Ultrasound-targeted microbubble destruction enhances AAV mediated gene transfection: human RPE cells in vitro and the rat retina in vivo

The present study was performed to investigate the efficacy and safety of Ultrasound-targeted microbubble destruction (UTMD) mediated rAAV2-EGFP to cultured human retinal pigment epithelium (RPE) cells _in vitro_ and the rat retina _in vivo_. _In vitro_ study, cultured human RPE cells were exposed to US under different conditions with or without microbubbles. Furthermore, the effect of UTMD to rAAV2-EGFP itself and the cells were evaluated. _In vivo_ study, gene transfer was examined by injecting rAAV2-EGFP into the subretinal space of the rats with or without microbubbles and then exposed to US. We investigated EGFP expression _in vivo_ via stereomicroscopy and performed quantitative analysis by Axiovision 3.1 software. HE staining and frozen sections were used to observe tissue damage and location of EGFP gene expression. _In vitro_ study, the transfection efficiency of rAAV2-EGFP increased 74.85% under the optimal UTMD conditions. Furthermore, there was almost no cytotoxicity to the cells and rAAV2-EGFP itself. _In vivo_ study, UTMD could be used safely to enhance and accelerate transgene expression of the retina. Fluorescence expression was mainly located in the layer of retina. UTMD is a promising method for gene delivery to the retina

Attention Loss Adjusted Prioritized Experience Replay

Prioritized Experience Replay (PER) is a technical means of deep reinforcement learning by selecting experience samples with more knowledge quantity to improve the training rate of neural network. However, the non-uniform sampling used in PER inevitably shifts the state-action space distribution and brings the estimation error of Q-value function. In this paper, an Attention Loss Adjusted Prioritized (ALAP) Experience Replay algorithm is proposed, which integrates the improved Self-Attention network with Double-Sampling mechanism to fit the hyperparameter that can regulate the importance sampling weights to eliminate the estimation error caused by PER. In order to verify the effectiveness and generality of the algorithm, the ALAP is tested with value-function based, policy-gradient based and multi-agent reinforcement learning algorithms in OPENAI gym, and comparison studies verify the advantage and efficiency of the proposed training framework

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE) condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme

Collaborative mechanism on profit allotment and public health for a sustainable supply chain

This paper explores the collaborative mechanism that motivates supply chain firms to collectively invest in environmental technology and produce environmental friendly products (EFPs) to reduce pollutant emissions and negative impacts on environment and public health. Our paper investigates how such firms can achieve the balance between economic feasibility and environmental and social sustainability under multiple sustainable constraints in terms of the triple bottom line dimensions. The work also describes the impacts of interrelated multiple sustainable constraints on optimal policy for the supply chain transfer price and profit allotment decisions. Our findings suggest that government intervention plays a dominant role in governing the supply chain firms’ behaviors in the context of environmental and public health sustainability. The profit allotment is determined through the process of negotiation of the transfer price interrelated with the government subsidy sharing between the supply chain firms