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Abstract The host limits Mycobacterium tuberculosis (Mtb) by enriching copper in high concentrations. This research investigates how Mtb escapes copper stress. The membrane protein encoded by Mtb Rv0102, when its homolog in M. smegmatis (MSMEG_4702) was knocked out, resulted in a fourfold decrease in intracellular copper levels and enhanced tolerance to elevated extracellular copper concentrations. Similarly, knockout mutants of its homolog in M. marinum (MMAR_0267) showed increased virulence in zebrafish and higher bacterial load within macrophages. In THP-1 cells infected with MMAR_0267 deletion mutants, the intracellular survival of these mutants increased, along with reduced THP-1 cell apoptosis. Deficiency in copper down-regulated the transcriptional level of the virulence factor CFP-10 in M. marinum, suppressed cytosolic signaling via the macrophage STING pathway, leading to decreased production of IFN-β and reduced cell apoptosis. In conclusion, these findings highlight the significant impact of copper on the survival and reproduction of mycobacteria, underscoring the importance of studying mycobacterial adaptation mechanisms in copper-rich environments.

The suspension system of the Electromagnetic Suspension (EMS) maglev train is crucial for ensuring safe operation. This article focuses on data-driven modeling and control optimization of the suspension system. By the Extended Dynamic Mode Decomposition (EDMD) method based on the Koopman theory, the state and input data of the suspension system are collected to construct a high-dimensional linearized model of the system without detailed parameters of the system, preserving the nonlinear characteristics. With the data-driven model, the LQR controller and Extended State Observer (ESO) are applied to optimize the suspension control. Compared with baseline feedback methods, the optimization control with data-driven modeling reduces the maximum system fluctuation by 75.0% in total. Furthermore, considering the high-speed operating environment and vertical dynamic response of the maglev train, a rolling-update modeling method is proposed to achieve online modeling optimization of the suspension system. The simulation results show that this method reduces the maximum fluctuation amplitude of the suspension system by 40.0% and the vibration acceleration of the vehicle body by 46.8%, achieving significant optimization of the suspension control.

Road vehicles and maglev trains have garnered significant attention, with their suspension systems being crucial for safe and stable performance. However, these systems can be compromised by faults such as sensor and actuator failures, posing risks to stability and safety. This review explores fault-tolerant controls for suspension systems, driven by the need to enhance fault tolerance in such scenarios. We examine the dynamic similarities between the semi-active/active suspension systems in road vehicles and the suspension systems in maglev trains, offering a comprehensive summary of fault-tolerant control strategies for both. Our analysis covers the histories, technical characteristics, fundamentals, modeling, mathematical derivations, and control objectives of both systems. The review categorizes fault-tolerant control methods into hardware redundancy, passive fault-tolerant control, and active fault-tolerant control. We evaluate the advantages and disadvantages of these strategies and propose future directions for the development of fault-tolerant control in suspension systems.

Abstract Background To evaluate multiple parameters in multiple b-value diffusion-weighted imaging (DWI) in characterizing breast lesions and predicting prognostic factors and molecular subtypes. Methods In total, 504 patients who underwent 3-T magnetic resonance imaging (MRI) with T1-weighted dynamic contrast-enhanced (DCE) sequences, T2-weighted sequences and multiple b-value (7 values, from 0 to 3000 s/mm2) DWI were recruited. The average values of 13 parameters in 6 models were calculated and recorded. The pathological diagnosis of breast lesions was based on the latest World Health Organization (WHO) classification. Results Twelve parameters exhibited statistical significance in differentiating benign and malignant lesions. alpha demonstrated the highest sensitivity (89.5%), while sigma demonstrated the highest specificity (77.7%). The stretched-exponential model (SEM) demonstrated the highest sensitivity (90.8%), while the biexponential model demonstrated the highest specificity (80.8%). The highest AUC (0.882, 95% CI, 0.852–0.912) was achieved when all 13 parameters were combined. Prognostic factors were correlated with different parameters, but the correlation was relatively weak. Among the 6 parameters with significant differences among molecular subtypes of breast cancer, the Luminal A group and Luminal B (HER2 negative) group had relatively low values, and the HER2-enriched group and TNBC group had relatively high values. Conclusions All 13 parameters, independent or combined, provide valuable information in distinguishing malignant from benign breast lesions. These new parameters have limited meaning for predicting prognostic factors and molecular subtypes of malignant breast tumors.

Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based position control parameter optimization method of PSRMs is proposed in this paper. First, to improve the accuracy of the motor dynamics model, a Hammerstein–Wiener model based on the BP neural network input–output nonlinear module is established by combining the linear model and nonlinear model structures so that the nonlinear and linear characteristics of the system are characterized simultaneously. Then, a position control parameter optimization system of PSRMs is developed using the established Hammerstein–Wiener model. In addition, with a self-designed simulated annealing adaptive particle swarm optimization algorithm (SAAPSO), the position control parameter optimization system is performed offline iteratively to obtain the optimal position control parameters. Simulations and experiments are carried out and the corresponding results show that the optimal position control parameters obtained by the proposed method can be directly applied in the actual control system of PSRMs and the control performance is improved effectively using the obtained optimal control parameters.

The high-speed maglev train will be subjected to extremely obvious aerodynamic load and instantaneous aerodynamic impact during passing another train, which brings significant challenges to the train’s suspension stability and safe operation. It’s necessary to consider the influence of aerodynamic load and shock waves in the design of suspension control algorithms. Traditional proportion integration differentiation (PID) control cannot follow the change of vehicle parameters or external disturbance, which is easy to cause suspension fluctuation and instability. To improve the suspension stability and vibration suppression of the high-speed maglev train under aerodynamic load and impact, we design a siding mode controller introducing the primary suspension’s deformation to replace the aerodynamic load on the electromagnet. Furthermore, we establish the train’s dynamic simulation model with three vehicles and compare the designed controller and the PID controller for their performance in controlling the model suspension stability in the presence of the train operating in open air. Simulation results show that the sliding mode control (SMC) method proposed in this paper can effectively restrain the electromagnet fluctuation of the train under aerodynamic loads.

As new advanced vehicles, electromagnetic suspension (EMS)-type maglev trains have received wide attention because of their advantages such as high speed, no mechanical friction, low noise, low cost and energy consumption, strong climbing ability, and green environmental protection. The open-loop instability is one of the key points and difficulties for the levitation control systems of maglev trains. The closed-loop feedback control method must be applied to realize stable levitation. However, there are currently many levitation control methods just in theory. Considering their advantages and disadvantages, it is a major demand for maglev trains to select efficient, stable, applicable, and cost-saving methods to improve their dynamic performance and safety, which motivated this review. First, the current status of research on maglev trains is introduced in this paper, including types, system components, and research modes in various countries, followed by an analysis of the levitation control methods for EMS-type maglev trains. Then, the technical characteristics of the levitation control systems are described according to the basic principles of levitation systems, model building, mathematical derivation, and control objectives. Next, three kinds of typical levitation control methods are reviewed, namely, linear state feedback methods, nonlinear control methods, and intelligent control methods, according to their improvements and applications. Lastly, we summarize and evaluate the advantages and disadvantages of the three methods, and future developments of levitation control are suggested.

To improve the anti-laser damage performance of one-dimensional (1D) subwavelength gratings (SWGs), this paper proposes to reasonably reduce the peak electric field at the grating ridges to further reduce the laser damage caused by the electric field contribution. Based on Maxwell’s equations, the equations for solving the field intensity of the near-field distribution of the dielectric materials are derived. The numerical simulation theoretical model is described. A 1064 nm laser in the –y direction irradiates the geometric surfaces under two perpendicular polarization directions: perpendicular to the grating ridges (transverse magnetic wave (TM) or x-polarization) and parallel to the grating ridges (transverse electric wave (TE) or z-polarization). We discuss the effects of the profile shape, structural parameters and refractive index of the material on the near-field distribution at the 1D SWG ridges by using a finite element method (FEM). The near-field distribution is sensitive to the x- and z-polarization of incident light. The near-field distribution of the 1D SWGs for TM waves is modulated by the surface, whereas that for TE waves is similar to the distribution of the bare substrate. The results show that the near-field distribution of 1D SWGs greatly depends on the laser polarization direction.

During recent years, maglev transportation has made great progress, and as a result, many intelligent levitation control algorithms have emerged. However, enterprises often find it difficult to make a choice when faced with the selection of a controller. The main reason is that the performance evaluation of control algorithms is a complex, multiple-criteria, multifactor coupling problem that cannot be represented by a precise mathematic model. In this paper, a novel artificial intelligent evaluation method for the selection of a levitation controller is developed based on a 3-grade fuzzy method and analytic hierarchy process (AHP). Three kinds of intelligent levitation control algorithms are applied to a full-size test maglev train to collect experimental results with real data. The proposed artificial intelligence method to develop a 3-grade fuzzy multicriteria approach is used to select the best levitation controller for the maglev train. This method can then provide information consultation services to maglev train firms. To the best of our knowledge, for maglev trains, this is the first intelligent evaluation approach with real experimental data. The proposed method can also be applied to other information consultation and decision making systems with appropriate modifications.

Modern low-speed maglev trains typically use multi-node decentralized levitation control modules, which results in a complex levitation control system with coupling interaction. Conducting systematic levitation condition awareness of the levitation control system is still a promising challenge. In this paper, under the hypothesis of levitation residuals following normal distribution, a levitation condition awareness architecture for the levitation control system is proposed based on data-driven random matrix analysis. The proposed architecture consists of an engineering procedure followed by a cascaded mathematical procedure. In the decentralized engineering procedure, the data-driven modeling for individual levitation control modules is achieved by nonlinear autoregressive modeling with an exogenous input neural network, and the unknown parameters are identified by a modified combinatorial genetic algorithm. On this basis, high-dimensional analysis of streaming residual random matrices for the levitation control system is conducted aided by large-dimensional random matrix theory, and the control limits of the constructed indicators are well-designed using the theorical distributions. Based on the comparative analysis of the experimental datasets, the proposed awareness architecture is verified to show the effectiveness of the systematic condition evaluation of the levitation system, and incipient train-guideway interaction vibration abnormalities can be detected in a timely manner.

The suspension system of Maglev vehicle needs strong robustness and anti-jamming ability in the process of operation and fluctuation control. In order to solve the open-loop instability and strong nonlinearity of the mechanical equation of the suspension system of the Maglev vehicle, the non-linear dynamic equation is established. At present, the research based on the single electromagnet and the rigid track is the most common. However, based on this method, it is impossible to study the coupled vibration caused by track factors. Therefore, based on the dynamic equation of a single span simply supported beam of flexible track and the non-linear equation of the suspended electromagnet itself, an overall control model is needed to discuss the control strategy. Based on this dynamic model, the singularities of the system are solved according to Hurwitz criterion, and the characteristic equation corresponding to the Jacobian matrix is obtained. The stability analysis shows that the system is unstable. At the same time, the necessity of using a feedback control method to control the air gap has been proved. On this basis, a sliding mode adaptive state feedback controller for the maglev system is designed based on the RBF network approximation principle. The corresponding simulation and experimental results are given. The simulation and experimental results show that the controller can ensure the vehicle's stable suspension and effectively suppress external interference. Compared with the traditional PID and fuzzy controllers, the controller can guarantee a faster dynamic response, stronger robustness, and smaller overshoot while considering the flexible track and external disturbances.

In this paper, a novel concept of nonlinear controller design is proposed, which is applicable to nonlinear systems subject to external periodic disturbances. At first, by employing a pioneering nonlinear coordinate transformation, a new nonlinear system model with expected dynamic characteristics is derived. Then, a corresponding linear system is achieved by means of linearization techniques at equilibrium points. Next, based on the results of classical linear control theory, a linear quadratic regulator is applied to the resulted linear system. Finally, the nonlinear controller can be obtained through the inverse nonlinear coordinate transformation of the linear feedback controller. The performance of the proposed method is validated on a magnetic levitation system and is compared with the classical linearization feedback control. Simulation results show that the proposed controller design assures enhanced performance in terms of both system stabilization and disturbance attenuation.

Programmed cell death (PCD) is the result of an intracellular program and is accomplished by a regulated process in both prokaryotic and eukaryotic organisms. Here, we report a programed cell death process in Mycobacterium smegmatis, an Actinobacteria species which involves a transcription factor and a DNase of the HNH family. We found that over-expression of an ArsR family member of the transcription factor, MSMEG_6762, leads to cell death. Transcriptome analysis revealed an increase in the genes’ transcripts involved in DNA repair and homologous recombination, and in three members of HNH family DNases. Knockout of one of the DNase genes, MSMEG_1275, alleviated cell death and its over-expression of programmed cell death. Purified MSMEG_1275 cleaved the M. smegmatis DNA at multiple sites. Overall, our results indicate that the MSMEG_6762 affects cell death and is mediated, at least partially, by activation of the HNH nuclease expression under a stress condition.

Mycobacterium abscessus is one of the common clinical non-tuberculous mycobacteria (NTM) that can cause severe skin infection. 5-Aminolevulinic acid photodynamic therapy (ALA_PDT) is an emerging effective antimicrobial treatment. To explore whether ALA_PDT can be used to treat M. abscessus infections, we conducted a series of experiments in vitro. We found that ALA_PDT can kill M. abscesses. Mechanistically, we found that ALA_PDT promoted ferroptosis-like death of M. abscesses, and the ROS scavenger N-Acetyl-L-cysteine (NAC) and ferroptosis inhibitor Ferrostatin-1 (Fer-1) can mitigate the ALA_PDT-mediated sterilization. Furthermore, ALA_PDT significantly up-regulated the transcription of heme oxygenase MAB_4773, increased the intracellular Fe2+ concentration and altered the transcription of M. abscessus iron metabolism genes. ALA_PDT disrupted the integrity of the cell membrane and enhanced the permeability of the cell membrane, as evidenced by the boosted sterilization effect of antibiotics. In summary, ALA_PDT can kill M. abscesses via promoting the ferroptosis-like death and antibiotic sterilization through oxidative stress by changing iron metabolism. The study provided new mechanistic insights into the clinical efficacy of ALA_PDT against M. abscessus.

The Mycobacterium (M.) tuberculosis comprising proline–glutamic acid (PE) subfamily proteins associate with virulence, pathogenesis, and host-immune modulations. While the functions of most of this family members are not yet explored. Here, we explore the functions of “PE only” subfamily member PE31 (Rv3477) in virulence and host-pathogen interactions. We have expressed the M. tuberculosis PE31 in non-pathogenic Mycobacterium smegmatis strain (Ms_PE31) and demonstrated that PE31 significantly altered the cell facet features including colony morphology and biofilm formation. PE31 expressing M. smegmatis showed more resistant to the low pH, diamide, H2O2 and surface stress. Moreover, Ms_PE31 showed higher intracellular survival in macrophage THP-1 cells. Ms_PE31 significantly down-regulated the production of IL-12p40 and IL-6, while up-regulates the production of IL-10 in macrophages. Ms_PE31 also induced the expression of guanylate-binding protein-1 (GBP-1) in macrophages. Further analysis demonstrates that Ms_PE31 inhibits the caspase-3 activation and reduces the macrophages apoptosis. Besides, the NF-κB signaling pathway involves the interplay between Ms_PE31 and macrophages. Collectively, our finding identified that PE31 act as a functionally relevant virulence factor of M. tuberculosis.

The control method and dynamic performance of a magnetic suspension system, which is the core component of maglev trains, have a significant influence on the performance of the maglev train. Currently, a control strategy based on the current feedback is widely used. However, the stable range of control parameters is relatively small, making it difficult to identify stable regions for the control parameters. In addition, the strong interactions between the control parameters are cause issues in the control. In this paper, a control strategy based on the flux density observer is proposed using an analysis of the working principle and the structure of the suspension system. A nonlinear dynamic model of a maglev system is established by utilizing the state equation of the flux feedback. Based on the current and voltage feedback, a hybrid magnetic flux density observer is presented. According to the mathematical model, an adaptive sliding mode controller is designed to reduce the upper bound of both uncertainty and interference of the sliding mode controller. Finally, a theoretical analysis and the effectiveness of the proposed control strategy are verified using both simulations and experiments.

The levitation stability of maglev trains is determined by the interaction of vehicle-bridge dynamic characteristics. The state change of vehicle and track beam will affect the dynamic performance of maglev trains. In order to study the levitation characteristics of maglev trains, a coupled vehicle-bridge dynamic model based on an elastic beam was established to study the influence of beam stiffness and vehicle load on the dynamic performance of the maglev system. In the form of numerical simulation, the time-domain characteristics of key characteristic variables, such as levitation gap and vertical deflection of track beam, under different working conditions of stiffness and load were analyzed. The simulation shows that the levitation system can smoothly converge to the stable value under each working condition, which indicates the rationality of the field test. Based on the Shanghai Lingang medium-and-low-speed maglev test line, the maglev test was carried out, and the time-domain and frequency-domain characteristics of the above key variables were analyzed based on the measured data. The results show that the fluctuation of the levitation gap was affected by load and stiffness, and the law was consistent with the simulation results. The increase in load or the decrease in beam stiffness would lead to an increase in vertical deflection and vibration of the track beam. However, the train could still maintain good levitation performance under the above extreme conditions, which verified the reliability of the levitation system and the correctness of the simulation model. The conclusion of this paper can provide a reference for the design of the levitation system and track line of medium-and-low-speed maglev train.

The effects of curvature ratios on heat transfer and flow characteristics based on multi-sensor technique for the multi-layer winding hoses are studied. A multi-sensor–based experimental platform is established. The working fluid is oil and the multi-layer winding hoses are produced by winding a 13.00 mm diameter hydraulic hose on a reel from 1 to 12 turns. Experiments on different curvature ratios for the multi-layer winding hose with constant wall temperature are presented. A k–ε standard model has been applied to present the simulations on heat transfer and turbulent flow. In order to solve this model, a finite volume method has been used. The simulation results are compared with the experimental results. The simulation results and experimental results are in the similar varying trends. The effects of centrifugal force in the multi-layer winding hose on heat transfer and pressure drop have been discussed.

The Mycobacterium tuberculosis (M. tb) genome encodes a large number of hypothetical proteins, which need to investigate their role in physiology, virulence, pathogenesis, and host interaction. To explore the role of hypothetical protein Rv0580c, we constructed the recombinant Mycobacterium smegmatis (M. smegmatis) strain, which expressed the Rv0580c protein heterologously. We observed that Rv0580c expressing M. smegmatis strain (Ms_Rv0580c) altered the colony morphology and increased the cell wall permeability, leading to this recombinant strain becoming susceptible to acidic stress, oxidative stress, cell wall-perturbing stress, and multiple antibiotics. The intracellular survival of Ms_Rv0580c was reduced in THP-1 macrophages. Ms_Rv0580c up-regulated the IFN-γ expression via NF-κB and JNK signaling, and down-regulated IL-10 expression via NF-κB signaling in THP-1 macrophages as compared to control. Moreover, Ms_Rv0580c up-regulated the expression of HIF-1α and ER stress marker genes via the NF-κB/JNK axis and JNK/p38 axis, respectively, and boosted the mitochondria-independent apoptosis in macrophages, which might be lead to eliminate the intracellular bacilli. This study explores the crucial role of Rv0580c protein in the physiology and novel host-pathogen interactions of mycobacteria.

Persisters, stochastic dormant variants of normal bacteria cell, represent a significant portion of the survivors upon exposure to antibiotics and other environmental stresses, which contributes substantially to high level antibiotics tolerance. Glutamine is a crucial component of the Mycobacteria nitrogen pool that is indispensable for survival upon stresses. To study whether a synergistic effect exists between glutamine and antibiotics against Mycobacterial persisters, the efficacy of rifampicin alone or together with exogenous glutamine upon Mycobacterium smegmatis mc2 155 persisters was monitored. The result showed that glutamine decreases M. smegmatis tolerance to rifampicin upon starvation. The reactive oxygen species level of the strains treated with rifampicin and glutamine increased. The synergism of glutamine and rifampicin to kill persisters might derive from altering the oxidative phosphorylation and TCA cycle, as both evidenced by both ATP level increase and transcriptome change. Glutamine might represent a synergistic agent of rifampicin to kill Mycobacteria persisters.

To study the characteristics of the coupling vibration between a maglev vehicle and its track beam system and to improve the performance of the levitation system, a new type of vibration test bench was developed. Take a single maglev frame as the study object; simulation of the coupling vibration of the maglev vehicle, levitation system, and track beam were achieved. In addition, all types of real track irregularity excitations can be simulated using hydraulic actuators of the test bench. To expand the research scope, a simulation model was developed that can conduct the simulation research synergistically with the test bench. Based on a dynamics model of the test bench, the dynamics simulation method determined the influence on the levitation control performance of three factors: the track beam support stiffness, the track beam mass, and the track irregularity. The vibration resonance phenomenon of the vehicle/track system was reproduced by the dynamics simulation, and a portion of the simulation results were validated by the test results. By combining the test bench and the dynamics model, experiments can be guided by the simulation results, and the experimental results can validate the dynamics simulation results.

In order to ensure the safe operation of electromagnetic suspension (EMS) maglev trains, it is necessary to pay attention to the control loop performance of the suspension system. The suspension system with closed-loop control is tuned to achieve excellent performance at its early stage of operation. After running for a period of time, the control loop may encounter problems e.g., degraded operation, and paralysis may occur in severe cases. In order to quantify the control performance of the suspension system in an explicable manner, this paper proposed a data-driven control loop performance evaluation method based on fractal analysis, which does not require any external sensors and can be applied without data source restrictions such as dimension, volume and resolution. The control loop performances of such suspension systems were monitored, analysed, and evaluated by cross-sectional study, based on the field data of a commercial operation line in the commissioning stage. Furthermore, the track condition was revealed by capturing performance changes of the suspension system running on different guideway girders. The results demonstrate that the proposed method enables early warning of the degeneration of the suspension systems and the track. [ABSTRACT FROM AUTHOR]