5-hydroxymethyl-cytosine enrichment of non-committed cells is not a universal feature of vertebrate development

5-hydroxymethyl-cytosine (5-hmc) is a cytosine modification that is relatively abundant in mammalian pre-implantation embryos and embryonic stem cells (Esc) derived from mammalian blastocysts. Recent observations imply that both 5-hmc and Tet1/2/3 proteins, catalyzing the conversion of 5-methyl-cytosine to 5-hmc, may play an important role in self renewal and differentiation of Escs. here we assessed the distribution of 5-hmc in zebrafish and chick embryos and found that, unlike in mammals, 5-hmc is immunochemically undetectable in these systems before the onset of organogenesis. In addition, Tet1/2/3 transcripts are either low or undetectable at corresponding stages of zebrafish development. however, 5-hmc is enriched in later zebrafish and chick embryos and exhibits tissue-specific distribution in adult zebrafish. Our findings show that 5-hmc enrichment of non-committed cells is not a universal feature of vertebrate development and give insights both into evolution of embryonic pluripotency and the potential role of 5-hmc in its regulation

Methodology development for coupled aeroelastic analysis of wing flutter

An integrated computational fluid dynamics and computational structural dynamics (CFD-CSD) approach is being developed for the simulation and prediction of transonic flutter. The CFD solver was based on an unsteady, parallel, multi-block, multi-grid, structured, finite volume algorithm for the Euler/Navier-Stokes equations. The solution of the flowfield is coupled with the structural dynamics by a fully implicit method. The twodimensional (2D) CSD solver is based on the time integration of a two-degree-of-freedom (two-DOF) structural model while the three-dimensional (3D) CSD solver is a finite element model. The coupled CFD-CSD method simulates the elastic system directly in the time domain to determine the stability and response of the aeroelastic system

Bayesian model selection using automatic relevance determination for nonlinear dynamical systems

Bayesian model selection is augmented with automatic relevance determination (ARD) to perform model reduction of complex dynamical systems modelled by nonlinear, stochastic ordinary differential equations (ODE). Given noisy measurement data, a parametrically flexible model is envisioned to represent the dynamical system. A Bayesian model selection problem is posed to find the best model nested under the envisioned model. This model selection problem is transferred from the model space to hyper-parameter space by regularizing the parameter posterior space through a parametrized prior distribution called the ARD prior. The resulting joint prior pdf is the combination of parametrized ARD priors assigned to parameters whose relevance to the system dynamics is questionable and the known prior pdf for parameters whose relevance is known a priori. The hyper-parameter of each ARD prior explicitly represents the relevance of the corresponding model parameter. The hyper-parameters are estimated using the measurement data by performing evidence maximization or type-II maximum likelihood. Superfluous model parameters are switched off during evidence maximization by the corresponding ARD prior, forcing the model parameter to be irrelevant for prediction purposes. An efficient numerical implementation for evidence computation using Markov Chain Monte Carlo sampling of the parameter posterior distribution is presented for the case when the analytical evaluation of evidence is not possible. The ARD approach is validated with synthetic measurements generated from a nonlinear, unsteady aeroelastic oscillator consisting of a NACA0012 airfoil undergoing limit cycle oscillation. A set of intentionally flexible stochastic ODEs having different state-space formulation is proposed to model the synthetic data. ARD is used to obtain an optimal nested model corresponding to each proposed model. The optimal nested model with the maximum posterior model probability is chosen as the overall optimal model. ARD provides a flexible Bayesian platform to find the optimal nested model by eliminating the need to propose candidate nested models and its prior pdfs

Bayesian inference of nonlinear unsteady aerodynamics from aeroelastic limit cycle oscillations

A Bayesian model selection and parameter estimation algorithm is applied to investigate the influence of nonlinear and unsteady aerodynamic loads on the limit cycle oscillation (LCO) of a pitching airfoil in the transitional Reynolds number regime. At small angles of attack, laminar boundary layer trailing edge separation causes negative aerodynamic damping leading to the LCO. The fluid-structure interaction of the rigid, but elastically mounted, airfoil and nonlinear unsteady aerodynamics is represented by two coupled nonlinear stochastic ordinary differential equations containing uncertain parameters and model approximation errors. Several plausible aerodynamic models with increasing complexity are proposed to describe the aeroelastic system leading to LCO. The likelihood in the posterior parameter probability density function (pdf) is available semi-analytically using the extended Kalman filter for the state estimation of the coupled nonlinear structural and unsteady aerodynamic model. The posterior parameter pdf is sampled using a parallel and adaptive Markov Chain Monte Carlo (MCMC) algorithm. The posterior probability of ea