Energy rebound as a potential threat to a low-carbon future: findings from a new exergy-based national-level rebound approach

150 years ago, Stanley Jevons introduced the concept of energy rebound: that anticipated energy efficiency savings may be “taken back” by behavioural responses. This is an important issue today because, if energy rebound is significant, this would hamper the effectiveness of energy efficiency policies aimed at reducing energy use and associated carbon emissions. However, empirical studies which estimate national energy rebound are rare and, perhaps as a result, rebound is largely ignored in energy-economy models and associated policy. A significant difficulty lies in the components of energy rebound assessed in empirical studies: most examine direct and indirect rebound in the static economy, excluding potentially significant rebound of the longer term structural response of the national economy. In response, we develop a novel exergy-based approach to estimate national energy rebound for the UK and US (1980–2010) and China (1981–2010). Exergy—as “available energy”—allows a consistent, thermodynamic-based metric for national-level energy efficiency. We find large energy rebound in China, suggesting that improvements in China’s energy efficiency may be associated with increased energy consumption (“backfire”). Conversely, we find much lower (partial) energy rebound for the case of the UK and US. These findings support the hypothesis that producer-sided economies (such as China) may exhibit large energy rebound, reducing the effectiveness of energy efficiency, unless other policy measures (e.g., carbon taxes) are implemented. It also raises the prospect we need to deploy renewable energy sources faster than currently planned, if (due to rebound) energy efficiency policies cannot deliver the scale of energy reduction envisaged to meet climate targets

Dietary Cholesterol Promotes Adipocyte Hypertrophy and Adipose Tissue Inflammation in Visceral, But Not Subcutaneous, Fat in Monkeys

Objective—Excessive caloric intake is associated with obesity and adipose tissue dysfunction. However, the role of dietary cholesterol in this process is unknown. The aim of this study was to determine whether increasing dietary cholesterol intake alters adipose tissue cholesterol content, adipocyte size, and endocrine function in nonhuman primates. Approach and Results—Age-matched, male African Green monkeys (n=5 per group) were assigned to one of three diets containing 0.002 (Lo), 0.2 (Med) or 0.4 (Hi) mg cholesterol/Kcal. After 10 weeks of diet feeding, animals were euthanized for adipose tissue, liver, and plasma collection. With increasing dietary cholesterol, free cholesterol (FC) content and adipocyte size increased in a step-wise manner in visceral, but not subcutaneous fat, with a significant association between visceral adipocyte size and FC content (r2=0.298; n=15; p=0.035). In visceral fat, dietary cholesterol intake was associated with: 1) increased pro-inflammatory gene expression and macrophage recruitment, 2) decreased expression of genes involved in cholesterol biosynthesis and lipoprotein uptake, and 3) increased expression of proteins involved in FC efflux. Conclusions—Increasing dietary cholesterol selectively increases visceral fat adipocyte size, FC and macrophage content, and proinflammatory gene expression in nonhuman primates

Development of novel membrane disrupting lipoguanidine compounds sensitizing Gram-negative bacteria to antibiotics

A new class of amphiphilic molecules, the lipoguanidines, designed as hybrids of guanidine and fatty acid compounds, has been synthesized and developed. The new molecules present both a guanidine polar head and a lipophilic tail that allow them to disrupt bacterial membranes and to sensitize Gram-negative bacteria to the action of the narrow-spectrum antibiotics rifampicin and novobiocin. The lipoguanidine 5g sensitizes Klebsiella pneumonia, Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli to rifampicin, thereby reducing the antibiotic minimum inhibitory concentrations (MIC) up to 256-fold. Similarly, 5g is able to potentiate novobiocin up to 64-fold, thereby showing a broad spectrum of antibiotic potentiating activity. Toxicity and mechanism studies revealed the potential of 5g to work synergistically with rifampicin through the disruption18 of bacterial membranes without affecting eukaryotic cells

Field-based high-throughput plant phenotyping reveals the temporal patterns of quantitative trait loci associated with stress-responsive traits in cotton

The application of high-throughput plant phenotyping (HTPP) to continuously study plant populations under relevant growing conditions creates the possibility to more efficiently dissect the genetic basis of dynamic adaptive traits. Towards this end, we employed a field-based HTPP system that deployed sets of sensors to simultaneously measure canopy temperature, reflectance, and height on a cotton (Gossypium hirsutum L.) recombinant inbred line mapping population. The evaluation trials were conducted under well-watered and water-limited conditions in a replicated field experiment at a hot, arid location in central Arizona, with trait measurements taken at different times on multiple days across three years. Canopy temperature, normalized difference vegetation index (NDVI), height, and leaf area index (LAI) displayed moderate to high broad-sense heritabilities as well as varied interactions among genotypes with water regime and time of day. Distinct temporal patterns of quantitative trait loci (QTL) expression were mostly observed for the more dynamic HTPP canopy traits, canopy temperature and NDVI, and varied across plant developmental stages. In addition, the strength of correlation between HTPP canopy and agronomic traits such as lint yield displayed a time-dependent relationship. We also found that the position of some QTL controlling HTPP canopy traits were shared with agronomic and physiological traits. This work demonstrates the novel use of a field-based, HTPP system to study the genetic basis of stress-adaptive traits in cotton, and these results have the potential to facilitate the development of stress-resilient cotton cultivars

Energy-Extended CES Aggregate Production: Current Aspects of Their Specification and Econometric Estimation

Capital–labour–energy Constant Elasticity of Substitution (CES) production functions and their estimated parameters now form a key part of energy–economy models which inform energy and emissions policy. However, the collation and guidance as to the specification and estimation choices involved with such energy-extended CES functions is disparate. This risks poorly specified and estimated CES functions, with knock-on implications for downstream energy–economic models and climate policy. In response, as a first step, this paper assembles in one place the major considerations involved in the empirical estimation of these CES functions. Discussions of the choices and their implications lead to recommendations for CES empiricists. The extensive bibliography allows those interested to dig deeper into any aspect of the CES parameter estimation process

Untangling the drivers of energy reduction in the UK productive sectors: Efficiency or offshoring?

The UK has been one of the few countries that has successfully decoupled final energy consumption from economic growth over the past 15 years. This study investigates the drivers of final energy consumption in the UK productive sectors between 1997 and 2013 using a decomposition analysis that incorporates two novel features. Firstly, it investigates to what extent changes in thermodynamic efficiency have contributed to overall changes in sectoral energy intensities. Secondly, it analyses how much of the structural change in the UK economy is driven by the offshoring of energy-intensive production overseas. The results show that energy intensity reductions are the strongest factor reducing energy consumption. However, only a third of the energy savings from energy intensity reductions can be attributed to reductions in thermodynamic efficiency with re- ductions in the exergy intensity of production making up the reminder. In addition the majority of energy savings from structural change are a result of offshoring, which constitutes the second biggest factor reducing energy consumption. In recent years the contributions of all decomposition factors have been declining with very little change in energy consumption after 2009. This suggests that a return to the strong reductions in energy consumption observed between 2001 and 2009 in the UK productive sectors should not be taken for granted. Given that further reductions in UK final energy consumption are needed to achieve global targets for climate change mitigation, additional policy interventions are needed. Such policies should adopt a holistic approach, taking into account all sectors in the UK economy as well as the relationship between the structural change in the UK and in the global supply chains delivering the goods and service for consumption and investment in the UK

Investigation of the influence of leaf thickness on canopy reflectance and physiological traits in upland and Pima cotton populations

Many systems for field-based, high-throughput phenotyping (FB-HTP) quantify and characterize the reflected radiation from the crop canopy to derive phenotypes, as well as infer plant function and health status. However, given the technology’s nascent status, it remains unknown how biophysical and physiological properties of the plant canopy impact downstream interpretation and application of canopy reflectance data. In that light, we assessed relationships between leaf thickness and several canopy-associated traits, including normalized difference vegetation index (NDVI), which was collected via active reflectance sensors carried on a mobile FB-HTP system, carbon isotope discrimination (CID), and chlorophyll content. To investigate the relationships among traits, two distinct cotton populations, an upland (Gossypium hirsutum L.) recombinant inbred line (RIL) population of 95 lines and a Pima (G. barbadense L.) population composed of 25 diverse cultivars, were evaluated under contrasting irrigation regimes, water-limited (WL) and well-watered (WW) conditions, across three years. We detected four quantitative trait loci (QTL) and significant variation in both populations for leaf thickness among genotypes as well as high estimates of broad-sense heritability (on average, above 0.7 for both populations), indicating a strong genetic basis for leaf thickness. Strong phenotypic correlations (maximum r = - 0.73) were observed between leaf thickness and NDVI in the Pima population, but not the RIL population. Additionally, estimated genotypic correlations within the RIL population for leaf thickness with CID, chlorophyll content, and nitrogen discrimination (푟̂푔푖푗 = -0.32, 0.48, and 0.40, respectively) were all significant under WW but not WL conditions. Economically important fiber quality traits did not exhibit significant phenotypic or genotypic correlations with canopy traits. Overall, our results support considering variation in leaf thickness as a potential contributing factor to variation in NDVI or other canopy traits measured via proximal sensing, and as a trait that impacts fundamental physiological responses of plants

Gene-based analysis in HRC imputed genome wide association data identifies three novel genes for Alzheimer's disease.

Late onset Alzheimer's disease is the most common form of dementia for which about 30 susceptibility loci have been reported. The aim of the current study is to identify novel genes associated with Alzheimer's disease using the largest up-to-date reference single nucleotide polymorphism (SNP) panel, the most accurate imputation software and a novel gene-based analysis approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 million genotypes from 17,008 Alzheimer's cases and 37,154 controls. In addition to earlier reported genes, we detected three novel gene-wide significant loci PPARGC1A (p = 2.2 × 10-6), RORA (p = 7.4 × 10-7) and ZNF423 (p = 2.1 × 10-6). PPARGC1A and RORA are involved in circadian rhythm; circadian disturbances are one of the earliest symptoms of Alzheimer's disease. PPARGC1A is additionally linked to energy metabolism and the generation of amyloid beta plaques. RORA is involved in a variety of functions apart from circadian rhythm, such as cholesterol metabolism and inflammation. The ZNF423 gene resides in an Alzheimer's disease-specific protein network and is likely involved with centrosomes and DNA damage repair

ABCA7 p.G215S as potential protective factor for Alzheimer’s disease

Genome-wide association studies (GWASs) have been effective approaches to dissect common genetic variability underlying complex diseases in a systematic and unbiased way. Recently, GWASs have led to the discovery of over 20 susceptibility loci for Alzheimer’s disease (AD). Despite the evidence showing the contribution of these loci to AD pathogenesis, their genetic architecture has not been extensively investigated, leaving the possibility that low frequency and rare coding variants may also occur and contribute to the risk of disease. We have used exome and genome sequencing data to analyse the single independent and joint effect of rare and low frequency protein coding variants in 9 AD GWAS loci with the strongest effect sizes after APOE (BIN1, CLU, CR1, PICALM, MS4A6A, ABCA7, EPHA1, CD33, CD2AP) in a cohort of 332 sporadic AD cases and 676 elderly controls of British and North American ancestry. We identified coding variability in ABCA7 as contributing to AD risk. This locus harbors a low frequency coding variant (p.G215S, rs72973581, MAF=4.3%) conferring a modest but statistically significant protection against AD (p-value= 6x10-4, OR=0.57, 95% CI 0.41-0.80). Notably, our results are not driven by an enrichment of loss of function variants in ABCA7, recently reported as main pathogenic factor underlying AD risk at this locus. In summary, our study confirms the role of ABCA7 in AD and provide new insights that should address functional studies