Land Resources for Wind Energy Development Requires Regionalized Characterizations.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

The state of macro-energy systems research: Common critiques, current progress, and research priorities.

The growing field of macro-energy systems (MES) brings together the interdisciplinary community of researchers studying the equitable and low-carbon future of humanity's energy systems. As MES matures as a community of scholars, a coherent consensus about the key challenges and future directions of the field can be lacking. This paper is a response to this need. In this paper, we first discuss the primary critiques of model-based MES research that have emerged because MES was proposed as a way to unify related interdisciplinary research. We discuss these critiques and current efforts to address them by the coalescing MES community. We then outline future directions for growth motivated by these critiques. These research priorities include both best practices for the community and methodological improvements.

Climate-Water Adaptation for Future US Electricity Infrastructure.

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.

Correlation Network Analysis Provides Important Modules and Pathways for Human Hyperlipidemia.

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.

Electroacupuncture attenuates hepatic lipid accumulation via AMP-activated protein kinase (AMPK) activation in obese rats.

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 (Thr(172)) and ACC (Ser(79)) 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.

[Anti-depression effect of acupunctrue and moxibustion based on SSRIs medication].

OBJECTIVE: To verify the anti-depression effect of acupuncture and moxibustion based on the medication with selective serotonin reuptake inhibitors (SSRIs). METHODS: Eighty cases of depression were randomly divided into an acupuncture-moxibustion-medication group (25 cases), an acupuncture-medication group (25 cases) and a medication group (30 cases). SSRIs medication was administered in all of the three groups. Complementarily, in acupuncture-moxibustion-medication group, the needling technique of qi conduction in the Governor Vessel was applied to Baihui (GV 20), Fengfu (GV 16), Dazhui (GV 14), etc. Additionally, mild moxibustion was added at Dazhui (GV 14) and Baihui (GV 20). In acupuncture-medication group, acupuncture for qi conduction in the Governor Vessel was only adopted. Hamilton Depression Scale (HAMD) was used for the evaluation of the total score, the score of each factor before and after treatment separately, and the therapeutic effects were observed among 3 groups. RESULTS: Compared with medication group, the scores of the factors as retardation, sleep, and anxiety/somatization, as well as the total score were all apparently improved in the other two groups (P < 0.05, P < 0.01). Compared with acupuncture-medication group, the scores of sleep and cognition factors as well as the total score in HAMD were much improved in acupuncture-moxibustion-medication group (P < 0.05, P < 0.01). The remarkable effective rates were 100.0% (25/25), 84.0% (21/25) and 56.7% (17/30) in the three groups separately, in which, the result in acupuncture-moxibustion-medication group was superior to acupuncture-medication group (P < 0.05), and the results of these two groups were superior to medication group (both P < 0.01). CONCLUSION: Either acupunctrure or moxibustion has a definite anti-depression effect based on SSRIs medication, but the coordination of acupuncture and moxibustion achieves a superior efficacy as compared with simple acupuncture therapy.