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Skin aging is a complex process involving intricate genetic and environmental factors. In this study, we performed a comprehensive analysis of the transcriptional regulatory landscape of skin aging in canines. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify aging-related gene modules. We subsequently validated the expression changes of these module genes in single-cell RNA sequencing (scRNA-seq) data of human aging skin. Notably, basal cell (BC), spinous cell (SC), mitotic cell (MC), and fibroblast (FB) were identified as the cell types with the most significant gene expression changes during aging. By integrating GENIE3 and RcisTarget, we constructed gene regulation networks (GRNs) for aging-related modules and identified core transcription factors (TFs) by intersecting significantly enriched TFs within the GRNs with hub TFs from WGCNA analysis, revealing key regulators of skin aging. Furthermore, we demonstrated the conserved role of CTCF and RAD21 in skin aging using an H2O2-stimulated cell aging model in HaCaT cells. Our findings provide new insights into the transcriptional regulatory landscape of skin aging and unveil potential targets for future intervention strategies against age-related skin disorders in both canines and humans.

BackgroundPathological cardiac hypertrophy is commonly resulted from sustained pressure overload and/or metabolic disorder and eventually leads to heart failure, lacking specific drugs in clinic. Here, we aimed to identify promising anti-hypertrophic drug(s) for heart failure and related metabolic disorders by using a luciferase reporter-based high-throughput screening.MethodsA screen of the FDA-approved compounds based on luciferase reporter was performed, with identified luteolin as a promising anti-hypertrophic drug. We systematically examined the therapeutic efficacy of luteolin on cardiac hypertrophy and heart failure in vitro and in vivo models. Transcriptome examination was performed to probe the molecular mechanisms of luteolin.ResultsAmong 2,570 compounds in the library, luteolin emerged as the most robust candidate against cardiomyocyte hypertrophy. Luteolin dose-dependently blocked phenylephrine-induced cardiomyocyte hypertrophy and showed extensive cardioprotective roles in cardiomyocytes as evidenced by transcriptomics. More importantly, gastric administration of luteolin effectively ameliorated pathological cardiac hypertrophy, fibrosis, metabolic disorder, and heart failure in mice. Cross analysis of large-scale transcriptomics and drug-target interacting investigations indicated that peroxisome proliferator activated receptor γ (PPARγ) was the direct target of luteolin in the setting of pathological cardiac hypertrophy and metabolic disorders. Luteolin can directly interact with PPARγ to inhibit its ubiquitination and subsequent proteasomal degradation. Furthermore, PPARγ inhibitor and PPARγ knockdown both prevented the protective effect of luteolin against phenylephrine-induced cardiomyocyte hypertrophy in vitro.ConclusionOur data clearly supported that luteolin is a promising therapeutic compound for pathological cardiac hypertrophy and heart failure by directly targeting ubiquitin-proteasomal degradation of PPARγ and the related metabolic homeostasis.

The safe operation of smart grid was the primary premise to ensure continuous and efficient power supply. Therefore, a graph neural network (GNN) based power system operation state analysis and evaluation model was proposed. Firstly, long short-term memory network was used to fill missing data, to ensure that the model had good performance in stability assessment and fault location. Secondly, a binary classifier for evaluating the stable state of power grid operation and a multi classifier for locating faulty components were designed based on GNN. Due to the ability of the proposed model to fully explore the spatiotemporal characteristics of power grid operation data, the proposed model exhibited superior performance compared to other methods under different measurement conditions. Experimental results showed that when the time series length of data was 0. 1 seconds, the stability assessment and fault location accuracy of the proposed model were 0. 985 5 and 0. 981 4, respectively, and higher than the comparative models. When only half of the component data can be measured, the accuracy of the proposed model for stability assessment, bus fault location, and generator fault location were 0. 998 0, 0. 960 9, and 0. 981 2, respectively, and higher than the comparative models. [ABSTRACT FROM AUTHOR]

This paper proposes a novel L1 neural network (NN) adaptive control strategy to address the challenges posed by the non-affine nonlinear characteristics of wind turbines operating at high wind speed. These challenges complicate the design of control inputs and the issues of ensuring system transient performance due to unmodeled dynamics and external disturbances in variable-pitch control. This strategy comprises an estimator, control laws, and adaptive laws. The L1 adaptive controller ensures rapid adaptation and robustness of the system, and the NN is utilized to approximate the ideal implicit controller. This approach guarantees the uniform boundedness of system transient responses without the need for wind speed measurements. Finally, the new strategy proposed in this work is compared with traditional NN adaptive and PID control methods through simulation experiments, demonstrating enhanced robustness and superior transient performance. [ABSTRACT FROM AUTHOR]

The feasibility and safety of laparoscopic major hepatectomy (LMH) are still uncertain. The purpose of the present study is to compare the short- and long-term outcomes of LMH with those of open major hepatectomy (OMH) for hepatocellular carcinoma (HCC).