Intrauterine growth retardation (IUGR) has been linked to the onset of diseases in adulthood, including type 2 diabetes, and has been proposed to result from altered gene regulation patterns due to epigenetic modifications of developmental genes. To determine whether epigenetic modifications may play a role in the development of adult diabetes following IUGR, we used a rodent model of IUGR that expresses lower levels of Pdx1, a pancreatic and duodenal homeobox 1 transcription factor critical for beta cell function and development, which develops diabetes in adulthood. We found that expression of Pdx1 was permanently reduced in IUGR beta cells and underwent epigenetic modifications throughout development. The fetal IUGR state was characterized by loss of USF-1 binding at the proximal promoter of Pdx1, recruitment of the histone deacetylase 1 (HDAC1) and the corepressor Sin3A, and deacetylation of histones H3 and H4. Following birth, histone 3 lysine 4 (H3K4) was demethylated and histone 3 lysine 9 (H3K9) was methylated. During the neonatal period, these epigenetic changes and the reduction in Pdx1 expression could be reversed by HDAC inhibition. After the onset of diabetes in adulthood, the CpG island in the proximal promoter was methylated, resulting in permanent silencing of the Pdx1 locus. These results provide insight into the development of type 2 diabetes following IUGR and we believe they are the first to describe the ontogeny of chromatin remodeling in vivo from the fetus to the onset of disease in adulthood.

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State estimation of random dynamical systems with noisy observations has been an important problem in many areas of science and engineering. Efficient new algorithms to estimate the present and future state of a dynamic signal based upon corrupted, distorted, and possibly partial observations of the signal are required. Since the true state is usually hidden and evolves according to its own dynamics, the objective of this work is to get an optimal estimation of the true state via noisy observations. The theory of filtering provides a recursive procedure for estimating an evolving signal or state from a noisy observation process. We consider a particle filter approach for nonlinear filtering in multiscale dynamical systems. Particle filters represent the posterior conditional distribution of the state variables by a system of particles, which evolves and adapts recursively as new information becomes available. Particle filters suffer from computational inefficiency when applied to high dimensional problems. In practice, large numbers of particles may be required to provide adequate approximations in higher dimensional poblems. In several high dimensional applications, after a sequence of updates, the particle system will often collapse to a single point. With the help of rigorous dimensional reduction methods, particle filters could regain their versatility. Based on our theoretical developments (Park, J. H., Sri Namachchivaya, N., and Sowers, R. B., 2008, “A Problem in Stochastic Averaging of Nonlinear Filters,” Stochastics Dyn., 8(3), pp. 543–560; Park, J. H., Sowers, R. B., and Sri Namachchivaya, N., 2010, “Dimensional Reduction in Nonlinear Filtering,” Nonlinearity, 23(2), pp. 305–324), we devise an efficient particle filter algorithm, which is applicable to high dimensional multiscale nonlinear filtering problems. In this paper, we present the homogenized hybrid particle filtering method that combines homogenization of random dynamical systems, reduced order nonlinear filtering, and particle methods.

The purpose of this work is to develop an averaging approach to study the dynamics of a vibro-impact system excited by random perturbations. As a prototype, we consider a noisy single-degree-of-freedom equation with both positive and negative stiffness and achieve a model reduction, i.e., the development of rigorous methods to replace, in some asymptotic regime, a complicated system by a simpler one. To this end, we study the equations as a random perturbation of a two-dimensional weakly dissipative Hamiltonian system with either center type or saddle type fixed points. We achieve the model-reduction through stochastic averaging. Examination of the reduced Markov process on a graph yields mean exit times, probability density functions, and stochastic bifurcations. Transactions of the ASME Downloaded From: https://appliedmechanics.asmedigitalcollection.asme.org/ on 06/12/2015 Terms of Use: http://asme.org/termswhere the vector b and the matrix are given by Journal of Applied Mechanics NOVEMBER 2005, Vol. 72 / 863 Downloaded From: https://appliedmechanics.asmedigitalcollection.asme.org/ on 06/12/2015 Terms of Use: http://asme.org/terms

We consider the stochastic averaging of a particular degenerate problem in nonlinear filtering. In particular, we consider a problem on the cylinder with fast angular diffusion and slow axial diffusion. The observation process depends only on the fast angle. We show that as the separation of scales (between fast and slow) grows, one can approximate the filter for the original problem by a much simpler one. Although our problem is similar to a number of others considered in the literature, the techniques required are different from the known ones.

With the demand for low‐power‐operating artificial intelligence systems, bio‐inspired memristor devices exhibit potential in terms of high‐density memory functions and the emulation of the synaptic dynamics of the human brain. The 2D material MXene attracts considerable interest for use in resistive‐switching memory and artificial synapse devices owing to its excellent physicochemical properties in memristor devices. However, few memristive and synaptic MXene devices that display increased switching performances are reported, with no significant results. Herein, the conductivity of MXene (Ti3C2Tx) is engineered via etching and oxidation to enhance the switching performance of the device. The exceptional properties of partially oxidized MXene memristors include large memory windows and low threshold biases, and the complex spike‐timing‐dependent plasticity synaptic rules are also emulated. The low threshold potential distribution, reliable retention time (104 s), and distinct resistance states with a high ON–OFF ratio (>104) are the main memory‐related features of this device. The experimentally determined switching potentials of the optimized device are also uniformly distributed, according to a statistical probability‐based approach. This investigation may promote the essential material properties for use in high‐density non‐volatile memory storage and artificial synapse systems in the field of innovative nanoelectronic devices.

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