects while avoiding violations of the preset error constraints. The proposed scheme constrains the convergence process within a predesigned region. Consequently, the overshoot and settling time can be specified by the user, and effective tracking performance can be maintained under parameter uncertainties, external disturbances, and input saturation. Finally, the closed-loop stability is analyzed using Lyapunov theory, and the effectiveness of the proposed method is verified through simulation and experimental results.
Aerial imaging systems are subject to complex multisource disturbances, including high-frequency engine vibrations and abrupt aerodynamic turbulence, which critically hinder line-of-sight stabilization. Moreover, input saturation nonlinearities imposed by stringent onboard hardware constraints severely limit the capability of the system to reject highly dynamic perturbations. To address these challenges, this study proposes a novel anti-saturation adaptive sliding-mode-assisted disturbance observer. The proposed scheme integrates a dual-strategy anti-saturation mechanism. First, a dynamic unsaturation-scale factor mathematically quantifies the residual driving capacity of the actuator to actively regulate the adaptive sliding-mode gain in real time. Second, a describing-function-based constraint is strictly embedded into the exact observer error dynamics. This dual approach intelligently manages the control effort within absolute physical limits, preventing integral windup, gain over-regulation, and catastrophic observer divergence during deep saturation. Through rigorous Lyapunov stability analysis, all closed-loop signals are proven to exhibit global uniform ultimate boundedness. Finally, comparative experiments conducted on an equivalent gimbal platform validated the superiority of the proposed controller in achieving both high dynamic disturbance rejection and high-precision imaging in adverse aerial environments. The proposed algorithm outperformed existing anti-disturbance methods in terms of convergence speed and dynamic performance, thereby validating its robustness.
Maintaining stable velocity control for a self-propelled pipeline inspection robot remains challenging under high-dimensional state spaces, time-varying friction, and non-stationary operating conditions. This study proposes a safety-constrained deep reinforcement learning control strategy for robust velocity regulation under contact-friction and lubrication disturbances. The proposed method employs a quantile-based distributional critic with a Huber loss to improve value estimation under uncertain disturbances. A lower-tail distributional risk indicator is introduced to regulate curiosity-driven exploration and clipped policy updates. In addition, a safety shield projects raw actions into the feasible actuator range, and the shield correction cost is fed back into policy learning. The controller is trained in Isaac Sim and deployed on an experimental prototype. Experiments are conducted under different operating conditions and velocity disturbances. Compared with the A2C baseline and ablation variants, the proposed method achieves lower MAPE under all tested conditions, with an average reduction of at least 16.5%. Compared with alternative value- and policy-update variants, the proposed method reduces the average MAPE and IQR by 67.4%-76.0% and 41.3%-62.7%, respectively, while exhibiting lower overshoot. These results indicate that the proposed method improves tracking accuracy, reduces velocity dispersion, and enhances velocity-control robustness under the tested contact and lubrication disturbances.
Deoxysphingolipids (dSLs) are atypical sphingolipids that accumulate in several pathological settings, yet their impact on hematologic malignancies is poorly understood. Here, we investigate the pathways and mechanisms of deoxysphinganine (dSA) cytotoxicity in lymphoma cells and its potential as a therapeutic agent. dSA exhibited markedly greater cytotoxicity than canonical sphingoid bases in lymphoma cell lines, yet induced only cytostatic effects in normal human T cells, indicating a therapeutically exploitable window. Inhibition of ceramide synthase blocked the generation of deoxy(dihydro)ceramides, prevented mitochondrial depolarization, caspase activation, ER stress, and DNA damage, establishing CerS-dependent deoxysphingolipids as essential mediators of dSA-induced death. Mechanistically, dSA engaged a mitochondrial apoptotic pathway, with DNA damage occurring downstream of mitochondrial permeabilization and caspase activation, while PERK-driven ER stress occurred in parallel and was dispensable for cytotoxicity. Subtype-specific engagement of ER stress and DNA damage further suggests that dSL signaling is shaped by lineage context. The differential sensitivity between malignant lymphoid cells and normal T cells, together with the central role of CerS-derived deoxy(dihydro)ceramides, highlights deoxysphingolipid metabolism as a druggable vulnerability in lymphoma. These findings support further exploration of dSA-based strategies and targeted modulation of dSL synthesis as a novel therapeutic avenue for non-solid hematologic malignancies.
This paper investigates the cooperative tracking error for multi-train systems subject to velocity constraints and time-varying resistance parameter uncertainties. A distributed tracking control algorithm, utilizing only relative position measurements between adjacent trains, is proposed to enable all trains to track a desired operational velocity while maintaining a desired formation. To analyze the coupled effects of velocity saturation and parameter uncertainties, we introduce a time-varying scaling factor to handle the saturation nonlinearity, transforming the original system into an equivalent form with a bounded, non-vanishing disturbance term. We rigorously prove ultimate boundedness of the disagreement state, the relative position errors, and the absolute velocity errors. Furthermore, explicit analytical expressions for the corresponding ultimate bounds are derived. Numerical simulations of a multi-train system are presented to validate the effectiveness of the proposed control law and the accuracy of the theoretical error analysis.
While the theories of predefined-time (PdT) stability have been well established for continuous-time systems, research on discrete-time systems remains an open problem. This paper investigates stability control for nonlinear discrete-time perturbed systems and proposes the discrete-time PdT stability concept. Based on this concept, this paper develops two Lyapunov theories-the Lyapunov theory for PdT stability and that for practical PdT stability. Furthermore, this paper constructs a PdT discrete-time sliding mode controller for a class of second-order perturbed systems, which guarantees PdT stability throughout the reaching and sliding phases. Additionally, this paper presents an evaluation of the proposed discrete-time sliding mode control method, centered on an analysis of its key performance indicators. The results of numerical simulations and physical scenario-based simulations verify the method's effectiveness.
This paper proposes a modified adaptive backstepping method with a control gain selection guideline for unknown nonlinear systems, focusing on adjustable transient performances. Unlike conventional backstepping methods that cancel linear cross terms, the proposed approach retains these terms to obtain a symmetric state matrix for the linearized closed-loop system. Based on this structure, a novel control gain determination method is developed to select control gains for achieving a desired settling time while effectively attenuating overshoot. Compared with conventional trial-and-error tuning, the proposed method improves tuning efficiency. Moreover, the potential solvability difficulties of optimization-based pole-placement methods are avoided. Numerical examples with different desired settling times demonstrate adjustable settling time and overshoot attenuation. Simulations and experiments on a tower crane platform further validate the proposed scheme and demonstrate its advantages compared with existing control methods.
In this paper, an adaptive fuzzy prescribed performance control strategy based on predefined-time theory is proposed for a space robot under external disturbances and dynamic uncertainties. Firstly, an adaptive fuzzy backstepping framework that integrates predefined-time control theory is presented. To mitigate the impact of external disturbances on tracking performance, a predefined-time nonlinear disturbance observer (PTNDO) is introduced to compensate for them, thereby enhancing the convergence accuracy of trajectory tracking control. Next, global predefined-time prescribed performance control (PTPPC) is introduced to improve the transient and steady-state performance of the control system. Additionally, a single-parameter fuzzy logic system (FLS) is introduced to compensate for dynamic uncertainties in the system. An adaptive fuzzy predefined-time controller is then developed based on PTNDO, PTPPC, and single-parameter FLS. Compared to multi-parameter FLS, single-parameter FLS reduces the consumption of computational resources while retaining the desired level of approximation accuracy. By utilizing PTNDO and PTPPC, the transient and steady-state performance of the space robot control system are enhanced and the limitations of traditional prescribed performance control caused by initial states are avoided. The predefined-time stability of the proposed controller is rigorously proven by using Lyapunov theory, and numerical simulations demonstrate its superiority and effectiveness compared with existing methods.
Whether the fecal metabolome differs according to intensive low-density lipoprotein cholesterol (LDL-C) target achievement among statin-treated patients is unclear. In this cross-sectional study, 124 statin-treated adults with chronic disease were stratified by fasting LDL-C into a target-achieved group (< 70 mg/dL, n = 52) and a target-not-achieved group (≥ 70 mg/dL, n = 72). Stool samples were profiled by untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry, and multivariable models adjusted for age, sex, chronic kidney disease, and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker use were used to identify metabolites independently associated with target achievement. Statin dose, treatment duration and glucose-lowering therapy were also compared between the groups. Paired 16S rRNA gene sequencing data available for a subset (n = 86) were used for integrative correlation and network analyses. Partial least-squares discriminant analysis showed separation between the two groups. Eight annotated metabolites-glutamine, glutamate, phenylalanine, N-acetyl-L-phenylalanine, L-methionine, N-acetyl-L-methionine, lysine, and N-methyl-D-aspartic acid, predominantly amino acids and their derivatives-were present at lower fecal levels in participants who achieved the LDL-C target. Metabolite set enrichment analysis implicated amino acid and nitrogen metabolism, and multiomics network analysis identified an Anaerotruncus-centered amino acid module with high degree centrality. In conclusion, LDL-C target achievement under statin therapy was associated with a coherent "low fecal amino acid" signature and an Anaerotruncus-linked microbe-metabolite hub. These findings suggest that intestinal nutrient handling and gut microbial amino acid metabolism may contribute to variability in LDL-C response, and they warrant prospective mechanistic evaluation.
Metabolic dysfunction-associated liver disease (MASLD) arises from the accumulation of triglycerides within the liver. MASLD can advance to metabolic dysfunction-associated steatohepatitis (MASH), cirrhosis, and hepatocellular carcinoma. Monoacylglycerol acyltransferase 2 (MOGAT2) is essential for triglyceride synthesis and plays a significant role in regulating lipid metabolism. Here, we demonstrate the ability of a new human MOGAT 2 inhibitor, VB-85387, to inhibit the development of MASLD/MASH and further define its effects on the key metabolic pathways that progress MASH development. MASLD/MASH was induced using a methionine, choline-deficient diet (LMCD) or by streptozotocin treatment combined with high fat diet feeding (STAM-HFD). VB-85387 significantly mitigated the severity of MASLD and reduced signs of MASH in mice subjected to these two distinct diets. VB-85387-treated mice exhibited decreased fibrosis, evidenced by reduced hepatic triglyceride concentrations, hydroxyproline levels, and collagen deposition. NAS scores were consistently lower in VB-85387-treated mice across both models. VB-85387-treated mice showed induced PPARα signaling and reduced SREBP transcription, demonstrating a likely role for VB-85387 in regulating lipogenesis and fatty acid β-oxidation. STAM-HFD treated mice showed lower NF-κBp65 activation, which was associated with lower TNFα expression. IL-1β and IFNβ levels were also both reduced, suggesting VB-85387 can reduce pro-inflammatory pattern recognition receptor signaling. In addition, treatment suppressed IL-4/IL-6-dependent JAK activation. Overall, VB-85387 inhibited MASLD development by reducing liver triglyceride levels, fibrosis, and meta-inflammatory signaling. VB-85387 was as effective or superior to the MOGAT2 inhibitor phase I clinical trial drug BMS-963272 in reducing MASLD and fibrosis. VB-85387 has considerable potential for developing therapeutics targeting MASLD/MASH.
Glioma represents one of the most aggressive tumors in the central nervous system, with clinical management facing significant challenges including high recurrence rates and therapeutic resistance. Ferroptosis, an iron-dependent form of cell death, holds potential for glioma treatment, yet tumor cells frequently develop evasion mechanisms. This study elucidates the molecular mechanisms by which hypoxic microenvironment confers ferroptosis resistance in glioma cells, focusing on the pivotal role of the HIF-1α/SREBP1 signaling axis and its downstream effectors FASN and SCD1. Our experimental results demonstrate that hypoxic conditions significantly upregulate HIF-1α expression and confer resistance to RSL3-induced ferroptosis. Mechanistic studies reveal that HIF-1α promotes SREBP1 activation, which subsequently upregulates FASN and SCD1 expression to suppress lipid peroxidation.Furthermore, the HIF-1α-specific inhibitor PX-478 effectively reverses hypoxia-induced ferroptosis resistance and significantly enhances tumor cell sensitivity to ferroptosis inducers. In vivo experiments confirm the potent antitumor effects of PX-478 combined with RSL3. This study systematically elucidates the role of the HIF-1α-SREBP1-FASN/SCD1 signaling axis in ferroptosis regulation in glioma, providing important theoretical foundations and experimental support for developing HIF-1α-targeted ferroptosis therapies.
BACKGROUND:Lipoprotein(a) [Lp(a)] reflects inherited atherothrombotic risk, whereas the C-reactive protein-triglyceride-glucose index (CTI) integrates systemic inflammation, triglyceride-related lipid disturbance, and glucose-related metabolic stress. Their individual and joint association with angiographic coronary lesion burden in acute coronary syndrome (ACS) remain incompletely defined. We examined whether CTI complements Lp(a) in characterizing coronary lesion burden in ACS. MATERIALS AND METHODS:This retrospective, single-center study included 2,836 consecutive patients with ACS who underwent coronary angiography. Coronary lesion burden was assessed using continuous Gensini score, a high Gensini score, and multivessel disease (MVD). Multivariable regression, restricted cubic spline analyses, CTI-stratified analyses, incremental receiver operating characteristic analyses, and internally validated machine-learning analyses with SHAP interpretation were performed. RESULTS:Higher Lp(a) and CTI level were both associated with greater coronary lesion burden. Compared with Lp(a) <75 nmol/L, Lp(a) ≥175 nmol/L was associated with high Gensini score (OR, 1.51 [95% CI, 1.17-1.96]) and MVD (OR, 1.69 [95% CI, 1.27-2.26]). Each 1-SD increase in CTI was associated with high Gensini score (OR, 1.47 [95% CI, 1.35-1.60]) and MVD (OR, 1.18 [95% CI, 1.08-1.28]). Among inflammatory-lipid indices, CTI showed the most consistent associations and provided the largest numerical incremental discrimination beyond Lp(a). The associaton between ver high Lp(a) and coronary lesion burden was more pronounced at higher CTI levels, particular for MVD. Machine-learning analyses further supported the relevance of both CTI and Lp(a). CONCLUSIONS:In patients with ACS, higher Lp(a) and CTI level were associated with greater angiographic coronary lesion burden. CTI may complement Lp(a) by capturing inflammatory-metabolic status, supporting their joint assessment for more refined characterization of lesion-burden risk in ACS.
Lipoprotein metabolism is significantly different between mice and humans thus making it difficult to model disorders of human lipid metabolism in transgenic mice. Systemic lipoprotein metabolism is predominantly governed by hepatocytes, and mice with humanized livers display human-like lipid profiles. Here we report a highly efficient method to knock out genes in human hepatocytes while retaining their ability to repopulate immune deficient rodents. As proof-of-principle Fah deficient, immune compromised mice were repopulated with Apolipoprotein B (APOB) knockout human hepatocytes. Mice humanized with knockout cells recapitulated typical features of human hypobetalipoproteinemia. We conclude that at least some human lipid metabolism disorders can be modeled in liver chimeric mice using human knockout hepatocytes.
As information exchange among agents increases, multi-agent systems with limited communication and energy resources have become increasingly vulnerable to cyber threats, particularly sensor attacks that compromise data integrity and system stability. To address this challenge, this paper proposes a control framework for nonlinear multi-agent systems under sensor attacks, integrating adaptive fuzzy control with dynamic attack detection mechanism and dynamic event-triggered strategies. The proposed detection scheme uses only the local output errors of the agents without requiring knowledge of inter-agent static output mappings, thereby reducing implementation complexity. To achieve consensus tracking under attacks, an adaptive fuzzy consensus controller incorporating Nussbaum-type functions is developed within the backstepping framework to handle uncertain and time-varying output gains caused by attacks. Additionally, a dynamic event-triggered mechanism employing an auxiliary variable is proposed to significantly reduce communication overhead while preserving resilient consensus performance under sensor attacks. Rigorous theoretical analysis proves that all closed-loop signals remain bounded and consensus tracking is achieved despite the presence of attacks. Finally, simulation studies further demonstrate the proposed framework’s effectiveness.
Classical dynamic surface control (CDSC) is widely adopted to alleviate the complexity explosion inherent in integrator backstepping control (IBC), where each virtual control input is processed through a linear low-pass filter. Nevertheless, the introduction of such filters inevitably induces additional errors, which may compromise the global boundedness of the closed-loop system. This paper develops an improved dynamic surface control (DSC) framework for strict-feedback nonlinear systems (SFNSs) that rigorously guarantees global stability. In contrast to CDSC approaches, the proposed method replaces linear filters with barrier function-based nonlinear filters, which effectively mitigate the complexity issue while constraining the filtered errors. A salient feature of the proposed framework is its ability to ensure global uniform boundedness (GUB) of all closed-loop signals, whereas existing CDSC methods typically provide only semiglobal uniform boundedness (SGUB). Extensive simulation and experimental results demonstrate that the proposed approach achieves an average reduction of 57.15% in tracking error compared with CDSC and preserves accurate tracking performance even under a large filtering time constant (ψ=1), thereby highlighting its improved robustness and superior control performance.
Fault diagnosis based on partial domain adaptation (PDA) has gained extensive attention, since it allows mismatched label sets between source and target domains and is thus more consistent with practical engineering applications. However, existing methods exhibit limited diagnostic performance under time-varying speed conditions. In this study, we propose a Structure-Preserving Constrained Smoothing Model (SPCSM) for partial cross-domain fault diagnosis, which is validated under time-varying speed conditions. By integrating the intrinsic geometric structure of the target domain with the category structure derived from the source domain, the model effectively enhances its smoothness, thereby mitigating the adverse effects of speed fluctuations on diagnostic performance. Experimental results demonstrate that the proposed SPCSM achieves an average accuracy of 98.06% on single-device tasks and 89.50% on cross-device tasks, outperforming several domain adaptation models.
