To effectively guide power manufacturers in the medium and long-term market and green certificate market trading behavior. This paper firstly constructs the model of power manufacturers' participation in the green certificate market and the model of participating in the medium and long-term market of power, and establishes the coupling constraint of power manufacturers' participation in the power market and the green certificate market. Secondly, considering the speculative behavior of power manufacturers and using conditional value-at-risk to depict the profit risk of electricity generation due to the uncertainty of market price, the optimal comprehensive benefit considering the annual expected profit and risk of power manufacturers is taken as the decision-making objective, and the joint decision-making model of power manufacturers participating in the green certificate market and the medium and long term power market is established. It is helpful for power manufacturers to make trading decisions in medium and longterm market and green certificate market.
Hypertrophic scar (HS) is one of the most common sequelae of patients, especially after burns and trauma. The roles of regulatory long noncoding RNAs (lncRNAs) in mediating HS remain underexplored. Human hypertrophic scar-derived fibroblasts (HSFBs) have been shown to exert more potent promoting effects on extracellular matrix (ECM) accumulation than normal skin-derived fibroblasts (NSFBs) and are associated with enhanced HS formation. The purpose of this study is to search for lncRNAs enriched in HSFBs and investigate their roles and mechanisms. LncRNA MSTRG.59347.16 is one of the most highly expressed lncRNAs in HS detected by lncRNA-seq and qRT-PCR and named as hypertrophic scar fibroblast-associated lncRNA (HSFAS). HSFAS overexpression significantly induces fibroblast proliferation, migration, and myofibroblast trans-differentiation and inhibits apoptosis in HSFBs, while knockdown of
Innovative methods for engineering cancer cell membranes promise to manipulate cell–cell interactions and boost cell-based cancer therapeutics. Here, we illustrate an in situ approach to selectively modify cancer cell membranes by employing an enzyme-instructed peptide self-assembly (EISA) strategy. Using three phosphopeptides (pY1, pY2, and pY3) targeting the membrane-bound epidermal growth factor receptor (EGFR) and differing in just one phosphorylated tyrosine, we reveal that site-specific phosphorylation patterns in pY1, pY2, and pY3 can distinctly command their preorganization levels, self-assembling kinetics, and spatial distributions of the resultant peptide assemblies in cellulo. Overall, pY1 is the most capable of producing preorganized assemblies and shows the fastest dephosphorylation reaction in the presence of alkaline phosphatase (ALP), as well as the highest binding affinity for EGFR after dephosphorylation. Consequently, pY1 exhibits the greatest capacity to construct stable peptide assemblies on cancer cell membranes with the assistance of both ALP and EGFR. We further use peptide–protein and peptide–peptide co-assembly strategies to apply two types of antigens, namely ovalbumin (OVA) protein and dinitrophenyl (DNP) hapten respectively, on cancer cell membranes. This study demonstrates a very useful technique for the in situ construction of membrane-bound peptide assemblies around cancer cells and implies a versatile strategy to artificially enrich cancer cell membrane components for potential cancer immunotherapy.
Diffusion models have garnered widespread attention in Reinforcement Learning (RL) for their powerful expressiveness and multimodality. It has been verified that utilizing diffusion policies can significantly improve the performance of RL algorithms in continuous control tasks by overcoming the limitations of unimodal policies, such as Gaussian policies, and providing the agent with enhanced exploration capabilities. However, existing works mainly focus on the application of diffusion policies in offline RL, while their incorporation into online RL is less investigated. The training objective of the diffusion model, known as the variational lower bound, cannot be optimized directly in online RL due to the unavailability of 'good' actions. This leads to difficulties in conducting diffusion policy improvement. To overcome this, we propose a novel model-free diffusion-based online RL algorithm, Q-weighted Variational Policy Optimization (QVPO). Specifically, we introduce the Q-weighted variational loss, which can be proved to be a tight lower bound of the policy objective in online RL under certain conditions. To fulfill these conditions, the Q-weight transformation functions are introduced for general scenarios. Additionally, to further enhance the exploration capability of the diffusion policy, we design a special entropy regularization term. We also develop an efficient behavior policy to enhance sample efficiency by reducing the variance of the diffusion policy during online interactions. Consequently, the QVPO algorithm leverages the exploration capabilities and multimodality of diffusion policies, preventing the RL agent from converging to a sub-optimal policy. To verify the effectiveness of QVPO, we conduct comprehensive experiments on MuJoCo benchmarks. The final results demonstrate that QVPO achieves state-of-the-art performance on both cumulative reward and sample efficiency.
Doxorubicin (DOX) is a commonly used chemotherapeutic drug that may induce a dose limiting cardiomyopathy. Cytotoxic effects of DOX are related to its ability to undergo redox cycling, resulting in superoxide anion generation and mitochondrial injury. The apoA‐I mimetic peptide 4F exerts anti‐oxidant effects by improving functional properties of HDL and by upregulating the enzyme superoxide dismutase (SOD). In this study, we tested the hypothesis that 4F treatment attenuates cardiac dysfunction in DOX‐treated rats. Male Sprauge‐Dawley rats were pre‐treated with 4F (5mg/kg/day) or saline by ip injection for 5 days. LV function was measured on day 5 in 4F‐ and saline‐treated rats by echocardiography. DOX (20mg/kg) was then administered by ip injection and echo measurements repeated on day 9. A separate CONTROL group received ip saline over the same time period. Stroke volume (SV) and ejection fraction (EF) were reduced by 25 ± 2% and 44 ± 7% respectively in DOX+saline rats (n=8) on day 9 compared to CONTROL rats (n=10). In contrast, the reduction in SV (8 ± 3%) and EF (7 ± 4%) was significantly attenuated in DOX+4F rats (n=8). It is proposed that a cardioprotective mechanism of 4F is likely due to the induction of SOD resulting in increased scavenging of DOX‐generated superoxide anion. In ongoing experiments, cardiac levels of SOD protein and mRNA expression are being assessed in DOX rats treated with saline or 4F.
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