Results from aerosol measurement in amine plant treating gas turbine and Residue Fluidized Catalytic Cracker flue gases at the CO2 Technology Centre Mongstad

This work discusses the relation between flue gas particle content, mainly related to sulfuric acid aerosols and dust, and corresponding MEA emissions. The work lays grounds for future necessary pre-treatment options for various flue gases with high aerosol content in order to operate post-combustion amine plants with minimum emissions. In 2015, the CO2 Technology Center Mongstad (TCM DA), operated a test campaign using aqueous monoethanolamine ( MEA) solvent at 30 wt%. The main objective was to demonstrate and document the performance of the TCM DA Amine Plant located in Mongstad, Norway. Two weeks were dedicated to the aerosol measurement testing. (C) 2017 The Authors. Published by Elsevier Ltd

The study protocol of the Norwegian randomized controlled trial of electroconvulsive therapy in treatment resistant depression in bipolar disorder

<p>Abstract</p> <p>Background</p> <p>The treatment of depressive phases of bipolar disorder is challenging. The effects of the commonly used antidepressants in bipolar depression are questionable. Electroconvulsive therapy is generally considered to be the most effective treatment even if there are no randomized controlled trials of electroconvulsive therapy in bipolar depression. The safety of electroconvulsive therapy is well documented, but there are some controversies as to the cognitive side effects. The aim of this study is to compare the effects and side effects of electroconvulsive therapy to pharmacological treatment in treatment resistant bipolar depression. Cognitive changes and quality of life during the treatment will be assessed.</p> <p>Methods/Design</p> <p>A prospective, randomised controlled, multi-centre six- week acute treatment trial with seven clinical assessments. Follow up visit at 26 weeks or until remission (max 52 weeks). A neuropsychological test battery designed to be sensitive to changes in cognitive function will be used. Setting: Nine study centres across Norway, all acute psychiatric departments. Sample: n = 132 patients, aged 18 and over, who fulfil criteria for treatment resistant depression in bipolar disorder, Montgomery Åsberg Depression Rating Scale Score of at least 25 at baseline. Intervention: Intervention group: 3 sessions per week for up to 6 weeks, total up to 18 sessions. Control group: algorithm-based pharmacological treatment as usual.</p> <p>Discussion</p> <p>This study is the first randomized controlled trial that aims to investigate whether electroconvulsive therapy is better than pharmacological treatment as usual in treatment resistant bipolar depression. Possible long lasting cognitive side effects will be evaluated. The study is investigator initiated, without support from industry.</p> <p>Trial registration</p> <p>NCT00664976</p

Low back pain and widespread pain predict sickness absence among industrial workers

BACKGROUND: The prevalence of musculoskeletal disorders (MSD) in the aluminium industry is high, and there is a considerable work-related fraction. More knowledge about the predictors of sickness absence from MSD in this industry will be valuable in determining strategies for prevention. The aim of this study was to analyse the relative impact of body parts, psychosocial and individual factors as predictors for short- and long-term sickness absence from MSD among industrial workers. METHODS: A follow-up study was conducted among all the workers at eight aluminium plants in Norway. A questionnaire was completed by 5654 workers at baseline in 1998. A total of 3320 of these participated in the follow-up study in 2000. Cox regression analysis was applied to investigate the relative impact of MSD in various parts of the body and of psychosocial and individual factors reported in 1998 on short-term and long-term sickness absence from MSD reported in 2000. RESULTS: MSD accounted for 45% of all working days lost the year prior to follow-up in 2000. Blue-collar workers had significantly higher risk than white-collar workers for both short- and long-term sickness absence from MSD (long-term sickness absence: RR = 3.04, 95% CI 2.08–4.45). Widespread and low back pain in 1998 significantly predicted both short- and long-term sickness absence in 2000. In addition, shoulder pain predicted long-term sickness absence. Low social support predicted short-term sickness absence (RR = 1.28, 95% CI 1.11–1.49). CONCLUSIONS: Reducing sickness absence from MSD among industrial workers requires focusing on the working conditions of blue-collar workers and risk factors for low back pain and widespread pain. Increasing social support in the work environment may have effects in reducing short-term sickness absence from MSD

Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.

To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity

Performance evaluation and optimisation of post combustion CO2 capture processes for natural gas applications at pilot scale via a verified rate-based model

CO2 absorption based on chemical reactions is one of the most promising technologies for post combustion CO2 capture (PCC). There have been significant efforts to develop energy efficient and cost effective PCC processes. Given that PCC is still maturing as a technology, there will be a continuing need for pilot scale facilities to support process optimisation, especially in terms of energy efficiency. Pilot scale PCC facilities, which are usually orders of magnitude smaller than those that will be used in future in large scale fossil power plants, make it possible to study details of the PCC process at an affordable scale. However, it is essential that pilot scale studies provide credible data, if this is to be used with confidence to envisage the future large-scale use of the PCC process, especially in terms of energy consumption. The present work therefore establishes and experimentally verifies (using a representative pilot plant as a case study) procedures for analysing the energy performance of a pilot scale amine based CO2 capture plants, focusing on natural gas fired applications. The research critically assesses the pilot plant’s current energy performance, and proposes new operating conditions and system modifications by which the pilot plant will operate more efficiently in terms of energy consumption. The methodology developed to assess and improve the energy performance of the PCC process is applicable, with appropriate inputs, to other plants of this type that employs aqueous 30 wt. % monoethanolamine (MEA) solution as the solvent. A rate based model of the post combustion CO2 capture process using an aqueous solution of 30 wt. % MEA as the solvent was developed in Aspen Plus® V.8.4, and verified using the results of experimental studies carried out using the UK Carbon Capture and Storage Research Centre / Pilot-scale Advanced Capture Technology (UKCCSRC/PACT) pilot plant, as a representative pilot-scale capture plant, and employed for parametric sensitivity studies. Several parameters have been identified and varied over a given range of lean solvent CO2 loading to evaluate their effects on the pilot plant energy requirement. The optimum lean solvent CO2 loading was determined using the total equivalent work concept. Results show, for a given packing material type, the majority of energy savings can be realised by optimising the stripper operating pressure. To some extent, a higher solvent temperature at the stripper inlet has the potential to reduce the regeneration energy requirement. A more efficient packing material, can greatly improve the pilot plant overall energy and mass transfer efficiency

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE-Glycated hemoglobin (HbA(1c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA(1c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA(1c) levels.RESEARCH DESIGN AND METHODS-We studied associations with HbA(1c) in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA(1c) loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening.RESULTS-Ten loci reached genome-wide significant association with HbA(1c), including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 x 10(-26)), HFE (rs1800562/P = 2.6 x 10(-20)), TMPRSS6 (rs855791/P = 2.7 x 10(-14)), ANK1 (rs4737009/P = 6.1 x 10(-12)), SPTA1 (rs2779116/P = 2.8 x 10(-9)) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 x 10(-9)), and four known HbA(1c) loci: HK1 (rs16926246/P = 3.1 x 10(-54)), MTNR1B (rs1387153/P = 4.0 X 10(-11)), GCK (rs1799884/P = 1.5 x 10(-20)) and G6PC2/ABCB11 (rs552976/P = 8.2 x 10(-18)). We show that associations with HbA(1c) are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (%HbA(1c)) difference between the extreme 10% tails of the risk score, and would reclassify similar to 2% of a general white population screened for diabetes with HbA(1c).CONCLUSIONS-GWAS identified 10 genetic loci reproducibly associated with HbA(1c). Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA(1c) levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA(1c) Diabetes 59: 3229-3239, 201