Sialic acid as the potential link between lipid metabolism and inflammation in the pathogenesis of atherosclerosis.

In the modern world, cardiovascular diseases have a special place among the most common causes of death. Naturally, this widespread problem cannot escape the attention of scientists and researchers. One of the main conditions preceding the development of fatal cardiovascular diseases is atherosclerosis. Despite extensive research into its pathogenesis and possible prevention and treatment strategies, many gaps remain in our understanding of this disease. For example, the concept of multiple low-density lipoprotein modifications was recently stated, in which desialylation is of special importance. Apart from this, sialic acids are known to be important contributors to processes such as endothelial dysfunction and inflammation, which in turn are major components of atherogenesis. In this review, we have collected information on sialic acid metabolism, analyzed various aspects of its implication in atherosclerosis at different stages, and provided an overview of the role of particular groups of enzymes responsible for sialic acid metabolism in the context of atherosclerosis.

Differential Neural Networks Prediction Using Slow and Fast Hybrid Learning: Application to Prognosis of Infectionsand Deaths of COVID-19 Dynamics.

This essay discusses a potential method for predicting the behavior of various physical processes and uses the COVID-19 outbreak to demonstrate its applicability. This study assumes that the current data set reflects the output of a dynamic system that is governed by a nonlinear ordinary differential equation. This dynamic system may be described by a Differential Neural Network (DNN) with time-varying weights matrix parameters. A new hybrid learning scheme based on the decomposition of the signal to be predicted. The decomposition considers the slow and fast components of the signal which is more natural to signals such as the ones corresponding to the number of infected and deceased patients who suffered of COVID 2019 sickness. The paper results demonstrate the recommended method offers competitive performance (70 days of COVID prediction) in comparison to similar studies.

Rational Continuous Neural Network Identifier for Singular Perturbed Systems With Uncertain Dynamical Models.

This study aims at designing a robust nonparametric identifier for a class of singular perturbed systems (SPSs) with uncertain mathematical models. The identifier structure uses a novel identifier based on a differential neural network (DNN) with rational form, which can take into account the multirate nature of SPS. The identifier uses a mixed learning law including a rational formulation of neural networks which is useful to solve the identification of the fast dynamics in the SPS dynamics. The rational form of the design is proposed in such a way that no-singularities (denominator part of the rational form never touches the origin) are allowed in the identifier dynamics. A proposed control Lyapunov function and a nonlinear parameter identification methodology yield to design the learning laws for the class of novel rational DNN which appears as the main contribution of this study. A complementary matrix inequality-based optimization method allows to get the smallest attainable convergence invariant region. A detailed implementation methodology is also given in the study with the aim of clarifying how the proposed identifier can be used in diverse SPSs. A numerical example considering the dynamics of the enzymatic-substrate-inhibitor system with uncertain dynamics is showing how to apply the DNN identifier using the multirate nature of the proposed DNN identifier for SPSs. The proposed identifier is compared to a classical identifier which is not taking into account the multirate nature of SPS. The benefits of using the rational form for the identifier are highlighted in the numerical performance comparison based on the mean square error (MSE). This example justifies the ability of the suggested identifier to reconstruct both the fast and slow dynamics of the SPS.

Sialic acid as the potential link between lipid metabolism and inflammation in the pathogenesis of atherosclerosis

In the modern world, cardiovascular diseases have a special place among the most common causes of death. Naturally, this widespread problem cannot escape the attention of scientists and researchers. One of the main conditions preceding the development of fatal cardiovascular diseases is atherosclerosis. Despite extensive research into its pathogenesis and possible prevention and treatment strategies, many gaps remain in our understanding of this disease. For example, the concept of multiple low-density lipoprotein modifications was recently stated, in which desialylation is of special importance. Apart from this, sialic acids are known to be important contributors to processes such as endothelial dysfunction and inflammation, which in turn are major components of atherogenesis. In this review, we have collected information on sialic acid metabolism, analyzed various aspects of its implication in atherosclerosis at different stages, and provided an overview of the role of particular groups of enzymes responsible for sialic acid metabolism in the context of atherosclerosis.

Animal models of human atherosclerosis: current progress.

Atherosclerosis retains the leading position among the causes of global morbidity and mortality worldwide, especially in the industrialized countries. Despite the continuing efforts to investigate disease pathogenesis and find the potential points of effective therapeutic intervention, our understanding of atherosclerosis mechanisms remains limited. This is partly due to the multifactorial nature of the disease pathogenesis, when several factors so different as altered lipid metabolism, increased oxidative stress, and chronic inflammation act together leading to the formation and progression of atherosclerotic plaques. Adequate animal models are currently indispensable for studying these processes and searching for novel therapies. Animal models based on rodents, such as mice and rats, and rabbits represent important tools for studying atherosclerosis. Currently, genetically modified animals allow for previously unknown possibilities in modelling the disease and its most relevant aspects. In this review, we describe the recent progress made in creating such models and discuss the most important findings obtained with them to date.

Animal models of human atherosclerosis: current progress

Atherosclerosis retains the leading position among the causes of global morbidity and mortality worldwide, especially in the industrialized countries. Despite the continuing efforts to investigate disease pathogenesis and find the potential points of effective therapeutic intervention, our understanding of atherosclerosis mechanisms remains limited. This is partly due to the multifactorial nature of the disease pathogenesis, when several factors so different as altered lipid metabolism, increased oxidative stress, and chronic inflammation act together leading to the formation and progression of atherosclerotic plaques. Adequate animal models are currently indispensable for studying these processes and searching for novel therapies. Animal models based on rodents, such as mice and rats, and rabbits represent important tools for studying atherosclerosis. Currently, genetically modified animals allow for previously unknown possibilities in modelling the disease and its most relevant aspects. In this review, we describe the recent progress made in creating such models and discuss the most important findings obtained with them to date.

Switching robust control for ozone generators using the attractive ellipsoid method.

This paper deals with a switching robust tracking feedback design for a corona-effect ozone generator. The generator is considered as a switched systems in the presence of bounded model uncertainties as well as external perturbations. Three nonlinear dynamic models under arbitrary switching mechanisms are considered assuming that a sample-switching times are known. The stabilization issue is achieved in the sense of a practical stability. We apply the newly elaborated (extended) version of the conventional attractive ellipsoid method (AEM) for this purpose. The same analysis was efficient to obtain the minimal size of region where the tracking error between the trajectories of the ozone generator and reference states converges. The numerically implementable sufficient conditions for the practical stability of systems are derived based on bilinear matrix inequalities (BMIs).

Uniform step-by-step observer for aerobic bioreactor based on super-twisting algorithm.

This paper describes a fixed-time convergent step-by-step high order sliding mode observer for a certain type of aerobic bioreactor system. The observer was developed using a hierarchical structure based on a modified super-twisting algorithm. The modification included nonlinear gains of the output error that were used to prove uniform convergence of the estimation error. An energetic function similar to a Lyapunov one was used for proving the convergence between the observer and the bioreactor variables. A nonsmooth analysis was proposed to prove the fixed-time convergence of the observer states to the bioreactor variables. The observer was tested to solve the state estimation problem of an aerobic bioreactor described by the time evolution of biomass, substrate and dissolved oxygen. This last variable was used as the output information because it is feasible to measure it online by regular sensors. Numerical simulations showed the superior behavior of this observer compared to the one having linear output error injection terms (high-gain type) and one having an output injection obtaining first-order sliding mode structure. A set of numerical simulations was developed to demonstrate how the proposed observer served to estimate real information obtained from a real aerobic process with substrate inhibition.

BTEX decomposition by ozone in gaseous phase.

Environment management is turning its efforts to control the air pollution. Nowadays, gas phase contaminants coming from different sources are becoming into the main cause of serious human illness. Particularly, benzene, toluene, ethylbenzene and xylene (BTEX) are getting more and more attention from the scientific community due the high level of volatilization showed by these compounds and their toxicity. Decomposition of these compounds using different treatments is requiring lots of new strategies based on novel options. In the present work the use of ozone was proposed as possible alternative treatment in the gaseous phase of VOC's liberated from water by stripping. This study deals with the decomposition by ozone in gaseous phase of model mixtures of BTEX stripped from water. The experiments were realized in a tubular reactor with fixed length (1.5 m length and diameter of 2.5 cm). The experiments were conducted in two stages: in the first one, organics was ventilated by oxygen flow to liberate BTEX to the gaseous phase; second stage deals with the liberated BTEX decomposition by ozone in the tubular reactor. Ozonation efficiency was determined measuring the VOC's concentration at the output of the tubular reactor. This concentration was compared to the concentration obtained at the input of the reactor. The obtained results confirm the possibility to use of ozone for the VOC's decomposition in gaseous phase. Also, the dynamic relationship between degradation and liberation was studied and characterized.

Application of a neural observer to phenols ozonation in water: simulation and kinetic parameters identification.

Presented in this study, a dynamic neural network (DNN) is employed to estimate the states dynamics of the phenols-ozone-water system. A new technique based on the dynamic neural network observer (DNNO) with relay (signum) term is applied to estimate the decomposition dynamics of phenols and to identify their kinetic parameters without any mathematical model usage. The decomposition of phenols (phenol (PH), 4-chlorophenol (4-CPH) and 2,4-dichlorophenol (2,4-DCPH)) and their mixture by ozone, realized in a semi-batch reactor, is considered as a process with uncertain model ("black-box"). Only one parameter monitoring, namely, the ozone concentration in gas phase in the reactor outlet, is measured during ozonation. The variation of this variable is used to obtain the summary characteristic curve for the phenols ozonation. Then, using the experimental decomposition dynamics of phenols and of their mixture, obtained by HPLC method, the proposed DNNO is applied to estimate the ozonation constants of phenols at the different pH 2-12. A good correspondence between the decomposition dynamics and the estimated ones by DNNO is obtained.

Learning through reinforcement for N-person repeated constrained games.

The design and analysis of an adaptive strategy for N-person averaged constrained stochastic repeated game are addressed. Each player is modeled by a stochastic variable-structure learning automaton. Some constraints are imposed on some functions of the probabilities governing the selection of the player's actions. After each stage, the payoff to each player as well as the constraints are random variables. No information concerning the parameters of the game is a priori available. The "diagonal concavity" conditions are assumed to be fulfilled to guarantee the existence and uniqueness of the Nash equilibrium. The suggested adaptive strategy which uses only the current realizations (outcomes and constraints) of the game is based on the Bush-Mosteller reinforcement scheme in connection with a normalization procedure. The Lagrange multipliers approach with a regularization is used. The asymptotic properties of this algorithm are analyzed. Simulation results illustrate the feasibility and the performance of this adaptive strategy.

Nonlinear adaptive trajectory tracking using dynamic neural networks.

In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dynamic neural networks. By means of a Lyapunov-like analysis we determine stability conditions for the identification error. Then we analyze the trajectory tracking error by a local optimal controller. An algebraic Riccati equation and a differential one are used for the identification and the tracking error analysis. As our main original contributions, we establish two theorems: the first one gives a bound for the identification error and the second one establishes a bound for the tracking error. We illustrate the effectiveness of these results by two examples: the second-order relay system with multiple isolated equilibrium points and the chaotic system given by Duffing equation.

Multimodal searching technique based on learning automata with continuous input and changing number of actions.

This paper describes a multimodal searching technique based on a stochastic automaton. The environment where the automaton operates corresponds to the function to be optimized which is assumed to be unknown function of a single parameter x. The admissible region of x is quantized into N subsets. The environment response is continuous (S-model). The complete set of actions of the automaton is divided into nonempty subsets. The action set is changing from instant to instant and is selected based on a probability distribution. These actions are in turn associated with the discrete values of the parameter x. Convergence and convergence rate results are presented. Simulation results illustrate the performance of this searching technique.