This article investigates the problem of asynchronous attack-compensated control for discrete-time nonlinear semi-Markov jump systems (S-MJSs) subject to false data injection (FDI) attacks. To mitigate the adverse effects of malicious data, a resilient controller is constructed by combining a false-signal observer with a compensation-based control law. Concurrently, considering limited network resources, a memory-based adaptive event-triggered scheme is proposed to enhance control performance while significantly reducing communication overhead. Recognizing the practical constraints in transition information identification, the semi-Markov kernel (SMK) and the high-level homogeneous Markov chain are assumed to be partially available. By employing a mode-rule-dependent Lyapunov function together with the linear matrix inequality method, sufficient conditions are derived to guarantee the $H_{\infty }$ performance of the control systems. Finally, a single-link robot arm model is employed to validate the efficacy of the proposed compensation control strategy.

This paper is concerned with the quadrotor navigation problem by proposing an integrated framework for active perception and safe planning, enabling quadrotors to navigate in unknown environments while autonomously avoiding areas with higher obstacle density during the planning process to generate collision-free trajectories. The active perception component leverages the quadrotor's onboard sensors to collect environmental data and dynamically adjusts its heading and path. By integrating real time sensor feedback with an active perception path planning algorithm, it maximizes the visibility and sensing coverage of regions of interest, enhancing the perception of key environmental features. Concurrently, a risk-aware safe flight corridor (SFC) is generated by constructing convex polyhedron around accessible areas using environmental data, with the corridors dynamically adapted based on obstacle density and proximity. This framework combines the optimization of model predictive control (MPC) to ensure that the planned trajectory is stable and dynamically feasible, adhering to the quadrotor dynamics. The motion of yaw angle is also planned in this framework to improve the quadrotor's perception efficiency and path optimization quality. Simulation results demonstrate the system's ability to navigate in unknown environments efficiently while maintaining a high safety margin and adaptability to different scenarios.

This paper investigates the problem of nonlinear data injection attacks against cyber-physical systems, a scenario that poses substantially greater challenges than linear ones. A novel fuzzy nonlinear data injection attack strategy, formulated within the H∞ performance framework, is proposed. The nonlinear cyber-physical system is represented by the T-S fuzzy model, and the attack signals are constructed directly from the fuzzy nonlinear representation. The attack objectives, defined in terms of H∞ and H- performance, are designed to maximally degrade the estimation accuracy of the state observer while avoiding detection. Distinct from prior attack strategies that rely on moment matrices techniques, a virtual system is introduced to characterize the influence of fuzzy injection signals on both the observer and the anomaly detector. This construction enables the transformation of the H- performance index into the H∞ performance index, thereby facilitating the derivation of the H∞ fuzzy-based attack strategy. Finally, the effectiveness of the proposed method is demonstrated through numerical simulations and comparative analyses, confirming its superiority over existing approaches.

In this article, the zonotopic set-membership state estimation (ZSMSE) for cyber-physical systems (CPSs) is investigated in the presence of stealthy false data injection (FDI) sensor attacks. A set-membership state estimation approach is established in which the true state is enclosed by a parallelotope (a special class of zonotope) at each sampling instant. Due to the openness of the network transmission process, malicious attackers have the ability to modify sensor measurement information by injecting false data. Therefore, from the perspective of the defender, the condition for an attack to bypass the detector and destroy the ZSMSE is discussed. Specifically, the stealthiness definition for the FDI sensor attacks and the vulnerability definition for ZSMSE are given. Moreover, the necessary and sufficient condition for the ZSMSE to be vulnerable is derived. Subsequently, a watermarking-based protection strategy is proposed for vulnerable ZSMSE to ensure the validity of state estimation results under stealthy attacks. The watermarking matrix is designed to break the stealthiness of the attack while ensuring the convergence of the ZSMSE. Finally, a series of illustrative examples are provided to demonstrate the effectiveness of the proposed strategy.

This paper studies the problem of robust data-driven control for linear continuous-time systems with time-varying delays under denial-of-service (DoS) attacks. Based on a set of generated input-state data satisfying rank conditions, an equivalent representation of the closed loop system is established without requiring explicit knowledge of the system matrices. By modeling the linear time-varying delay system under DoS attacks as a switched system with two modes corresponding to DoS active and dormant intervals, novel data-driven stability conditions have been obtained, without relying on any prior knowledge of attack interval distributions. Then, a state feedback controller synthesis method is developed using linear matrix inequalities and linear matrix equations, to guarantee the asymptotic stability and robustness against disturbances of the closed loop system under DoS attacks. Finally, two numerical examples have been given to validate the effectiveness of the proposed approach.

Heart failure (HF) is a multifactorial metabolic disorder. While gut microbial dysbiosis is increasingly implicated in HF, the specific causal microbes and their molecular mediators linking intestinal perturbations to cardiac dysfunction remain undefined. We performed integrated fecal 16S rRNA and serum metabolomic profiling in a clinical HF cohort comprising 149 HF patients and 50 healthy controls. We systematically assessed the multidimensional alterations and diagnostic potential of the gut microbiome and serum metabolome. Key microbe-metabolite interactions were identified through causal inference and experimentally validated using i n vitro bacterial cultures, in vivo mouse models of HF, and assays of mitochondrial function. HF patients exhibited significant alterations in the gut microbiome and serum metabolome related to energy homeostasis, vascular tone regulation, and inflammatory balance. These differential microbial and metabolic signatures demonstrated superior diagnostic potential for HF. A key finding was depletion of the commensal bacterium Phocaeicola vulgatus led to the accumulation of serum 5’-methylthioadenosine (MTA), which in turn induced mitochondrial dysfunction and aggravated cardiac injury. Restoring P. vulgatus mitigated these effects by metabolizing MTA, whereas direct MTA administration recapitulated mitochondrial dysfunction and exacerbated HF pathology. This study provides an integrative multi-omics perspective on the gut microbiome-serum metabolome interplay in HF, revealing both diagnostic biomarkers and mechanistic insights. Through a causal inference framework, we identify the P. vulgatus –MTA axis as a causal pathway through which gut microbes influence HF progression.

This paper investigates injection attacks incorporating stochastic noise against linear discrete time-varying system, which is more general but also more challenging to defend than deterministic injection attacks. Based on the optimization theory and novel key defining matrices, an optimal stochastic injection attack strategy is proposed. Unlike existing strategies, this strategy is stochastic, making it harder for defenders to predict. Therefore, the newly designed attack is expected to be widely used to disrupt system performance. By leveraging estimated data from the observer and the attack input, a virtual residual system is established, which more accurately reflects changes in the system error before and after the attack than the traditional error system. Using the state and output residuals along with the stochastic attack input, two performances are defined to ensure the stealthiness and effectiveness of the attack, respectively. Subsequently, an optimal attack problem with non-convex objective function and constraint is formulated. The key to acquiring the designed optimal stochastic injection attack strategy is to apply a semi-definite relaxation involving moment matrices for transforming this non-convex optimization problem into a convex optimization problem and solving it. Finally, the effectiveness of the proposed attack strategy is validated through numerical simulation of a networked mass-spring-damper system and a V-formation experiment involving three quadrotors. Note to Practitioners-The primary objective of this paper is to focus on the cyber security of discrete time-varying systems from the perspective of the attacker, which provides insight into the way of generating attack strategies under the stochastic case. The majority of existing injection attack strategies against intelligent systems are deterministic, and this can make the attacks less effective as the attacked system reconstructs and compensates for the attack signals, and the attack strategies tend to fail or are mostly ineffective. This paper synthesizes state estimation, semi-definite programming, optimization principles, and control theory to propose an optimal stochastic injection attack strategy. Compared with the deterministic injection attack strategies, the latter is harder to defend. Specifically, the operation of the attacker is divided into two phases: initial data eavesdropping and strategy generation. In the initial data eavesdropping phase, the attacker continuously eavesdrops and stores the initial traffic data of the system for a virtual state residual data estimation. In the strategy generation phase, the attacker generates the attack strategy using semi-definite programming with the help of the measured initial data. The mathematical analytical form of the proposed optimal stochastic injection attack strategy is given in detail. Subsequently, the effectiveness is verified by the numerical simulation and experiment based on a leader-following form consisting of three quadcopters, but not yet tested in production. In the future, we plan to design stochastic injection attack strategies with a data-driven framework.

BACKGROUND AND AIMS:The gut microbiome is closely associated with pediatric Crohn's disease (CD), while the multidimensional microbial signature and their capabilities for distinguishing pediatric CD are underexplored. This study aims to characterize the microbial alterations in pediatric CD and develop a robust classification model. METHODS:A total of 1175 fecal metagenomic sequencing samples, predominantly from 3 cohorts of pediatric CD patients, were re-analyzed from raw sequencing data using uniform process pipelines to obtain multidimensional microbial alterations in pediatric CD, including taxonomic profiles, functional profiles, and multi-type genetic variants. Random forest algorithms were used to construct classification models after comparing multiple machine learning algorithms. RESULTS:We found pediatric CD samples exhibited reduced microbial diversity and unique microbial characteristics. Pronounced abundance differences in 45 species and 1357 KEGG orthology genes. Particularly, Enterocloster bolteae emerged as a pivotal pediatric CD-associated species. Additionally, we identified a vast amount of microbial genetic variants linked to pediatric CD, including 192 structural variants, 1256 insertions/deletions (InDels), and 3567 single nucleotide variants, with a considerable portion of these variants located in non-genic regions. The InDel-based model outperformed other predictive models against multidimensional microbial signatures, achieving an area under the ROC curve (AUC) of 0.982. The robustness and disease specificity were further confirmed in an independent CD cohort (AUC = 0.996) and 5 other microbiome-associated pediatric cohorts. CONCLUSIONS:Our study provided a comprehensive landscape of microbial alterations in pediatric CD and introduced a highly effective diagnostic model rooted in microbial InDels, which contributes to the development of noninvasive diagnostic tools and targeted therapies.

This paper presents an attack-defense formation control framework for connected vehicle platoons. This framework effectively addresses the challenges posed by malicious attacks on multi-intelligent vehicle systems in open network environments. First, from the attacker’s perspective, an optimal data injection attack strategy is designed, taking into account energy constraints and the complexities of the communication environment. This strategy aims to disrupt the multi-intelligent vehicle system with minimal energy expenditure. Next, from the defender’s perspective, a formation compensation control strategy is proposed to enhance the system’s resilience against such attacks. This defense control strategy includes real-time monitoring and adjustments of the communication data between vehicles, ensuring that the vehicle formation remains stable and coordinated even in the case of data injection attacks. Finally, simulation experiments are conducted to validate the effectiveness and robustness of the proposed methods.

Previous studies establish guanylate binding protein 5 (GBP5) as a driver in the development of inflammatory bowel diseases (IBDs). Here, we aim to elucidate the mechanism underlying the pro-inflammatory role of GBP5. We observe that loss of Gbp5 causes reduced colonic inflammation and decreased numbers of innate lymphoid cells (ILCs) in colitis mice. The transcriptional alterations observed in GBP5-deficient THP-1 cells mirrored those triggered by STAT1 activation, leading to the findings that GBP5 is essential for the stimulated expression of STAT1 and its downstream effectors, including cytokines that drive the expansion of ILCs. Remarkably, over-expression of STAT1 reverses the reduced cytokine expression caused by GBP5 deficiency. While GBP5 does not directly drive gene transcription, it binds with STAT1 and facilitates its nuclear translocation, thereby enhancing the expression of STAT1 itself and its downstream effectors. Overall, GBP5 plays a pro-inflammatory role in IBD by enhancing the activity and expression of STAT1.

To investigate the protective effect and mechanism of Qingdu Wenxin Formula(QD-WXF) against doxorubicin-induced cardiotoxicity(DIC), as well as the regulatory effect of QD-WXF on the cyclic guanosine monophosphate-adenosine monophosphate synthetase(cGAS)/stimulator of interferon genes(STING)/nuclear factor(NF)-κB signaling pathway. C57BL/6J mice were randomized into control, model, pravastatin(40 mg·kg~(-1)), and low-(1.3 g·kg~(-1)), medium-(2.6 g·kg~(-1)), and high-dose(5.2 g·kg~(-1)) QD-WXF groups. The mouse model of DIC was established through tail vein injection of doxorubicin, followed by four weeks of gavage. The cardiac function was evaluated by echocardiography, lactate dehydrogenase(LDH) assay, and troponin Ⅰ(TNⅠ) assay. Myocardial histopathology was assessed via hematoxylin-eosin and Masson's trichrome staining. RT-qPCR was conducted to measure the mRNA levels of interleukin(IL)-6, tumor necrosis factor(TNF)-α, and IL-1β in the cardiac tissue. Network pharmacology and molecular docking were employed to predict key pathways associated with QD-WXF and DIC, while Western blot to assess the protein level of STING. The mice with STING knockout(STING~(KO)) were prepared to validate the expression changes of proteins involved in the STING pathway and inflammation. The results of the animal experiment showed the low, medium and high doses of QD-WXF increased the ejection fraction(EF) and fractional shortening(FS) to different extents, reduced the myocardial injury markers LDH and TNⅠ, down-regulated the mRNA levels of IL-6, IL-1β, and TNF-α in the cardiac tissue, significantly reduced the inflammatory cells, recovered the regular arrangement of myocardial cells, and decreased the area of perivascular fibrosis. The high-dose group of QD-WXF had the best effect. According to the prediction results of network pharmacology and molecular docking, the key pathway between QD-WXF and DIC was the STING pathway. In addition, QD-WXF up-regulated the level of phosphorylated STING(p-STING)/STING. In the STING~(KO) mouse experiment, the results of cardiac function evaluation and inflammatory index detection of the wild type(WT) group were consistent with those of C57BL/6J mice. QD-WXF downregulated the protein levels of phosphorylated TANK-binding kinase 1(p-TBK1), phosphorylated interferon regulatory factor 3(p-IRF3), and phosphorylated nuclear factor κB(p-NF-κB). In the experiment with STING~(KO), there was no significant difference between the QD-WXF group and the model group, while significant differences existed between the WT model group and the STING~(KO) model group. The above results indicate that QD-WXF can effectively alleviate doxorubicin-induced cardiotoxicity by inhibiting the inflammation via the cGAS/STING/NF-κB pathway.

Prior studies indicate no correlation between the gut microbes of healthy first-degree relatives (HFDRs) of patients with Crohn's disease (CD) and the development of CD. Here, we utilize HFDRs as controls to examine the microbiota and metabolome in individuals with active (CD-A) and quiescent (CD-R) CD, thereby minimizing the influence of genetic and environmental factors. When compared to non-relative controls, the use of HFDR controls identifies fewer differential taxa. Faecalibacterium, Dorea, and Fusicatenibacter are decreased in CD-R, independent of inflammation, and correlated with fecal short-chain fatty acids (SCFAs). Validation with a large multi-center cohort confirms decreased Faecalibacterium and other SCFA-producing genera in CD-R. Classification models based on these genera distinguish CD from healthy individuals and demonstrate superior diagnostic power than models constructed with markers identified using unrelated controls. Furthermore, these markers exhibited limited discriminatory capabilities for other diseases. Finally, our results are validated across multiple cohorts, underscoring their robustness and potential for diagnostic and therapeutic applications.

This article studies the optimal distributed denial-of-service attack strategy for cyber–physical systems with multiple attackers and multiple defenders. An advanced attack strategy is proposed to cause the great damage to system in a multiattacker–defender form. First, a novel model of signal-to-interference-to-noise ratio for the multiattacker and multidefender is built. Taking the energy constraints into consideration, the objective of defenders is to minimize the system performance, while the attackers tend to deteriorate it by emitting interference energy. Thus, the optimal channel selection and optimal energy allocation strategies are proposed to answer which channel both of them should choose and how much power both of them should allocate to each channel in a finite time horizon. Second, a two-player zero-sum matrix game is formulated to solve the optimal problem by linear programming and obtain the Nash equilibrium. When the channel parameters are time-varying, a dynamic optimal channel selection problem is considered and a multistage game algorithm is proposed to find the Nash equilibrium. In addition, the designed optimal strategies of both players are demonstrated and analyzed. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach.