Overexpression of Tropomyosin Isoform Tpm3.1 Does Not Alter Synaptic Function in Hippocampal Neurons

Tropomyosin (Tpm) has been regarded as the master regulator of actin dynamics. Tpms regulate the binding of the various proteins involved in restructuring actin. The actin cytoskeleton is the predominant cytoskeletal structure in dendritic spines. Its regulation is critical for spine formation and long-term activity-dependent changes in synaptic strength. The Tpm isoform Tpm3.1 is enriched in dendritic spines, but its role in regulating the synapse structure and function is not known. To determine the role of Tpm3.1, we studied the synapse structure and function of cultured hippocampal neurons from transgenic mice overexpressing Tpm3.1. We recorded hippocampal field excitatory postsynaptic potentials (fEPSPs) from brain slices to examine if Tpm3.1 overexpression alters long-term synaptic plasticity. Tpm3.1-overexpressing cultured neurons did not show a significantly altered dendritic spine morphology or synaptic activity. Similarly, we did not observe altered synaptic transmission or plasticity in brain slices. Furthermore, expression of Tpm3.1 at the postsynaptic compartment does not increase the local F-actin levels. The results suggest that although Tpm3.1 localises to dendritic spines in cultured hippocampal neurons, it does not have any apparent impact on dendritic spine morphology or function. This is contrary to the functional role of Tpm3.1 previously observed at the tip of growing neurites, where it increases the F-actin levels and impacts growth cone dynamics.     

Neural networks for constrained optimization problems

This paper is concerned with utilizing neural networks and analogue circuits to solve constrained optimization problems. We propose a novel neural network architecture for solving a class of non-linear programming problems. the proposed neural network is then used, and if necessary modified, to solve minimum norm problems subject to linear constraints. Minimum norm problems have many applications in various areas, but we focus on their applications to the control of discrete dynamic processes. the applicability of the proposed neural network is demonstrated on numerical examples.     

Biofiltration of H<Subscript>2</Subscript>S-rich biogas using <Emphasis Type="Italic">Acidithiobacillus thiooxidans</Emphasis>

Biogas has limited use in energy generation mainly due to the presence of hydrogen sulfide (H₂S). Currently, most of the techniques employed in the removal of H₂S from biogas have a chemical base, with high material costs and generating secondary pollutants. Biological processes for H₂S removal have become effective and economical alternative techniques to traditional gas-treatment systems based on physicochemical techniques. Therefore, the aim of this work was to investigate the performance of a bench-scale biofilter for the removal of H₂S present in synthetic biogas. In addition, CO₂ and CH₄ concentrations in the outlet biogas were evaluated. The inoculum used in the experiment was composed of Acidithiobacillus thiooxidans fixed on a packing of wood chips. Synthetic biogas was supplied to the system with a composition of 60 % CH₄, 39 % CO₂ and 1 % H₂S. The biofilter operated continuously for 37 days with an average H₂S removal efficiency of 75 ± 13 % and maximum of 97 %. The elimination capacity of the system reached an average of 130 ± 23 g m^?3 h^?1 and a maximum of 169 g m³ h^?1. The biofiltration system showed an average reduction of only 6 % in CH₄ concentration from biogas. Thus, besides being efficient in the removal of H₂S, the system was able to maintain the biogas energy value.     

Robust output feedback stabilization of uncertain dynamic systems with bounded controllers

We propose a new class of infinitely many bounded output feedback controllers for uncertain dynamic systems with bounded uncertainties. No statistical information about the uncertainties is assumed. A variable structure systems approach is employed in the synthesis of the proposed output feedback controllers. The role of the system zeros in the output feedback stabilization using the variable structure approach is discussed. We show that the proposed controllers guarantee the practical stability of the closed-loop system and give estimates of the regions of practical stability.     

Estimating regions of asymptotic stability with sliding for relay-control systems

The object of this article is the estimation of stability boundaries and regions of asymptotic stability with sliding for a class of single-input relay-control systems. The direct method of Lyapunov is used to obtain these estimates. A coordinate transformation that brings the system into a special canonical form is utilized to facilitate the stability analysis. The proposed approach to stability regions estimation is applied to a class of second-order systems, and analytical expressions for stability regions are derived.Recommended by K. Mizukami     

On discrete-time variable structure sliding mode control

The purpose of this paper is to show the limitations of discrete-time variable structure sliding mode control and that the equivalent control must be used in order to have sliding in a neighborhood of the switching surface. Conflicting requirements for the sliding mode controller behavior in the continuous and discrete-time domains are revealed and analyzed. A linear control law for an uncertain discrete-time linear plant, with bounded uncertainties, is analyzed and its superiority over nonlinear controllers is demonstrated. The conclusion of the obtained results is that in the discrete-time variable structure sliding mode controller design, unlike in the continuous-time, the designer may have limited flexibility in selecting controller architectures.     

On the Lyapunov stability of discrete-time processes modeled by difference inclusions

The subject of this note is the stability in the sense of Lyapunov of difference inclusions. Connection with the convergence of algorithms is also discussed.     

Low-order state estimators and compensators for dynamical systems with unknown inputs

We propose a new type of state estimator for a class of dynamical systems with unknown inputs. We synthesize the combined low-order estimator-controller compensator and show the stability of the closed-loop system. The results are illustrated with a numerical example.     

On the estimation of sliding domains and stability regions of variable structure control systems with bounded controllers

This article examines the problem of the control of a class of multivariable linear time-invariant uncertain dynamic systems with bounded controllers using the variable structure control (VSC) approach and the second method of Lyapunov. A special coordinate transformation is utilized to facilitate the analysis. Sliding domains and estimates of the domain of attraction along with the regions of asymptotic stability (RAS) are obtained with the aid of Lyapunov-type arguments. Numerical examples are given to illustrate the approach developed in the article.Dedicated to the memory of Professor Janislaw M. Skowronski     

Variable neural adaptive robust controllers for uncertain systems

A class of variable neural adaptive robust controllers is proposed. Essential components of the proposed controllers are raised‐cosine radial basis function (RCRBF) neural networks that can vary their structures dynamically by adding or removing RBFs online. The compact support of the RCRBFs alleviate the problem of determining the parameters of the RBFs and can, together with a simple adding and removing algorithm, significantly reduce the computational effort in the training process of the network. The stability of the overall closed‐loop system is analyzed using the Lypaunov type of arguments. It is guaranteed that the tracking error of the closed‐loop systems driven by the proposed controller is uniformly ultimately bounded. Copyright © 2007 John Wiley & Sons, Ltd.