Calcium cycling is a major determinant of cardiac function. S100A1 is the most abundant member of the calcium-binding S100 protein family in myocardial tissue. S100A1 interacts with a variety of calcium regulatory proteins such as SERCA2a, ryanodine receptors, L-type calcium channels and Na+/Ca2+ exchangers, thus enhancing calcium cycling. Aside from this major function, S100A1 has an important role in energy balance, myofilament sliding, myofilament calcium sensibility, titin-actin interaction, apoptosis and cardiac remodeling. Apart from its properties regarding cardiomyocytes, S100A1 is also important in vessel relaxation and angiogenesis. S100A1 potentiates cardiac function thus increasing the cardiomyocytes’ functional reserve; this is an important feature in heart failure. In fact, S100A1 seems to normalize cardiac function after myocardial infarction. Also, S100A1 is essential in the acute response to adrenergic stimulation. Gene therapy experiments show promising results, although further studies are still needed to reach clinical practice. In this review, we aim to describe the molecular basis and regulatory function of S100A1, exploring its interactions with a myriad of target proteins. We also explore its functional effects on systolic and diastolic function as well as its acute actions. Finally, we discuss S100A1 gene therapy and its progression so far., S. Duarte-Costa, R. Castro-Ferreira, J. S. Neves, A. F. Leite-Moreira., and Obsahuje bibliografii
The saccadic eye movement related potentials (SEMRPs) enable to study brain mechanisms of the sensorimotor integration. SEMRPs provide insight into various cognitive mechanisms related to planning, programming, generation and execution of the saccadic eye movements. SEMRPs can be used to investigate pathophysiological mechanisms of several disorders of the central nervous system. Here we shortly summarize basic findings concerning the significance of SEMRP components, their relationship to the functional brain asymmetry and visual attention level as well as changes related to certain neuropsychological disorders., F. Jagla, M. Jergelová, I. Riečanský., and Obsahuje bibliografii a bibliografické odkazy
The purpose of this paper is to apply second order η-approximation method introduced to optimization theory by Antczak \cite{2} to obtain a new second order η-saddle point criteria for vector optimization problems involving second order invex functions. Therefore, a second order η-saddle point and the second order η-Lagrange function are defined for the second order η-approximated vector optimization problem constructed in this approach. Then, the equivalence between an (weak) efficient solution of the considered vector optimization problem and a second order η-saddle point of the second order η-Lagrangian in the associated second order η-approximated vector optimization problem is established under the assumption of second order invexity.
In this paper, by using the second order η-approximation method introduced by Antczak \cite{antczak3}, new saddle point results are obtained for a nonlinear mathematical programming problem involving second order invex functions with respect to the same function η. Moreover, a second order η-saddle point and a second order η-Lagrange function are defined for the so-called second order η-approximated optimization problem constructed in this method. Then, the equivalence between an optimal solution in the original mathematical programming problem and a second order η-saddle point of the second order η -Lagrangian in the associated second order η-approximated optimization problem is established. Finally, some example of using this approach to characterize of solvability of some O.R. problem is given.
This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach. Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP bipartite consensus algorithm, which guarantees that the DP strategy only works on competitive pairs of agents. We then prove that the proposed algorithm can achieve the mean square bipartite consensus and (p,r)-accuracy. Furthermore, a differential privacy analysis is conducted, which shows that the performance of privacy protection is positively correlated with the number of neighbors. Thus, a practical method is established for the agents to select their own privacy levels. Finally, the simulation results are presented to demonstrate the validity of the proposed safe consensus algorithm.