Search

The proliferation and myogenic differentiation of muscle stem cells (MuSCs) are important factors affecting muscle development and beef quality. There is increasing evidence that circRNAs can regulate myogenesis. We found a novel circRNA, named circRRAS2 that is significantly upregulated in the differentiation phase of bovine MuSCs. Here, we aimed to determine its roles in the proliferation and myogenic differentiation of these cells. The results showed that circRRAS2 was expressed in several bovine tissues. CircRRAS2 inhibited MuSCs proliferation and promoted myoblast differentiation. In addition, chromatin isolation by using RNA purification and mass spectrometry in differentiated muscle cells identified 52 RNA-binding proteins that could potentially bind to circRRAS2, in order to regulate their differentiation. The results suggest that circRRAS2 could be a specific regulator of myogenesis in bovine muscle. CircRRAS2 expression is higher in DM cells than in GM cells. CircRRAS2 could significantly inhibit the proliferation and apoptosis of bovine MuSCs. CircRRAS2 promotes the differentiation of bovine MuSCs into myotubes. CircRRAS2 may exert regulatory effects through multiple RNA binding proteins. [ABSTRACT FROM AUTHOR]

This paper establishes ergodic properties for Markovian semigroups generated by segment processes associated with several classes of retarded stochastic differential equations (SDEs) with constant/variable/distributed time lags. It derives exponential ergodicity for (a) retarded SDEs by the Arzelà–Ascoli tightness characterization of the spaceequipped with the uniform topology, (b) neutral SDEs with continuous sample paths by a generalized Razumikhin-type argument and a stability-in-distribution approach, and (c) retarded SDEs driven by jump processes using the Kurtz criterion of tightness for the spaceendowed with the Skorohod topology. [ABSTRACT FROM AUTHOR]

This paper investigates noise attenuation problems for systems with unmodelled dynamics and unknown noise characteristics. A unique methodology is introduced that employs signal estimation in one phase, followed by control design for noise rejection. The methodology enjoys certain advantages in its simple control design process, accommodation of unmodelled dynamics, and non-conservative noise rejection performance. Under mild information on unmodelled dynamics, we first derive robust performance bounds on noise attenuation with respect to unmodelled dynamics without noise estimation errors. Then more general results are presented for systems that are subject to both stochastic signal estimation errors and unmodelled dynamics. Examples are also presented to demonstrate our findings. [ABSTRACT FROM AUTHOR]

This paper studies identification of linear systems with binary-valued observations generated via fixed thresholds. In addition to stochastic measurement noises, the systems are also subject to structural uncertainties, including deterministic unmodelled dynamics, nonlinear model mismatch, and sensor observation bias. Since binary-valued observations can supply only limited information on the signals, truncated empirical measures are introduced to extract further information for system identification. An effective identification algorithm is constructed based on the proposed empirical measures. Optimal identification errors, time complexity, optimal input design, and impact of disturbances, unmodelled dynamics, observation bias, and nonlinear model mismatch are thoroughly investigated in a stochastic information framework. Asymptotic upper and lower bounds are established on identification errors. Numerical experiments are presented to demonstrate the effectiveness of the algorithms and the main results. [ABSTRACT FROM PUBLISHER]

Although the mean–variance control was initially formulated for financial portfolio management problems in which one wants to maximize the expected return and control the risk, our motivations stem from highway vehicle platoon controls that aim to maximize highway utility while ensuring zero accident. This paper develops near-optimal mean–variance controls of switching diffusion systems. To reduce the computational complexity, with motivations from earlier work on singularly perturbed Markovian systems [Sethi and Zhang,Hierarchical Decision Making in Stochastic Manufacturing Systems, Birkhäuser, Boston, MA, 1994; Yin and Zhang,Continuous-Time Markov Chains and Applications: A Singular Pertubation Approach, Springer-Verlag, New York, 1998 and Yin et al.,Ann. Appl. Probab.10 (2000), pp. 549–572], we use a two-time-scale formulation to treat the underlying system, which is represented by the use of a small parameter. As the small parameter goes to 0, we obtain a limit problem. Using the limit problem as a guide, we construct controls for the original problem, and show that the control so constructed is nearly optimal. [ABSTRACT FROM PUBLISHER]