Cost and performance of some carbon capture technology options for producing different quality CO₂ product streams

A techno-economic assessment of power plants with CO2 capture technologies with a focus on process scenarios that deliver different grades of CO2 product purity is presented. The three leading CO2 capture technologies are considered, namely; oxyfuel combustion, pre-combustion and post-combustion capture. The study uses a combination of process simulation of flue gas cleaning processes, modelling with a power plant cost and performance calculator and literature values of key performance criteria in order to evaluate the performance, cost and CO2 product purity of the considered CO2 capture options. For oxyfuel combustion capture plants, three raw CO2 flue gas processing strategies of compression and dehydration only, double flash system purification and distillation purification are considered. Analysis of pre-combustion capture options is based on integrated gasification combined cycle plants using physical solvent systems for capturing CO2 and sulfur species via three routes; co-capture of sulfur impurities with the CO2 stream using Selexol™ solvent, separate capture of CO2 and sulfur impurities using Selexol™, and Rectisol® solvent systems for separate capture of sulfur impurities and CO2. Analysis of post-combustion capture plants was made with and without some conventional pollution control devices. The results highlight the wide variation in CO2 product purity for different oxyfuel combustion capture scenarios and the wide cost variation for the pre-combustion capture scenarios. The post-combustion capture plant with conventional pollution control devices offers high CO2 purity (99.99 mol%) for average cost of considered technologies. The calculations performed will be of use in further analyses of whole chain CCS for the safe and economic capture, transport and storage of CO2

Element distribution is altered in a zone surrounding human glioblastoma multiforme

Recent data indicate that A(1) adenosine receptor (A(1)AR) density is increased in a zone surrounding human and experimental gliomas. On the contrary, tumor tissue and adjacent brain tissue show low to intermediate A(1)AR densities. In order to assess whether changes in A(1)AR expression are indicating further processes of a chemical reorganization of the peritumoral zone, we investigated element concentrations and distribution patterns of copper and zinc in six human glioblastoma multiforme (GBM) specimens by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). Uranium and lead were used as external standards. Copper and zinc levels were increased in a peritumoral zone corresponding to the region of elevated A(1)AR density. They showed a lower density in the solid tumor in comparison to surrounding brain tissue, although the cellular density was higher within GBM. Our findings suggest that the immediate vicinity of GBM is characterized by increased levels of copper and zinc supporting the view that higher A(1)AR density surrounding GBM is not an isolated alteration of peritumoral tissue but an indicator of complex changes in the vicinity of infiltrative tumors. Further research is needed to explore the pathophysiological consequences of altered peritumoral element distribution

Comparison results for stochastic volatility models via coupling

The aim of this paper is to investigate the properties of stochastic volatility models, and to discuss to what extent, and with regard to which models, properties of the classical exponential Brownian motion model carry over to a stochastic volatility setting. The properties of the classical model of interest include the fact that the discounted stock price is positive for all t but converges to zero almost surely, the fact that it is a martingale but not a uniformly integrable martingale, and the fact that European option prices (with convex payoff functions) are convex in the initial stock price and increasing in volatility. We explain why these properties are significant economically, and give examples of stochastic volatility models where these properties continue to hold, and other examples where they fail. The main tool is a construction of a time-homogeneous autonomous volatility model via a time-change

Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: Findings from the ENIGMA Epigenetics Working Group

DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)—three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions

Robust pricing–hedging dualities in continuous time

We pursue a robust approach to pricing and hedging in mathematical finance. We consider a continuous-time setting in which some underlying assets and options, with continuous price paths, are available for dynamic trading and a further set of European options, possibly with varying maturities, is available for static trading. Motivated by the notion of prediction set in Mykland (Ann. Stat. 31:1413–1438, 2003), we include in our setup modelling beliefs by allowing to specify a set of paths to be considered, e.g. superreplication of a contingent claim is required only for paths falling in the given set. Our framework thus interpolates between model-independent and model-specific settings and allows us to quantify the impact of making assumptions or gaining information. We obtain a general pricing–hedging duality result: the infimum over superhedging prices of an exotic option with payoff G is equal to the supremum of expectations of G under calibrated martingale measures. Our results include in particular the martingale optimal transport duality of Dolinsky and Soner (Probab. Theory Relat. Fields 160:391–427, 2014) and extend it to multiple dimensions, multiple maturities and beliefs which are invariant under time-changes. In a general setting with arbitrary beliefs and for a uniformly continuous G, the asserted duality holds between limiting values of perturbed problems