Surface topography and bioactive molecules can generate physicochemical cues that control proliferation and differentiation of neural cells. In this study, polystyrene (PS) submicron-patterns with different widths (400 and 800 nm) and depths (100 and 400 nm) were prepared and subsequently modified with polydopamine (PDA) by a coating method. We examined neurites of PC12 cells and human adipose-derived stem cells (hADSCs) incubated in neuronal induction medium containing nerve growth factor (NGF) and basic fibroblast growth factor (bFGF), respectively.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
October 2018
This paper presents the integration of reinforcement learning-based differential evolution (DE) with the cooperative coevolution (R-CCDE) method in a compensatory neuro-fuzzy controller (CNFC). The CNFC model employs compensatory fuzzy operations, which increase the adaptability and effectiveness of the controller. The R-CCDE method was used to determine an adequate control policy for nonlinear system problems.
View Article and Find Full Text PDFAm J Physiol Cell Physiol
November 2015
Endothelial cell migration is a fundamental process during angiogenesis and, therefore, a point of intervention for therapeutic strategies aimed at controlling pathologies involving blood vessel growth. We sought to determine the role of the gap junction protein connexin 43 (Cx43) in key features of angiogenesis in the central nervous system. We used an in vitro model to test the hypothesis that a complex of interacting proteins, including Cx43 and zonula occludens-1 (ZO-1), regulates the migratory behavior of cerebral endothelium.
View Article and Find Full Text PDFObjective: To test the hypothesis that Hcy impairs angiogenic outgrowth through an iNOS-dependent mechanism.
Methods: Adult C57Bl/6 mouse choroid explants were used in angiogenic outgrowth assays. Mouse microvascular endothelial cells were studied in culture during scrape-induced migration and dispersed cell locomotion experiments.
Hyperhomocysteinemia (HHcy) is a risk factor for cognitive impairment. The purpose of this study was to determine the temporal pattern of cerebral pathology in a mouse model of mild HHcy, because understanding this time course provides the basis for understanding the mechanisms involved. C57Bl/6 mice with heterozygous deletion cystathionine β-synthase (cbs (+/-); Het) were used as a model of mild HHcy along with their wild-type littermates (cbs (+/+); WT).
View Article and Find Full Text PDFObjective: Hcy is an independent risk factor for cerebrovascular disease and cognitive impairment. The purpose of this study was to elucidate the role of mGluR5 in Hcy-mediated impairment of cerebral endothelial wound repair.
Methods: Mouse CMVECs (bEnd.
Hyperhomocysteinemia (HHcy) disrupts nitric oxide (NO) signaling and increases nitrative stress in cerebral microvascular endothelial cells (CMVECs). This is mediated, in part, by protein nitrotyrosinylation (3-nitrotyrosine; 3-NT) though the mechanisms by which extracellular homocysteine (Hcy) generates intracellular 3-NT are unknown. Using a murine model of mild HHcy (cbs(+/-) mouse), we show that 3-NT is significantly elevated in cerebral microvessels with concomitant reductions in serum NO bioavailability as compared with wild-type littermate controls (cbs(+/+)).
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
March 2010
This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptive control for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptive control is used for linearized dynamics to minimize tracking error. The simulations of this hybrid controller, when compared with the proportional-integral-derivative (PID) controller showed enhanced performance.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
May 2009
A chronological overview of the applications of control theory to prosthetic hand is presented. The overview focuses on hard computing or control techniques such as multivariable feedback, optimal, nonlinear, adaptive and robust and soft computing or control techniques such as artificial intelligence, neural networks, fuzzy logic, genetic algorithms and on the fusion of hard and soft control techniques. This overview is not intended to be an exhaustive survey on this topic and any omissions of other works is purely unintentional.
View Article and Find Full Text PDF