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Climate change may affect the US electricity system through changes in electricity demand, mediated by increases in average surface temperature, and through changes in electricity supply, mediated by changes in both surface temperature and regional water availability. By coupling projections from four general circulation models (GCMs) with a state-of-the-art US electricity system model—the Regional Energy Deployment System (ReEDS)—this study evaluates both the isolated and combined effects of different climate-mediated drivers of US electricity system change through 2050. Comparing results across climate models allows us to evaluate which effects are robust to uncertainty in projected climate outcomes. Comparing effects of different drivers in isolation and in combination allows us to determine the relative contributions of the climate-mediated effects on system evolution. Our results indicate that national-level energy and economic impacts are largely driven by increases in electricity demand that follow from a consistent increase in surface air temperature that is largely robust to the choice of climate model. Other electricity system changes can be equally or more significant in some regions, but these effects are more regionally variable, less significant when aggregated to the national scale, and less robust to the choice of climate model. The findings show that the impacts of climate change on the electricity system can be understood in terms of fewer drivers and with greater certainty at the national level than at the regional level.

Future climate-water conditions are anticipated to increase electricity demand, reduce transmission capacity, and limit power production. Yet, typical electricity capacity expansion planning does not consider climate-water constraints. We project four alternative U.S. power system configurations using an iterative modeling and data exchange platform that integrates climate-driven hydrological, thermal power plant, and capacity expansion models. Through a comparison with traditional modeling approaches, we show that this novel approach provides greater confidence in electricity capacity projections by incorporating feasibility checks that adjust infrastructure development to reach grid reliability thresholds under climate-water constraints. Initial projections without climate-water impacts on electricity generation show future power systems become less vulnerable, independent of climate-water adaptation, as economic drivers increase renewable and natural gas-based capacity, while water-intensive coal and nuclear plants retire. However, power systems may face reliability challenges without climate-water adaptation, revealing the significance of incorporating climate-water impacts into power system planning. Climate-adjusted (Iterative approach) projections require a 5.3-12.0% increase in national-level capacity, relative to Initial projections, leading to an additional $125-143 billion (5.0-7.0%) in infrastructure costs. Variable renewable and natural gas technologies account for nearly all the additional capacity and, together with regional trade-offs in electricity generation, enhance grid performance to reach reliability thresholds. These adaptation transitions also lower water use and emissions, contributing to climate change mitigation, and highlight the trade-offs and impacts of both near and long-term electricity generation planning decisions.

Hyperlipidemia casts great threats to humans around the world. The systemic co-expression and function enrichment analysis for this disease is limited to date. This study was to identify co-expression modules to explore hyperlipidemia-associated functional pathways. Gene expression data of human hyperlipidemia (GSE17170) were downloaded from the Gene Expression Omnibus (GEO) database. We evaluated the top 3,000 genes with the highest average expression, with which the co-expression modules were constructed in weighted correlation network analysis (WGC-NA).Cluster analysis was then applied to visualize the interaction relationships of these modules. By gene ontology (GO) and KEGG functional enrichment analysis, we finally investigated the function enrichment of co-expression genes from important modules in the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database (https:// david.ncifcrf.gov/summary.jsp).15 Thirteen co-expression modules were constructed for 3,000 genes in the 70samples. Interaction relationships of hub genes between pairwise modules showed high confidence. In functional enrichments of the co-expression modules, genes in Modules 3 and 4 were significantly enriched in biological processes and pathways that are associated with ubiquitination-for example, G0:0016567 (protein ubiquitination) and hsa04120 (ubiquitin-mediated proteolysis). We inferred these two modules as key modules associated with hyperlipidemia. Additionally, G0:0098609 (cell-cell adhesion) was enriched in four modules, suggesting an important function in hyperlipidemia. In conclusion, Protein ubiquitination may play important roles in human hyperlipidemia. All the discoveries made in this study enrich understanding of the pathogenesis of hyperlipidemia and might contribute much to the development of diagnosis and outcome evaluation of this disease.

Recent wind and solar deployment in the United States has grown at unprecedented rates, but the extent to which this growth will continue is a key question. Energy-economic planning models are often used to develop future projections of these technologies, and although it is well-understood that model outcomes are sensitive to assumptions about technology developments and market conditions, it is less clear how underlying model structures might impact outcomes. For wind and solar technologies, model structures might be an even larger driver due to the complexity required to represent their variability, uncertainty, and location-dependence. Here, we apply three state-of-the-art electric sector planning models to evaluate variable renewable generation under different conditions with both harmonized and native input assumptions to isolate the impact that model structures might have on deployment outcomes. We find growth in wind and solar deployment in all models under a business-as-usual scenario (without new policies), where the median value for variable renewable energy generation share is found to be 27% by 2050—but with a wide range of estimates (18–43%), types of renewable technologies deployed, and regional distributions across models. With harmonized assumptions about technology costs and market conditions the range narrows slightly, but a large range in penetration outcomes remains (24–41% by 2050) suggesting that model representations play significant roles in projections of future wind and solar deployment. We also assess the implications of achieving 55% wind and solar penetration by 2050—to assess potential challenges at higher penetration levels—and the magnitude of variable generation under a carbon-constrained future. Finally, we identify research needs to better assess the future deployment of variable renewable energy.

The article “Decomposing supply-side and demand-side impacts of climate change on the US electricity system through 2050”.

Electrification of end-use technologies could have important implications for bulk power sector planning, operations, and impacts. By combining previously-defined electrification scenarios with capacity expansion modeling, we evaluate the potential challenges and opportunities associated with electrification. Our scenario analysis reveals that electrification reduces energy consumption and air emissions from across the energy sector, and its impacts on energy system costs are modest due, in part, to the abundance of low-cost resources in the United States.

Future electricity system portfolios will require strategic transmission expansions for economically integrating regionally dependent, high-quality renewable resources. In this context, accurate representation of transmission flows in large-scale electricity infrastructure capacity expansion planning problems is important. This paper describes an approximate linearized power flow transmission modeling method for such expansion problems and demonstrates the impact on planning solutions using the National Renewable Energy Laboratory's Regional Energy Deployment System (ReEDS) model. Results are compared against a pipe flow transmission representation, which is used in a number of large-scale planning models. We show that national-scale generation and transmission expansion is not substantially influenced by the power flow methodology but that the pipe flow representation does underestimate required transmission capacity and total system costs.

Environmental problems, associated with climate change and air pollution, have become increasingly serious for China in recent years, which have aroused great domestic and international concerns. To mitigate these problems with great efforts, the Chinese government has implemented the Environmental Protection Tax Law in the whole country since the beginning of 2018. Although the new tax law is perceived as an aggressive policy that tends to establish a taxation system for promoting air pollution control, evaluations of its effectiveness are insufficient and urgently needed for China. Using a multiregion multisector Computable General Equilibrium model, we, for the first time, quantify the impacts of this ‘pollution tax’ policy on modulating air pollutants emissions. Our analysis shows that current tax policy is generally able to reduce many short-lived air pollutants emissions (e.g. SO2, NO X , TSP, PM10, PM2.5, CO, VOCs, OC, NH3 and BC), but the significant effects only happen in regions with large economic scale (i.e. Guangdong, Shandong and Zhejiang provinces) and in sectors with high emission intensity (i.e. the electric power and nonmetal manufacturing sectors). However, at the national level, the overall effect of the current policy on air pollution mitigation is relatively small, less than 2% compared to a business-as-usual scenario. Large emission reduction potentials exist if the tax increases. Therefore, a more ambitious tax policy is urgently needed in order to achieve China’s air pollution mitigation target of 2020. We also find that in China for implementing any pollution tax policies, the rate of decline in CO2 emissions is much larger than those of short-lived pollutants, which indicates a huge co-benefit on global climate change mitigation.

Background Electroacupuncture (EA) may offer an effective alternative approach for the treatment of obesity. EA mobilizes energy stores, but its effect on hepatic lipid metabolism is unknown, and the underlying mechanisms remain unclear. Objective To examine the effect of EA on hepatic lipid accumulation in diet-induced obese (DIO) rats, and to explore potential underlying mechanisms. Methods Male Sprague-Dawley rats were fed a normal diet (control group, n=10) or a high-fat diet (HFD) for 12 weeks to induce obesity. Those exhibiting diet-induced obesity were subdivided into two groups, one receiving EA (DIO+EA group, n=10) and one left untreated (DIO group, n=10) and observed for a further 4 weeks. Body, liver and fat pad weight were measured, and liver injury was assessed histologically as well as by measuring serum values of alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Hepatic triglyceride (TG) and total cholesterol were quantified by enzymatic colorimetric methods. Expression of liver AMP-activated protein kinase (AMPK), acetyl-coenzyme A carboxylase (ACC), and carnitine palmitoyltransferase (CPT-1) was measured by Western blotting. Results EA treatment led to a reduction in body, liver and fat pad weight in DIO rats. This was accompanied by decreases in hepatic TG and total cholesterol values, fatty droplet accumulation, and serum concentrations of ALT and AST. Furthermore, EA treatment restored phosphorylation levels of AMPK (Thr172) and ACC (Ser79) inhibited by HFD, and increased CPT-1 expression. Conclusions EA reduces HFD-induced hepatic lipid accumulation, an effect that appears to be mediated through AMPK signalling pathways. Our results shed new light on the mechanisms by which EA may reduce obesity.

Objective To observe the effects of acupuncture and moxibustion on the skin histomorphology of senile mice.