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Pancreatic ductal adenocarcinoma (PDAC) is distinguished by its aggressive malignancy, limited treatment avenues and a tendency towards chemotherapy resistance, underscoring the critical need for advanced research to uncover new therapeutic approaches. Stress granules (SGs) that is implicated in cellular self-protection mechanism, along with its associated family molecules have shown pro-cancer effects and are closely related to tumor chemotherapy resistance. In this study we investigated the relationship between Ras GTPase-activating protein-binding proteins 2 (G3BP2), a core component of SGs, and the malignancy of PDAC as well as its resistance to the chemotherapy drug gemcitabine. Analyzing TCGA dataset revealed that the expression of G3BP1 and G3BP2 was significantly upregulated in PDAC compared with adjacent normal pancreatic tissues, and the high expression of G3BP2 rather than G3BP1 was significantly associated with poorer overall survival (OS) in PDAC patients. We demonstrated that knockdown of G3BP2 inhibited the proliferation and invasion of PANC‐1 and CFPAC-1 cells in vitro and in vivo. By analyzing the differentially expressed genes in G3BP2 knockdown and overexpressed PANC‐1 cells, we identified DKC1 that was associated with RNA stability and regulation as the target of G3BP2. We demonstrated that G3BP2 bound to PDIA3 mRNA and recruited them into SGs, increasing the stability of PDIA3 mRNA and attenuating its translation efficiency, thereby promoting DKC1 expression. Furthermore, DKC1 could bind to hENT mRNA and inhibited its expression, which enhanced gemcitabine resistance of PDAC. Therefore, we propose a novel mechanism wherein G3BP2 facilitates PDAC’s resistance to chemotherapy by modulating PDIA3-DKC1-hENT in a SGs-dependent way, suggesting G3BP2 SGs a protentional therapeutic target for the treatment in PDAC.

This article presents a data-driven approach that adaptively tunes the parameters of a virtual synchronous generator to achieve optimal frequency response against disturbances. In the proposed approach, the control variables, namely, the virtual moment of inertia and damping factor, are transformed into actions of a reinforcement learning agent. Different from the state-of-the-art methods, the proposed study introduces the settling time parameter as one of the observations in addition to the frequency and rate of change of frequency (RoCoF). In the reward function, preset indices are considered to simultaneously ensure bounded frequency deviation, low RoCoF, fast response, and quick settling time. To maximize the reward, this study employs the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm. TD3 has an exceptional capacity for learning optimal policies and is free of overestimation bias, which may lead to suboptimal policies. Finally, numerical validation in MATLAB/Simulink and real-time simulation using RTDS confirm the superiority of the proposed method over other adaptive tuning methods.

A new concept called virtual inertia scheduling (VIS) is proposed to efficiently handle the increasing penetration of inverter-based resources (IBRs) in power systems. VIS is an inertia management framework that targets security-constrained and economy-oriented inertia scheduling and generation dispatch with a large scale of renewable generations. Specifically, it determines the appropriate power setpoint and reserved capacity of synchronous generators and IBRs, as well as the control modes and control parameters of IBRs to provide secure and cost-effective inertia support. First, a uniform system model is employed to quantify the frequency dynamics of IBR-penetrated power systems after disturbances. Leveraging this model, the s-domain and time-domain analytical responses of IBRs with inertia support capability are derived. Then, VIS-based real-time economic dispatch (VIS-RTED) is formulated to minimize generation and reserve costs, with full consideration of dynamic frequency constraints and the derived inertia support reserve constraints. The virtual inertia and damping of IBRs are formulated as decision variables. A deep learning-assisted linearization approach is further employed to address the non-linearity of dynamic constraints. Finally, VIS-RTED is demonstrated on a two-machine system and a modified IEEE 39-bus system. A full-order time-domain simulation is performed to verify the scheduling results.

The increasing penetration of inverter-based resources (IBRs) calls for an advanced active and reactive power (PQ) control strategy in microgrids. To enhance the controllability and flexibility of the IBRs, this paper proposes an adaptive PQ control method with trajectory tracking capability, combining model-based analysis, physics-informed reinforcement learning (RL), and power hardware-in-the-loop (HIL) experiments. First, model-based analysis proves that there exists an adaptive proportional-integral controller with time-varying gains that can ensure any exponential PQ output trajectory of IBRs. These gains consist of a constant factor and an exponentially decaying factor, which are then obtained using a model-free deep RL approach known as the twin delayed deeper deterministic policy gradient. With the model-based derivation, the learning space of the RL agent is narrowed down from a function space to a real space, which reduces the training complexity significantly. Finally, the proposed method is verified through numerical simulation in MATLAB-Simulink and power HIL experiments in the CURENT center. With the physics-informed learning method, exponential response time constants can be freely assigned to IBRs, and they can follow any predefined trajectory without complicated gain tuning.