New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Performance optimization of the Växtkraft biogas production plant

All over the world there is a strong interest and also potential for biogas production from organic residues as well as from different crops. However, to be commercially competitive with other types of fuels, efficiency improvements of the biogas production process are needed. In this paper, results of improvements studies done on a full scale co-digestion plant are presented   In the plant organic wastes from households and restaurants are mixed and digested with crops from graze land. The areas for improvements of the plant addressed are treatment of the feed material to enhance the digestion rate, limitation of the ballast of organics in the water stream recirculated in the process, and use of the biogas plant residues at farms. Results from previous studies on pre-treatment and membrane filtration of recirculated process water are combined for estimation of the total improvement potential. Further, the possibility to use neural networks to predict biogas production using historical data from the full-scale biogas plant was investigated. Results from investigation of using the process residues as fertilizer are also presented.   The results indicates a potential to increase the biogas yield from the process with up to over  30 % with pre-treatment of the feed and including membrane filtration in the process. Neural networks have the potential to be used for prediction of biogas production. Further, it is shown that the residues from biogas production can be used as fertilizers but that the emission of N2O from the fertilised soil is dependent on the soil type and spreading technology.This is the author’s version of a work accepted for publication by Elsevier. Changes resulting from the publishing process,including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in thisdocument. Changes may have been made to this work since it was submitted for publication. The definitive version has been published in Applied Energy, vol 97, DOI: 10.1016/j.apenergy.2012.03.007.BioGasOp

Safe Efficient Vehicle Solutions -On Driving Forces for Future Road Transportations

The primary objective of this paper is to present the most relevant factors and driving forces that influence future sustainable road transportations and exemplify how they may influence the development. The research methodology used is explorative scenarios where data collected from workshops, expert panels and surveys lay the foundations for the explanatory models [1]. Several driving forces are identified. However, two of them are found to be more important for the study as they have a strong influence on the development of the road transport system; yet it is uncertain how these driving forces will develop. The first of these driving forces is the ability of the authorities to take an active role when developing a sustainable transport system and the second how actively people will demand and support changes in the vehicles and the transport system. Four different future road transportation scenarios have been created to explore how changes in these two driving forces will influence the development of vehicles and road transport system; these scenarios are explained together with characteristics of future road transportation solutions. It is concluded that plans for technology development need to consider the uncertainties of these driving forces in order to enable creation of robust development roadmaps

Safe Efficient Vehicle Solutions -On Driving Forces for Future Road Transportations

The primary objective of this paper is to present the most relevant factors and driving forces that influence future sustainable road transportations and exemplify how they may influence the development. The research methodology used is explorative scenarios where data collected from workshops, expert panels and surveys lay the foundations for the explanatory models [1]. Several driving forces are identified. However, two of them are found to be more important for the study as they have a strong influence on the development of the road transport system; yet it is uncertain how these driving forces will develop. The first of these driving forces is the ability of the authorities to take an active role when developing a sustainable transport system and the second how actively people will demand and support changes in the vehicles and the transport system. Four different future road transportation scenarios have been created to explore how changes in these two driving forces will influence the development of vehicles and road transport system; these scenarios are explained together with characteristics of future road transportation solutions. It is concluded that plans for technology development need to consider the uncertainties of these driving forces in order to enable creation of robust development roadmaps

Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture.

The extent to which low-frequency (minor allele frequency (MAF) between 1-5%) and rare (MAF ≤ 1%) variants contribute to complex traits and disease in the general population is mainly unknown. Bone mineral density (BMD) is highly heritable, a major predictor of osteoporotic fractures, and has been previously associated with common genetic variants, as well as rare, population-specific, coding variants. Here we identify novel non-coding genetic variants with large effects on BMD (ntotal = 53,236) and fracture (ntotal = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n = 2,882 from UK10K (ref. 10); a population-based genome sequencing consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication genotyping (n = 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size fourfold larger than the mean of previously reported common variants for lumbar spine BMD (rs11692564(T), MAF = 1.6%, replication effect size = +0.20 s.d., Pmeta = 2 × 10(-14)), which was also associated with a decreased risk of fracture (odds ratio = 0.85; P = 2 × 10(-11); ncases = 98,742 and ncontrols = 409,511). Using an En1(cre/flox) mouse model, we observed that conditional loss of En1 results in low bone mass, probably as a consequence of high bone turnover. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817(T), MAF = 1.2%, replication effect size = +0.41 s.d., Pmeta = 1 × 10(-11)). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population

Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture.

The extent to which low-frequency (minor allele frequency (MAF) between 1-5%) and rare (MAF ≤ 1%) variants contribute to complex traits and disease in the general population is mainly unknown. Bone mineral density (BMD) is highly heritable, a major predictor of osteoporotic fractures, and has been previously associated with common genetic variants, as well as rare, population-specific, coding variants. Here we identify novel non-coding genetic variants with large effects on BMD (ntotal = 53,236) and fracture (ntotal = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n = 2,882 from UK10K (ref. 10); a population-based genome sequencing consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication genotyping (n = 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size fourfold larger than the mean of previously reported common variants for lumbar spine BMD (rs11692564(T), MAF = 1.6%, replication effect size = +0.20 s.d., Pmeta = 2 × 10(-14)), which was also associated with a decreased risk of fracture (odds ratio = 0.85; P = 2 × 10(-11); ncases = 98,742 and ncontrols = 409,511). Using an En1(cre/flox) mouse model, we observed that conditional loss of En1 results in low bone mass, probably as a consequence of high bone turnover. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817(T), MAF = 1.2%, replication effect size = +0.41 s.d., Pmeta = 1 × 10(-11)). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population