Huntington’s disease (HD) is a hereditary neurodegenerative disease that is caused by polyglutamine expansion within the huntingtin (HTT) gene. One of the cellular activities that is dysregulated in HD is store-operated calcium entry (SOCE), a process by which Ca2+ release from the endoplasmic reticulum (ER) induces Ca2+ influx from the extracellular space. HTT-associated protein-1 (HAP1) is a binding partner of HTT. The aim of the present study was to examine the role of HAP1A protein in regulating SOCE in YAC128 mice, a transgenic model of HD. After Ca2+ depletion from the ER by the activation of inositol-(1,4,5)triphosphate receptor type 1 (IP3R1), we detected an increase in the activity of SOC channels when HAP1 protein isoform HAP1A was overexpressed in medium spiny neurons (MSNs) from YAC128 mice. A decrease in the activity of SOC channels in YAC128 MSNs was observed when HAP1 protein was silenced. In YAC128 MSNs that overexpressed HAP1A, an increase in activity of IP3R1 was detected while the ionomycin-sensitive ER Ca2+ pool decreased. 6-Bromo-N-(2-phenylethyl)-2,3,4,9-tetrahydro-1H-carbazol-1-amine hydrochloride (C20H22BrClN2), identified in our previous studies as a SOCE inhibitor, restored the elevation of SOCE in YAC128 MSN cultures that overexpressed HAP1A. The IP3 sponge also restored the elevation of SOCE and increased the release of Ca2+ from the ER in YAC128 MSN cultures that overexpressed HAP1A. The overexpression of HAP1A in the human neuroblastoma cell line SK-N-SH (i.e., a cellular model of HD (SK-N-SH HTT138Q)) led to the appearance of a pool of constitutively active SOC channels and an increase in the expression of STIM2 protein. Our results showed that HAP1A causes the activation of SOC channels in HD models by affecting IP3R1 activity.
Introduction. The study of heat exchange transients in the climate system “Heater-Ventilator-Room”, when ventilator capacity varies step-wise, is presented. The construction of functional relations between inputs and outputs of the system is the object of special attention. This allows for a non-parametric identification of impulse responses in the system for simulation and control. Materials and methods. The climate system is represented by a combination of several different-type elements with step inputs and experimental data as outputs. Mathematical models of the elements are governed by Volterra integral equation of the 2nd kind. Solution of this equation is an ill-posed problem, and specifics of identification experiments do not allow applying computational methods of classical regularization algorithms. A non-parametric identification of impulse responses for the elements is performed by the authors’ stable algorithm with due regard for real technical systems specifics. The algorithm is founded on stable differentiation by smoothing cubic splines with optimal smoothing parameter estimation and special type boundary conditions. Results. Non-parametric identification algorithm is adapted for the investigated climate system. The inverse problems of impulse responses identification and the direct problems of heat flux reactions prediction are solved. A high convergence of theoretical and experimental data is shown. Conclusions. The behavior of the transients is predictable for the climate system under the particular operation mode. The algorithm proposed takes proper account of practical problems specifics. The results obtained suggest the efficiency of the algorithm for applied identification problems solutions in real complex technical systems.
Математические модели многих технических систем имеют вид интегрального уравнения Вольтерра I рода с разностным ядром. Для таких систем задача идентификации заключается в построении оценки для импульсной переходной функции системы по измеренным (с шумами) значениям входного и выходного сигналов и является некорректно поставленной. В недавней работе авторов предложен устойчивый алгоритм идентификации, использующий аппарат сглаживающих кубических сплайнов для вычисления первых производных входного и выходного сигналов. К сожалению, сглаживающие кубические сплайны неудовлетворительно фильтруют аномальные измерения. Поэтому предложен двухшаговый алгоритм идентификации, на первом шаге которого аномальные измерения удаляются с использованием пространственно-локального фильтра, а затем строятся сглаживающие сплайны Volterra integral equation of the first kind often represents stationary dynamic systems. For such a model, the non-parametric identification problem reduces to the estimation of pulse transition characteristics (that is the kernel of integral equation) from the registered noise-contaminated values of input and output signals. To formulate stable solution for identification problem authors propose algorithm that estimates pulse transition characteristics by solving Volterra integral equation of the second kind and involving first derivatives of input and output signals application that corresponds to non-stable problem. Smoothing cubic splines employed in robust calculation of first derivatives allow finding a stable solution of identification problem even when input and output signals of system identified are essentially noise-contaminated. Unfortunately, measured values of input and output signals also contain anomalous measurements such as pulse noises, glitches, etc. Such measurements are poorly smoothable by splines that cause high levels of first derivatives errors and, conversely, significant pulse transition characteristics identification errors of dynamic system. For all the reasons aforementioned, in this paper authors present the new stable two-step identification algorithm in case of anomalous measurements. The first step of the algorithm is for non-linear local-spatial combined filtration procedure of input and output signals that helps to effectively remove anomalous measurements. At the second step, smoothing cubic splines are used to calculate stable first derivatives of previously filtered signals. An extensive computational experiment showed the effectiveness of the proposed algorithm, which allows solving the identification problem with acceptable accuracy in practice even at high intensity of anomalous measurements. The experimental results give reason to recommend this algorithm for solving practical problems of identifying stationary systems, the mathematical model of which is the Voltaire integral equation of the first kind with a difference kernel
In this paper, we propose a new algorithm for constructing an integral model of a nonlinear dynamic system of the “input–output” type in the form of a quadratic segment of the Volterra integro-power series (polynomial). We consider nonparametric identification of models using physically realizable piecewise linear test signals in the time domain. The advantage of the presented approach is to obtain explicit formulas for calculating the transient responses (Volterra kernels), which determine the unique solution of the Volterra integral equations of the first kind with two variable integration limits. The numerical method proposed in the paper for solving the corresponding equations includes the use of smoothing splines. An important result is that the constructed identification algorithm has a low methodological error.
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