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As cross‐shelf gradients of most properties are typically much steeper than those in the alongshore direction, transport across isobaths tends to be inhibited, particularly at oceanic fronts where cross‐shelf gradients are markedly pronounced. Consequently, variations in cross‐shelf gradients may exert a significant influence on offshore transport; however, this influence is not yet well understood. This study employs reconstructed daily suspended sediment concentration (SSC) data from the Yellow Sea's offshore region to investigate the dynamics of offshore transport. Our analysis on an interannual scale shows that offshore SSC correlates more with temperature gradients at the oceanic front than with winter storms, despite the latter's vital role in causing frontal instability. The observed increase in offshore transport over the past two decades is likely connected to Kuroshio Current warming, which has strengthened the horizontal density gradient at the oceanic front, driving the strengthened offshore transport of coastal sediments during instability episodes. [ABSTRACT FROM AUTHOR]

Summary: Chronic inflammation of adipose tissue is a prominent characteristic of many metabolic diseases. Lipid metabolism in adipose tissue is consistently dysregulated during inflammation, which is characterized by substantial infiltration by proinflammatory cells and high cytokine concentrations. Adipose tissue inflammation is caused by a variety of endogenous factors, such as mitochondrial dysfunction, reactive oxygen species (ROS) production, endoplasmic reticulum (ER) stress, cellular senescence, ceramides biosynthesis and mediators of lipopolysaccharides (LPS) signaling. Additionally, the gut microbiota also plays a crucial role in regulating adipose tissue inflammation. Essentially, adipose tissue inflammation arises from an imbalance in adipocyte metabolism and the regulation of immune cells. Specific inflammatory signals, including nuclear factor‐κB (NF‐κB) signaling, inflammasome signaling and inflammation‐mediated autophagy, have been shown to be involved in the metabolic regulation. The pathogenesis of metabolic diseases characterized by chronic inflammation (obesity, insulin resistance, atherosclerosis and nonalcoholic fatty liver disease [NAFLD]) and recent research regarding potential therapeutic targets for these conditions are also discussed in this review. [ABSTRACT FROM AUTHOR]

Chronic nonhealing wounds significantly reduce patients' quality of life and are a major burden on healthcare systems. Over the past few decades, tissue engineering materials have emerged as a viable option for wound healing, with cell-derived extracellular matrix (CDM) showing remarkable results. The CDM's compatibility and resemblance to the natural tissue microenvironment confer distinct advantages to tissue-engineered scaffolds in wound repair. This review summarizes the current processes for CDM preparation, various cell decellularization protocols, and common characterization methods. Furthermore, it discusses the applications of CDM in wound healing, including skin defect and wound repair, angiogenesis, and engineered vessels, and offers perspectives on future developments. [ABSTRACT FROM AUTHOR]

Sparse General Matrix-Matrix Multiplication (SpGEMM) has played an important role in a number of applications. So far, many efficient algorithms have been proposed to improve the performance of SpGEMM on GPUs. However, the performance of each algorithm for matrices of different structures varies a lot. There is no algorithm that can achieve the optimal performance of SpGEMM computation on all matrices. In this article, we design a machine learning based approach for predicting the optimal SpGEMM algorithm on input matrices. By extracting features from input matrices, we utilize LightGBM and XGBoost to train different lightweight models. The models are capable of predicting the best performing algorithm with low inference overhead and high accuracy for the given input matrices. We also investigate the impact of tree depth on model accuracy and inference overhead. Our evaluation shows that an increase in tree depth leads to a corresponding increase in prediction accuracy, reaching a maximum of approximately 85%, while resulting in increased inference overhead of approximately 10 µs. Compared with the state-of-the-art algorithms on three GPU platforms, our method achieves better overall performance. [ABSTRACT FROM AUTHOR]