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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]

The research on tropical cyclone (TC) relieson numerical models and simulations, with the currently widely used boundary layer parameterization posing a significant challenge on accurately predicting turbulent mixing. However, machine learning has opened up new possibilities for boundary layer sub‐grid process parameterization. In this study, a deep‐learning parameterization scheme for the TC boundary layer (DeepBL) on predicting turbulent flux is proposed. DeepBL comprises a one‐dimensional convolutional structure that relies on a small number of learnable parameters that accomplishes an error reduction compared to the standard fully connected neural networks. Furthermore, a nonlinear transformation scheme is introduced to alleviate the training data's skewness and improve the DeepBL performance by affording a smaller prediction error. Specifically, the output data of a large‐eddy simulation of an idealized TC are used to train, validate, and test DeepBL, affording significantly better performance than the YSU scheme in the Weather Research and Forecast model. Interpretability analysis on DeepBL demonstrates that the deep‐learning parameterization scheme is physically reasonable. Plain Language Summary: Traditional boundary layer parameterizations explicitly program algorithms to reproduce the effects of turbulent processes in numerical models. They suffer from evident biases when simulating the tropical cyclone boundary layer. Therefore, a deep learning model with a concise structure is introduced to improve the boundary‐layer representation. The developed model is trained on the output of a large‐eddy simulation of a tropical cyclone, where the turbulent processes are explicitly simulated. Although the unbalanced training data distribution will hinder the deep learning model's performance, the nonlinear transformation proposed in this study assists the training data distribution to be more compact and reduce the prediction errors of the trained model. The results demonstrate that the proposed deep‐learning model is significantly improved compared with traditional methods in reproducing the spatial features and data distribution of turbulent fluxes. Key Points: A neural network (NN) with a one‐dimensional convolutional structure is designed to parameterize the tropical cyclone boundary layerThe proposed NN can reproduce the spatial features and data distribution of turbulent fluxes better than the traditional schemeA nonlinear transformation applied to the training data of the NN can markedly reduce errors for turbulent flux prediction [ABSTRACT FROM AUTHOR]

This paper studies the impact of stochastic load variations on distributed optimal load tracking and allocation (OLTA) problems in cyber-physical DC microgrids (MGs) for transportation electrification. Without load variations, the distributed optimization strategies developed in our earlier work can achieve convergence to global optimal solutions in a multi-objective optimization that balances fair load allocation and power loss reduction. Under persistent stochastic load variations, this paper develops distributed optimal strategies to track time-varying loads under noisy observations and establishes their convergence properties and error bounds. The limiting behavior of the errors characterizes the fundamental impact of the step size on irreducible errors due to conflict between attenuating observation noises and tracking load changes. Optimality conditions and algorithms for selecting the optimal step size are introduced to guide step size selection in practical applications. Simulation studies on real-world systems demonstrate the effectiveness of the proposed algorithms and validate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is concerned with persistent identification of systems that involve deterministic unmodeled dynamics and stochastic observation disturbances, and whose unknown parameters switch values (possibly large jumps) that can be represented by a Markov chain. Two classes of problems are considered. In the first class, the switching parameters are stochastic processes modeled by irreducible and aperiodic Markov chains with transition rates sufficiently faster than adaptation rates of the identification algorithms. In this case, tracking real-time parameters by output observations becomes impossible and we show that an averaged behavior of the parameter process can be derived from the stationary measure of the Markov chain and can be estimated with periodic inputs and least-squares type algorithms. Upper and lower error bounds are established that explicitly show impact of unmodeled dynamics. In contrast, the second class of problems represents systems whose state transitions occur infrequently. An adaptive algorithm with variable step sizes is introduced for tracking the time-varying parameters. Convergence and error bounds are derived. Numerical results are presented to illustrate the performance of the algorithm. Index Terms--Error bounds, exogenous noise, parameter switching, parameter tracking, persistent identification, unmodeled dynamics.

Input design is of essential importance in system identification for providing sufficient probing capabilities to guarantee convergence of parameter estimates to their true values. This paper presents conditions on input signals that characterize their probing richness for strongly consistent parameter estimation of linear systems with binary-valued output observations. Necessary and sufficient conditions on periodic signals are derived for sufficient richness. These conditions are further studied under different system configurations including open-loop and feedback systems, and different scenarios of noises including actuator noise, input measurement noise, and output measurement noise. In addition to system parameter estimation, essential properties of identifiability and input conditions are also derived when sensor thresholds or noise distribution functions are unknown. The findings of this paper provide a foundation to study identification of systems that either use binary-valued or quantized sensors or involve communication channels, which mandate quantization of signals. Index Terms--Binary-valued observation, distribution function, identification, input design, parameter estimation, sensor threshold, sufficient excitation.