Hepatocyte transformation is induced by laminin γ2 monomer.

Serum laminin-γ2 monomer (Lm-γ2m) is a potent predictive biomarker for hepatocellular carcinoma (HCC) onset in patients with hepatitis C infection who achieve a sustained virologic response with liver cirrhosis (LC) and for the onset of extrahepatic metastases in early-stage HCC. Although Lm-γ2m involvement in late-stage cancer progression has been well investigated, its precise roles in HCC onset remain to be systematically investigated. Therefore, we analyzed an HCC model, human hepatocytes and cholangiocytes, and surgically resected liver tissues from patients with HCC to understand the roles of Lm-γ2m in HCC onset. Ck-19- and EpCAM-positive hepatic progenitor cells (HPCs) in the liver of pdgf-c transgenic HCC mouse model with ductular reaction showed ectopic expression of Lm-γ2m. Forced expression of Lm-γ2m in hepatocytes adjacent to HPCs resulted in enhanced tumorigenicity, cell proliferation, and migration in immortalized hepatocytes, but not in cholangiocytes in vitro. Further, pharmacological inhibition of epidermal growth factor receptor (EGFR) and c-Jun activator JNK suppressed Lm-γ2m-induced hepatocyte transformation, suggesting the involvement of EGFR/c-Jun signaling in the transformation, leading to HCC development. Finally, immunohistochemical staining of HCC tissues revealed a high level of Lm-γ2 expression in the HPCs of the liver with ductular reaction in normal liver adjacent to HCC tissues. Overall, HPC-derived Lm-γ2m in normal liver with ductular reaction acts as a paracrine growth factor on surrounding hepatocytes and promotes their cellular transformation through the EGFR/c-Jun signaling pathway. Furthermore, this is the first report on Lm-γ2m expression detected in the normal liver with ductular reaction, a human precancerous lesion of HCC.

Decentralized stabilization of large-scale linear parameter varying systems.

In stabilization of a Large-Scale System (LSS), the decentralized nature of the controller is a significant issue, because centralized controllers are difficult and impractical for real-time implementation. The designing procedure for decentralized controllers should guarantee the stability of the overall LSS and at the same time, allow limited information exchange in the LSS. In this paper, a decentralized controller for a nonlinear LSS modeled by a Linear Parameter Varying (LPV) model is designed. The controller design procedure is formulated as a convex feasibility problem which can be solved by finding a feasible answer to some Linear Matrix Inequalities (LMIs). The solution to this feasibility problem assures a fully decentralized controller where information exchange among local controllers is forbidden and, only data transfer among each controller and its corresponding subsystem is allowed. In the proposed approach, the Lyapunov function of the LSS equipped with local controllers is considered as the sum of the Lyapunov functions of all subsystems. Then, the stability conditions are derived to assure the stability of the LSS. After designing a decentralized controller to ensure LSS stability, the same approach is exploited for H∞ and H2 performance improvement of the LSS in the presence of disturbances. To verify the efficacy of the designed controller, a large-scale power system which is a practical example is considered, and the proposed approach is applied on it. The simulation results prove the appropriateness of the designed local controllers.

Simultaneous model prediction and data-driven control with relaxed assumption on the model.

This paper aims to design a Model Predictive Control (MPC) law based on the time series data gathered from the input and output of a system. An Auto-Regressive Integrated Moving Average (ARIMA) model with unknown parameters and an unknown sequence of controller signal are considered for the system. Based on a window of data, an optimization problem is formulated which can find the optimal unknown model parameters and controller sequence, simultaneously. This problem is a non-convex optimization problem with many non-convex constraints and difficult to solve. Therefore, a transformation is developed which can transfer the optimization problem to an equivalent problem with convex constraints and a non-convex objective function. This new problem is much easier to solve with the present solvers. The effectiveness of the overall approach is proved via several examples that reveal satisfaction and convincingness.

A novel robust discrete-time integral sliding mode tracking control design for time-varying delay MIMO systems with unknown uncertainties.

This paper proposes a novel robust tracking control scheme for discrete time linear uncertain Multiple-Input Multiple-Output (MIMO) systems subject to time-varying delay on the states. The considered system is affected by unknown but norm bounded uncertainties on parameters as well as matched disturbances on the states. The designed controller is based upon a proposed novel integral sliding surface and a new switching type of reaching law. Sufficient conditions based on Linear Matrix Inequalities (LMIs) and a suitable Lyapunov-Krasovskii Functional (LKF) are derived in order to guarantee the asymptotic stability of such system. The proposed controller ensures a good tracking performance despite the presence of the time varying delay and the matched/unmatched disturbances. Moreover and thanks to the proposed integral surface, the time reaching phase is eliminated and the chattering phenomenon is significantly reduced. The proposed controller is applied on an Autonomous Underwater Vehicle (AUV) to follow a prescribed desired trajectory. The simulation results illustrate the effectiveness of such controller.

Sampled-data exponential consensus of multi-agent systems with Lipschitz nonlinearities.

In this paper, the exponential consensus of leaderless and leader-following multi-agent systems with Lipschitz nonlinear dynamics is illustrated with aperiodic sampled-data control using a two-sided loop-based Lyapunov functional (LBLF). Firstly, applying input delay approach to reformulate the resulting sampled-data system as a continuous system with time-varying delay in the control input. A two-sided LBLF which captures the information on sampled-data pattern is constructed and the symmetry of the Laplacian matrix together with Newton-Leibniz formula have been employed to obtain reduced number of decision variables and decreased LMI dimensions for the exponential sampled-data consensus problem. Subsequently, an aperiodic sampled-data controller was designed to simplify and enhance stability conditions for computation and optimization purposes in the proposed approach. Finally, based on the controller design, simulation examples including the power system are proposed to illustrate the theoretical analysis, moreover, a larger sampled-data interval can be acquired by this method than other literature, thereby conserving bandwidth and reducing communication resources.

BrrA02.LMI1 Encodes a Homeobox Protein That Affects Leaf Margin Development in Brassica rapa.

Leaf margin morphology is an important quality trait affecting the commodity and environmental adaptability of crops. Brassica rapa is an ideal research material for exploring the molecular mechanisms underlying leaf lobe development. Here, we identified BrrA02.LMI1 to be a promising gene underlying the QTL qBrrLLA02 controlling leaf lobe formation in B. rapa, which was detected in our previous study. Sequence comparison analysis showed that the promoter divergences were the most obvious variations of BrrA02.LMI1 between parental lines. The higher expression level and promoter activity of BrrA02.LMI1 in the lobe-leafed parent indicated that promoter variations of BrrA02.LMI1 were responsible for elevating expression and ultimately causing different allele effects. Histochemical GUS staining indicated that BrrA02.LMI1 is mainly expressed at the leaf margin, with the highest expression at the tip of each lobe. Subcellular localization results showed that BrrA02.LMI1 was in the nucleus. The ectopic expression of BrrA02.LMI1 in A. thaliana resulted in a deep leaf lobe in the wild-type plants, and lobed leaf formation was disturbed in BrrA02.LMI11-downregulated plants. Our findings revealed that BrrA02.LMI1 plays a vital role in regulating the formation of lobed leaves, providing a theoretical basis for the selection and breeding of leaf-shape-diverse varieties of B. rapa.

Preliminary Evaluation of 18F-Labeled Benzylguanidine Analogs as NET Tracers for Myocardial Infarction Diagnosis.

PURPOSE: Heart failure (HF) remains a major cause of late morbidity and mortality after myocardial infarction (MI). To date, no clinically established 18F-labeled sympathetic nerve PET tracers for monitoring myocardial infarction are available. Therefore, in this study, we synthesized a series of 18F-labeled benzyl guanidine analogs and evaluated their efficacy as cardiac neuronal norepinephrine transporter (NET) tracers for myocardial imaging. We also investigated the preliminary diagnostic capabilities of these tracers in myocardial infarction animal models, as well as the structure-activity relationship of these tracers. PROCEDURES: Three benzyl guanidine-NET tracers, including [18F]1, [18F]2, and [18F]3, were synthesized and evaluated in vivo as PET tracers in a myocardial infarction mouse model. [18F]LMI1195 was used as a positive control for the tracers. H&E staining of the isolated myocardial infarction heart tissue sections was performed to verify the efficacy of the selected PET tracer. RESULTS: Our data show that [18F]3 had a moderate decay corrected labeling yield (~10%) and high radiochemical purity (>95%) compared to other tracers. The uptake of [18F]3 in normal mouse hearts was 1.7±0.1%ID/cc at 1 h post-injection (p. i.), while it was 2.4±0.1, 2.6±0.9, and 2.1±0.4%ID/cc in the MI mouse hearts at 1, 2, and 3 days after surgery, respectively. Compared with [18F]LMI1195, [18F]3 had a better myocardial imaging effect in terms of the contrast between normal and MI hearts. The area of myocardial infarction shown by PET imaging corresponded well with the infarcted tissue demonstrated by H&E staining. CONCLUSIONS: With an obvious cardiac uptake contrast between normal mice and the myocardial infarction mouse model, [18F]3 appears to be a potential tool in the diagnosis of myocardial infarction. Therefore, it is necessary to conduct further structural modification studies on the chemical structure of [18F]3 to improve its in vivo stability and diagnostic detection ability to achieve reliable and practical imaging effects.

Robust Stabilization of Linear Time-Delay Systems under Denial-of-Service Attacks.

This research examines new methods for stabilizing linear time-delay systems that are subject to denial-of-service (DoS) attacks. The study takes into account the different effects that a DoS attack can have on the system, specifically delay-independent and -dependent behaviour. The traditional proportional-integral-derivative (PID) acts on the error signal, which is the difference between the reference input and the measured output. The approach in this paper uses what we call the PID state feedback strategy, where the controller acts on the state signal. Our proposed strategy uses the Lyapunov-Krasovskii functional (LKF) to develop new linear matrix inequalities (LMIs). The study considers two scenarios where the time delay is either a continuous bounded function or a differentiable and time-varying function that falls within certain bounds. In both cases, new LMIs are derived to find the PID-like state feedback gains that will ensure robust stabilization. The findings are illustrated with numerical examples.

Beyond "Economic Immigration": Understanding the Role of Labor Market and Lifestyle Expectations in Technology Sector Newcomer Experiences in Canada.

Canada has long sought to disperse skilled immigration across the country, with the goal of promoting economic development, improving cultural diversity, and mitigating population decline. The Provincial Nominee Programs (PNPs) are one mechanism for achieving regionalized immigration: they allow Canadian provinces and territories to use labor market information (LMI) to identify in-demand skills and offer visas to newcomers who match local needs. However, even when LMI is accurate, many factors can prevent newcomer access to local labor markets, particularly in third-tier cities (populations of 100,000 to 500,000), including credential recognition, discrimination, and a lack of settlement infrastructure. This paper centers the stories of three newcomers to Canada, each with senior technology sector experience and arriving through PNPs into third-tier cities. Amidst well-established themes in settlement narratives, such as housing affordability, family, lifestyle, and the role of Local Immigration Partnerships (LIPs), this paper suggests that newcomers arriving under programs such as the PNPs may experience LMI congruence or incongruence: the degree to which expectations of a labor market (shaped by being selected for immigration based on particular in-demand skills) match or do not match newcomers' real experiences of labor market access. Policymakers and institutions that use LMI to guide decisions may consider two lessons from the narratives offered in this study: one, the continued importance of reducing barriers to labor market entry for newcomers, and two, the possibility that LMI congruence and accurate expectations play a role in retention.

Lower triangle factor based sliding mode observers design for T-S fuzzy systems with faults.

The paper mainly focuses on the fault estimation for a class of Takagi-Sugeno (T-S) fuzzy systems with faults. A synthetic estimation observer design method is proposed. The synthetic estimation observer can cover the robust observer, adaptive observer and intermediate estimation observer in the existing study work. Based on the observer design method, an LTF-based sliding mode observer (SMO) is designed for the T-S fuzzy system in consideration. Under the observer, the fault occurring in the system can be well estimated. The obtained LMI-based conditions guarantee the states of the error dynamics to be uniformly ultimately bounded. A numerical example tests the proposed method.

Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach.

Most existing algorithms in mobile robotics consider a kinematic robot model for the the Simultaneous Localization and Mapping (SLAM) problem. However, in the case of autonomous vehicles, because of the increase in the mass and velocities, a kinematic model is not enough to characterize some physical effects as, e.g., the slip angle. For this reason, when applying SLAM to autonomous vehicles, the model used should be augmented considering both kinematic and dynamic behaviours. The inclusion of dynamic behaviour implies that nonlinearities of the vehicle model are most important. For this reason, classical observation techniques based on the the linearization of the system model around the operation point, such as the well known Extended Kalman Filter (EKF), should be improved. Consequently, new techniques of advanced control must be introduced to more efficiently treat the nonlinearities of the involved models. The Linear Parameter Varying (LPV) technique allows working with nonlinear models, making a pseudolinear representation, and establishing systematic methodologies to design state estimation schemes applying several specifications. In recent years, it has been proved in many applications that this advanced technique is very useful in real applications, and it has been already implemented in a wide variety of application fields. In this article, we present a SLAM-based localization system for an autonomous vehicle considering the dynamic behaviour using LPV techniques. Comparison results are provided to show how our proposal outperforms classical observation techniques based on model linearization.

Proposal of a New Parameter for Evaluating Muscle Mass in Footballers through Bioimpedance Analysis.

The evaluation of muscle mass in athletes correlates with sports performance directly. Bioimpedance vector analysis is a growing method of assessing body composition in athletes because it is independent of predictive formulas containing variables such as body weight, ethnicity, age, and sex. The study aims to propose a new parameter (Levi's Muscle Index, LMI) that evaluates muscle mass through raw bioelectrical data. A total of 664 male footballers underwent bioimpedance assessment during the regular season. LMI was correlated with body cell mass (BCM) and phase angle (PA) to establish efficacy. The footballers were 24.5 ± 5.8 years old, 180.7 ± 5.9 cm tall and weighed 76.3 ± 7.1 kg. The relationships were: LMI-BMI: r = 0.908, r2 = 0.824, p < 0.001; LMI-PA: r = 0.704, r2 = 0.495, p = 0.009 and PA-BCM: r = 0.491, r2 = 0.241, p < 0.001. The results obtained confirm that LMI could be considered a new parameter that provides reliable information to evaluate the muscle mass of athletes. Furthermore, the higher LMI-BCM relationship than PA-BCM demonstrates specificity for muscle mass evaluation in athletes regardless of body weight, ethnicity, age, and sex.

Robust adaptive fault detection and diagnosis observer design for a class of nonlinear systems with uncertainty and unknown time-varying internal delay.

This paper introduces a novel robust adaptive fault detection and diagnosis (FDD) observer design approach for a class of nonlinear systems with parametric uncertainty, unknown system fault and time-varying internal delays. The conditions for the existence of the proposed FDD are obtained based on the well-known Linear Matrix Inequalities (LMI) technique. Using Lyapunov stability theory, the adaptation laws for updating the observer weights and unknown faults estimation are derived based on which the convergence of the state estimation error to zero and asymptotic stability of the error dynamics are proven. Toward this, a new structural algorithm for FDD observer design is also derived based on LMIs. The performance of the proposed method is also investigated while applying to some industrial systems. Simulation results illustrate superior performance of the proposed method for the systems subject to time-varying unknown delays on states, uncertainty in nonlinear system modeling and unknown system faults.

Preview repetitive control for polytopic nonlinear discrete-time systems.

In this study, a preview repetitive control (PRC) strategy was developed for uncertain nonlinear discrete-time systems subjected to a previewable periodic reference signal. The proposed preview repetitive controller was designed such that the system output tracked a previewable periodic reference signal even with model uncertainties and nonlinear terms. An augmented two-dimensional (2D) model was constructed based on the 2D model approach and state augmented technique. Second, considering the state unmeasured and periodic tracking reference signal, a static output PRC law was designed using the linear matrix inequality (LMI) techniques. Finally, the effectiveness of the proposed controller was verified through two illustrative examples.

Fast actuator and sensor fault estimation based on adaptive unknown input observer.

This study evaluates the robust fault estimation problem of systems with actuator and sensor faults though the simultaneous use of unknown input disturbances and measurement noise. Specifically, an augmented descriptor system is preliminarily developed by creating an augmented state consisting of system states and sensor faults. Next, a novel fast adaptive unknown input observer (FAUIO) is proposed for the system to enhance its fault estimation performance. The existence condition of the novel FAUIO is then introduced for linear time-invariant systems with unknown input disturbances. Furthermore, the proposed FAUIO is extended to a class of Lipschitz nonlinear systems with unknown input disturbances and measurement noise to investigate the robust fault estimation problem. Accordingly, an H∞ performance index is employed to attenuate the influence of disturbances on fault estimation. Moreover, the linear matrix inequality (LMI) technique is applied to solve the designed FAUIO. Finally, the effectiveness of the developed FAUIO is validated via the simulation of two examples.

Improvements on stability conditions and control design of Takagi-Sugeno fuzzy descriptor systems.

Several methods to improve stabilization conditions of Takagi-Sugeno fuzzy descriptor systems (TSFDS) are presented. First, we prove that with a modified non-quadratic fuzzy Lyapunov function and a PDC controller, stabilization problems of TSFDS are reformulated as checking negativity of "triple" fuzzy summations, and then relaxed methods of T-S fuzzy systems can be directly applied to descriptor systems. In the sequel, two relaxed methods are extended to TSFDS based on slack decision variables and Polya's Theorem, respectively, but these conditions are only sufficient. Second, we design a non-quadratic fuzzy Lyapunov function which simultaneously consists of membership functions of derivative matrices and state matrices, and it generalizes previous related Lyapunov functions. Then with a non-PDC controller, not only sufficient but asymptotically necessary conditions for TSFDS are presented based on Polya's theorem. All conditions are cast into LMIs, and simulation examples illustrate improvements and effectiveness of these methods.

H∞ filtering for nonlinearly coupled complex networks subjected to unknown varying delays and multiple fading measurements.

In this paper, the robust filtering problem for uncertain complex networks with time-varying state delay and stochastic nonlinear coupling based on H∞ performance criterion is studied. The random connections of coupling nodes are represented by utilizing independent random variables and the multiple fading measurements phenomenon is characterized by introducing diagonal matrices with independent stochastic elements. Moreover, the probabilistic time-varying delays in the measurement outputs are described by white sequences with the Bernoulli distributions. Furthermore, All system's matrices are supposed to have uncertainty and a quadratic bound is assumed for nonlinear part of the network. This bound can be obtained by solving a sum of squares (SOS) optimization problem. By applying the Lyapunov theory, we design a robust filter for each node of the network so that the filtering error system is asymptomatically stable and the H∞ performances are met. Then, the parameters of the filters are achieved by solving a linear matrix inequality (LMI) feasibility problem. Finally, the applicability and performance of the proposed H∞ filtering approach are demonstrated via a practical example.

Imaging pheochromocytoma in small animals: preclinical models to improve diagnosis and treatment.

Pheochromocytomas (PCCs) and paragangliomas (PGLs), together referred to as PPGLs, are rare chromaffin cell-derived tumors. They require timely diagnosis as this is the only way to achieve a cure through surgery and because of the potentially serious cardiovascular complications and sometimes life-threatening comorbidities that can occur if left untreated. The biochemical diagnosis of PPGLs has improved over the last decades, and the knowledge of the underlying genetics has dramatically increased. In addition to conventional anatomical imaging by CT and MRI for PPGL detection, new functional imaging modalities have emerged as very useful for patient surveillance and stratification for therapy. The availability of validated and predictive animal models of cancer is essential for translating molecular, imaging and therapy response findings from the bench to the bedside. This is especially true for rare tumors, such as PPGLs, for which access to large cohorts of patients is limited. There are few animal models of PPGLs that have been instrumental in refining imaging modalities for early tumor detection, as well as in identifying and evaluating novel imaging tracers holding promise for the detection and/or treatment of human PPGLs. The in vivo PPGL models mainly include xenografts/allografts generated by engrafting rat or mouse cell lines, as no representative human cell line is available. In addition, there is a model of endogenous PCCs (i.e., MENX rats) that was characterized in our laboratory. In this review, we will summarize the contribution that various representative models of PPGL have given to the visualization of these tumors in vivo and we present an example of a tracer first evaluated in MENX rats, and then translated to the detection of these tumors in human patients. In addition, we will illustrate briefly the potential of ex vivo biological imaging of intact adrenal glands in MENX rats.

A New Data-Driven Control System for MEMSs Gyroscopes: Dynamics Estimation by Type-3 Fuzzy Systems.

In this study, a novel data-driven control scheme is presented for MEMS gyroscopes (MEMS-Gs). The uncertainties are tackled by suggested type-3 fuzzy system with non-singleton fuzzification (NT3FS). Besides the dynamics uncertainties, the suggested NT3FS can also handle the input measurement errors. The rules of NT3FS are online tuned to better compensate the disturbances. By the input-output data set a data-driven scheme is designed, and a new LMI set is presented to ensure the stability. By several simulations and comparisons the superiority of the introduced control scheme is demonstrated.

Health-aware control design based on remaining useful life estimation for autonomous racing vehicle.

The accurate estimation of the State of Charge (SOC) and an acceptable prediction of the Remaining Useful Life (RUL) of batteries in autonomous vehicles are essential for safe and lifetime optimized operation. The estimation of the expected RUL is quite helpful to reduce maintenance cost, safety hazards, and operational downtime. This paper proposes an innovative health-aware control approach for autonomous racing vehicles to simultaneously control it to the driving limits and to follow the desired path based on maximization of the battery RUL. To deal with the non-linear behavior of the vehicle, a Linear Parameter Varying (LPV) model is developed. Based on this model, a robust controller is designed and synthesized by means of the Linear Matrix Inequality (LMI) approach, where the general objective is to maximize progress on the track subject to win racing and saving energy. The main contribution of the paper consists in preserving the lifetime of battery and optimizing a lap time to achieve the best path of a racing vehicle. The control design is divided into two layers with different time scale, path planner and controller. The first optimization problem is related to the path planner where the objective is to optimize the lap time and to maximize the battery RUL to obtain the best trajectory under the constraints of the circuit. The proposed approach is formulated as an optimal on-line robust LMI based Model Predictive Control (MPC) that steered from Lyapunov stability. The second part is focused on a controller gain synthesis solved by LPV based on Linear Quadratic Regulator (LPV-LQR) problem in LMI formulation with integral action for tracking the trajectory. The proposed approach is evaluated in simulation and results show the effectiveness of the proposed planner for optimizing the lap time and especially for maximizing the battery RUL.