Myocardin-related transcription factor A regulates conversion of progenitors to beige adipocytes

Thermogenic brown adipose tissue generates heat via mitochondrial uncoupling protein-1 (UCP-1), increases whole-body energy expenditure and may protects against obesity and metabolic disorders. White adipocytes store excess energy in the form of triglycerides. UCP-1 positive adipocytes develop within white adipose tissue (beige or brite adipocytes) in response to cold exposure or β3 adrenergic agonists. It was known that beige adipocytes arise from a distinct lineage compared with brown adipocytes, but the developmental origin of the beige adipocytes is still unclear. Signaling pathways that control beige adipocyte determination and formation are essentially unknown. Here, we identified a novel signaling pathway that regulates the lineage specification of beige adipocytes. Bone morphogenetic protein 7 (BMP7), a known brown adipogenesis inducer, suppresses Rho-GTPase kinase (ROCK) and depolymerizes F-actin (filamentous actin) into G-actin (globular actin) in mesenchymal stem cells. G-actin regulates myocardin-related transcription factor A (MRTFA) that co-transactivates serum response factor (SRF) and promotes smooth muscle cell differentiation in various organs. Subcutaneous white adipose tissue from MRTFA-/- mice had enhanced accumulation of UCP-1+ adipocytes and elevated levels of brown-selective proteins. Compared with wild type (WT) controls, MRTFA-/- mice exhibited improved metabolic profiles and were protected from diet-induced obesity and insulin resistance, suggesting that the beige adipocytes are physiologically functional. Compared to WT mice, stromal vascular cells from MRTFA-/- mice expressed higher levels of distinct beige progenitor markers and reduced levels of smooth muscle markers. Our studies demonstrate a novel ROCK-actin-MRTFA/SRF pathway that contributes to the development of beige adipocytes

Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms

Marginal-based methods achieve promising performance in the synthetic data competition hosted by the National Institute of Standards and Technology (NIST). To deal with high-dimensional data, the distribution of synthetic data is represented by a probabilistic graphical model (e.g., a Bayesian network), while the raw data distribution is approximated by a collection of low-dimensional marginals. Differential privacy (DP) is guaranteed by introducing random noise to each low-dimensional marginal distribution. Despite its promising performance in practice, the statistical properties of marginal-based methods are rarely studied in the literature. In this paper, we study DP data synthesis algorithms based on Bayesian networks (BN) from a statistical perspective. We establish a rigorous accuracy guarantee for BN-based algorithms, where the errors are measured by the total variation (TV) distance or the $L^2$ distance. Related to downstream machine learning tasks, an upper bound for the utility error of the DP synthetic data is also derived. To complete the picture, we establish a lower bound for TV accuracy that holds for every $\epsilon$-DP synthetic data generator

Multi-period Optimal Control for Mobile Agents Considering State Unpredictability

The optimal control for mobile agents is an important and challenging issue. Recent work shows that using randomized mechanism in agents' control can make the state unpredictable, and thus improve the security of agents. However, the unpredictable design is only considered in single period, which can lead to intolerable control performance in long time horizon. This paper aims at the trade-off between the control performance and state unpredictability of mobile agents in long time horizon. Utilizing random perturbations consistent with uniform distributions to maximize the attackers' prediction errors of future states, we formulate the problem as a multi-period convex stochastic optimization problem and solve it through dynamic programming. Specifically, we design the optimal control strategy considering both unconstrained and input constrained systems. The analytical iterative expressions of the control are further provided. Simulation illustrates that the algorithm increases the prediction errors under Kalman filter while achieving the control performance requirements successfully

Optimal Asset Allocation in a High Inflation Regime: a Leverage-feasible Neural Network Approach

We study the optimal multi-period asset allocation problem with leverage constraints in a persistent, high-inflation environment. Based on filtered high-inflation regimes, we discover that a portfolio containing an equal-weighted stock index partially stochastically dominates a portfolio containing a capitalization-weighted stock index. Assuming the asset prices follow the jump diffusion model during high inflation periods, we establish a closed-form solution for the optimal strategy that outperforms a passive strategy under the cumulative quadratic tracking difference (CD) objective. The closed-form solution provides insights but requires unrealistic constraints. To obtain strategies under more practical considerations, we consider a constrained optimal control problem with bounded leverage. To solve this optimal control problem, we propose a novel leverage-feasible neural network (LFNN) model that approximates the optimal control directly. The LFNN model avoids high-dimensional evaluation of the conditional expectation (common in dynamic programming (DP) approaches). We establish mathematically that the LFNN approximation can yield a solution that is arbitrarily close to the solution of the original optimal control problem with bounded leverage. Numerical experiments show that the LFNN model achieves comparable performance to the closed-form solution on simulated data. We apply the LFNN approach to a four-asset investment scenario with bootstrap resampled asset returns. The LFNN strategy consistently outperforms the passive benchmark strategy by about 200 bps (median annualized return), with a greater than 90% probability of outperforming the benchmark at the terminal date. These results suggest that during persistent inflation regimes, investors should favor short-term bonds over long-term bonds, and the equal-weighted stock index over the cap-weighted stock index

The predatory bug Orius strigicollis shows a preference for egg-laying sites based on plant topography

Background. Oviposition site selection is an important factor in determining the success of insect populations. Orius spp. are widely used in the biological control of a wide range of soft-bodied insect pests such as thrips, aphids, and mites. Orius strigicollis (Heteroptera: Anthocoridae) is the dominant Oriusspecies in southern China; however, what factor drives its selection of an oviposition site after mating currently remains unknown. Methods. Here, kidney bean pods (KBPs) were chosen as the oviposition substrate, and choice and nonchoice experiments were conducted to determine the preferences concerning oviposition sites on the KBPs of O. strigicollis. The mechanism of oviposition behavior was revealed through observation and measurement of oviposition action, the egg hatching rate, and the oviposition time. Results. We found that O. strigicollis preferred the seams of the pods for oviposition, especially the seams at the tips of the KBPs. Choice and nonchoice experiments showed that females did not lay eggs when the KBP tail parts were unavailable. The rates of egg hatching on different KBP parts were not significantly different, but the time required for females to lay eggs on the tip seam was significantly lower. Decreased oviposition time is achieved on the tip seam because the insect can exploit support points found there and gain leverage for insertion of the ovipositor. Discussion. The preferences for oviposition sites of O. strigicollis are significantly influenced by the topography of the KBP surface. Revealing such behavior and mechanisms will provide an important scientific basis for the mass rearing of predatory bugs

Optimal Cooperative Line-of-Sight Guidance for Defending a Guided Missile

This correspondence proposes an optimal cooperative guidance law for protecting a target from a guided missile. The linearized three-body kinematics using the line-of-sight (LOS) triangle concept is formulated, and a new concept called error distance is introduced. A generalized linear quadratic optimization problem is formulated in minimizing weighted energy consumption while regulating the error distance. The analytic guidance command is derived by solving the optimization problem formulated. The main feature of the proposed guidance law lies in that it helps reduce the maneuver capability demand of the defender. Extensive numerical simulations are carried out to demonstrate the effectiveness of the proposed solution

Optimal Cooperative Line-of-Sight Guidance for Defending a Guided Missile

This correspondence proposes an optimal cooperative guidance law for protecting a target from a guided missile. The linearized three-body kinematics using the line-of-sight (LOS) triangle concept is formulated, and a new concept called error distance is introduced. A generalized linear quadratic optimization problem is formulated in minimizing weighted energy consumption while regulating the error distance. The analytic guidance command is derived by solving the optimization problem formulated. The main feature of the proposed guidance law lies in that it helps reduce the maneuver capability demand of the defender. Extensive numerical simulations are carried out to demonstrate the effectiveness of the proposed solution

A High-Performance Adaptive Incremental Conductance MPPT Algorithm for Photovoltaic Systems

The output characteristics of photovoltaic (PV) arrays vary with the change of environment, and maximum power point (MPP) tracking (MPPT) techniques are thus employed to extract the peak power from PV arrays. Based on the analysis of existing MPPT methods, a novel incremental conductance (INC) MPPT algorithm is proposed with an adaptive variable step size. The proposed algorithm automatically regulates the step size to track the MPP through a step size adjustment coefficient, and a user predefined constant is unnecessary for the convergence of the MPPT method, thus simplifying the design of the PV system. A tuning method of initial step sizes is also presented, which is derived from the approximate linear relationship between the open-circuit voltage and MPP voltage. Compared with the conventional INC method, the proposed method can achieve faster dynamic response and better steady state performance simultaneously under the conditions of extreme irradiance changes. A Matlab/Simulink model and a 5 kW PV system prototype controlled by a digital signal controller (TMS320F28035) were established. Simulations and experimental results further validate the effectiveness of the proposed method

Distribution network monitoring and management system based on intelligent recognition and judgement

Based on the shortcomings of the current intelligent management of distribution networks, the article designs and implements a remote intelligent detection system for live distribution networks. The article constructs and realises an algorithm based on a genetic algorithm (GA) and agent system. The algorithm is applied to energy-saving intelligent supervision of equipment in the distribution network. The simulation experiment shows that the integrated algorithm based on GA and agent system can accurately detect power quality in real time. At the same time, the algorithm can monitor the energy consumption of equipment in the distribution network under multiple disturbances

Descemet Stripping Automated Endothelial Keratoplasty in Thick Corneas

Purpose: To evaluate the outcomes of Descemet’s Stripping Automated Endothelial Keratoplasty (DSAEK) in corneas > 820 microns in thickness. Methods: This retrospective study included 30 eyes of 30 patients who underwent DSAEK. Endothelial cell destiny (ECD) and corneal thickness were recorded before surgery and at 1 and 12 months postoperatively. Patients were divided into two groups (≤ 820 microns and > 820 microns) based on median preoperative corneal thickness. Linear regression analyses were used to investigate the correlations between ECD and preoperative corneal thickness. Results: Recipient corneal thickness (RCT) and postoperative central cornea thickness had a statistically significant difference 1 month after surgery (p = 0.03, p = 0.08, respectively). BCVA and ECD did not have a statistical difference in the two groups at 1 month and 12 months after DSAEK. Conclusions: BCVA, ECD and corneal thickness were similar at 12 months after DSAEK in thick corneas. DSAEK is a viable surgical option in thick corneas