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Alcohol-associated liver disease(ALD), caused by excessive alcohol consumption, are often associated with inflammatory outbreaks and lipid deposition in the liver. The role of Insulin-like growth factor-binding protein 7 (IGFBP7), an important metabolic regulator, in ALD, its underlying regulatory mechanism, and its potential implication in anti-ALD therapies remain unknown. We investigated the effects of IGFBP7 on hepatic inflammation and lipid metabolism disruption in a mouse model of ALD. Mice were fed by chronic ethanol feeding plus a single binge of ethanol feeding(chronic-plus-single-binge model). In addition, ethanol exposure modeling studies were performed on cultured hepatocytes to verify molecular correlations. The results showed that IGFBP7 expression was significantly elevated in the livers of mice and hepatocytes after chronic ethanol exposure. Subsequently, the results of a study by specific knockout of IGFBP7(IGFBP7-cKO) in mouse hepatocytes and lentiviral silencing of IGFBP7 in vivo suggested that IGFBP7 deletion could improve liver function levels in alcohol-fed mice; It also attenuated the outbreak of hepatitis factor and the disorder of lipid metabolism in mice.Using RNA-seq sequencing of mouse liver tissue, we found that IGFBP7 affects several downstream metabolic signaling pathways, including PPAR, MAPK, FoxO, etc. Then, we used the PPARα plasmid in hepatocytes and discovered that overexpressing PPARα reversed the impact of IGFBP7 on lipid metabolism disorders in hepatocytes. In conclusion, IGFBP7 deficiency in alcohol-associated liver disease alleviates the decline in liver function and the imbalance of lipid metabolism in mice, attenuates the inflammatory outbreak, and affects a variety of downstream lipid metabolism factors by regulating PPARα. Hence, IGFBP7 may be an effective therapeutic target in the treatment of ALD.

Alcohol-associated liver disease(ALD), caused by excessive alcohol consumption, are often associated with inflammatory outbreaks and lipid deposition in the liver. The role of Insulin-like growth factor-binding protein 7 (IGFBP7), an important metabolic regulator, in ALD, its underlying regulatory mechanism, and its potential implication in anti-ALD therapies remain unknown. We investigated the effects of IGFBP7 on hepatic inflammation and lipid metabolism disruption in a mouse model of ALD. Mice were fed by chronic ethanol feeding plus a single binge of ethanol feeding(chronic-plus-single-binge model). In addition, ethanol exposure modeling studies were performed on cultured hepatocytes to verify molecular correlations. The results showed that IGFBP7 expression was significantly elevated in the livers of mice and hepatocytes after chronic ethanol exposure. Subsequently, the results of a study by specific knockout of IGFBP7(IGFBP7-cKO) in mouse hepatocytes and lentiviral silencing of IGFBP7 in vivo suggested that IGFBP7 deletion could improve liver function levels in alcohol-fed mice; It also attenuated the outbreak of hepatitis factor and the disorder of lipid metabolism in mice.Using RNA-seq sequencing of mouse liver tissue, we found that IGFBP7 affects several downstream metabolic signaling pathways, including PPAR, MAPK, FoxO, etc. Then, we used the PPARα plasmid in hepatocytes and discovered that overexpressing PPARα reversed the impact of IGFBP7 on lipid metabolism disorders in hepatocytes. In conclusion, IGFBP7 deficiency in alcohol-associated liver disease alleviates the decline in liver function and the imbalance of lipid metabolism in mice, attenuates the inflammatory outbreak, and affects a variety of downstream lipid metabolism factors by regulating PPARα. Hence, IGFBP7 may be an effective therapeutic target in the treatment of ALD.

Abstract This paper investigates the spillovers of extreme risks between crude oil and stock markets using daily data of the S&P 500 stock index and West Texas Intermediate (WTI) crude oil futures returns. Based on the method of Granger causality in risk, Value at Risk (VaR) is employed to measure market risk, and a class of kernel-based tests is used to detect negative and positive risk spillover effects. Empirical results reveal that there are significant risk spillovers between the two markets. Extreme movements, past or current, in one market may have a significant predictive power for those in the other market. Prior to the recent financial crisis, there are positive risk spillovers from stock market to crude oil market, and negative spillovers from crude oil market to stock market. After the financial crisis, bidirectional positive risk spillovers are strengthened markedly. The risk spillovers may occur instantaneously, and/or with a (long) time delay. Both positive and negative risk spillover effects exhibit asymmetric correlations.

Abstract This paper investigates the spillovers of extreme risks between crude oil and stock markets using daily data of the S&P 500 stock index and West Texas Intermediate (WTI) crude oil futures returns. Based on the method of Granger causality in risk, Value at Risk (VaR) is employed to measure market risk, and a class of kernel-based tests is used to detect negative and positive risk spillover effects. Empirical results reveal that there are significant risk spillovers between the two markets. Extreme movements, past or current, in one market may have a significant predictive power for those in the other market. Prior to the recent financial crisis, there are positive risk spillovers from stock market to crude oil market, and negative spillovers from crude oil market to stock market. After the financial crisis, bidirectional positive risk spillovers are strengthened markedly. The risk spillovers may occur instantaneously, and/or with a (long) time delay. Both positive and negative risk spillover effects exhibit asymmetric correlations.

Drought-tolerant species play a crucial role in maintaining ecosystem services in arid and semi-arid regions wherein subject to rapid climate change. However, how future climate change affect the distribution of drought-tolerant plants with different growth forms (e.g., herb and woody) remains largely unknown. Here, we used the MaxEnt model to simulate the potential species distribution under current conditions, and predicted the future species distribution of 82 common drought-tolerant plants in China under two time periods (2041-2060 and 2081-2100) and three climate change scenarios (SSP126, SSP245 and SSP585) in the future. We found that the western and northern regions of China are hotspots for drought-tolerant plant distribution. Compared with other predictors, aridity index (AI) explained the largest portion of variation (45%) in the distribution patterns of drought-tolerant plant plants. Climate change would change the distribution of drought-tolerant plants, with more than 50% of the species showing a trend of shrinking ranges in China. For both herb and woody plants, the highest turnover values were observed under SSP585 for the period 2081-2100, reaching 37.67% and 29.08%, respectively. Our results highlighted that herb and woody plants respond differently to climate change stresses, with herb plants projected to greatly expand their ranges in the future. These insights are vital for evaluating the impacts of climate change on biodiversity and informing the development of effective adaptation strategies.

Drought-tolerant species play a crucial role in maintaining ecosystem services in arid and semi-arid regions wherein subject to rapid climate change. However, how future climate change affect the distribution of drought-tolerant plants with different growth forms (e.g., herb and woody) remains largely unknown. Here, we used the MaxEnt model to simulate the potential species distribution under current conditions, and predicted the future species distribution of 82 common drought-tolerant plants in China under two time periods (2041-2060 and 2081-2100) and three climate change scenarios (SSP126, SSP245 and SSP585) in the future. We found that the western and northern regions of China are hotspots for drought-tolerant plant distribution. Compared with other predictors, aridity index (AI) explained the largest portion of variation (45%) in the distribution patterns of drought-tolerant plant plants. Climate change would change the distribution of drought-tolerant plants, with more than 50% of the species showing a trend of shrinking ranges in China. For both herb and woody plants, the highest turnover values were observed under SSP585 for the period 2081-2100, reaching 37.67% and 29.08%, respectively. Our results highlighted that herb and woody plants respond differently to climate change stresses, with herb plants projected to greatly expand their ranges in the future. These insights are vital for evaluating the impacts of climate change on biodiversity and informing the development of effective adaptation strategies.

In this paper, a hybrid modeling approach is proposed to model two-phase flow evaporators. The main procedures for hybrid modeling includes: (1) Based on the energy and material balance, and thermodynamic principles to formulate the process fundamental governing equations; (2) Select input/output (I/O) variables responsible to the system performance which can be measured and controlled; (3) Represent those variables existing in the original equations but are not measurable as simple functions of selected I/Os or constants; (4) Obtaining a single equation which can correlate system inputs and outputs; and (5) Identify unknown parameters by linear or nonlinear least-squares methods. The method takes advantages of both physical and empirical modeling approaches and can accurately predict performance in wide operating range and in real-time, which can significantly reduce the computational burden and increase the prediction accuracy. The model is verified with the experimental data taken from a testing system. The testing results show that the proposed model can predict accurately the performance of the real-time operating evaporator with the maximum error of ±8%. The developed models will have wide applications in operational optimization, performance assessment, fault detection and diagnosis.