The land use change impact of biofuels consumed in the EU: Quantification of area and greenhouse gas impacts

Biofuels are promoted as an option to reduce climate emissions from the transport sector. As most biofuels are currently produced from land based crops, there is a concern that the increased consumption of biofuels requires agricultural expansion at a global scale, leading to additional carbon emissions. This effect is called Indirect Land Use Change, or ILUC. The EU Renewable Energy Directive (2009/28/EC) directed the European Commission to develop a methodology to account for the ILUC effect. The current study serves to provide new insights to the European Commission and other stakeholders about these indirect carbon and land impacts from biofuels consumed in the EU, with more details on production processes and representation of individual feedstocks than was done before. ILUC cannot be observed or measured in reality, because it is entangled with a large number of other changes in agricultural markets at both global and local levels. The effect can only be estimated through the use of models. The current study is part of a continuous effort to improve the understanding and representation of ILUC

Assessment and optimisation of normalisation methods for dual-colour antibody microarrays

<p>Abstract</p> <p>Background</p> <p>Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm.</p> <p>Results</p> <p>We compare the performance of several normalisation methods that have been established for dual-colour gene expression microarrays. The focus is on an invariant selection algorithm, for which effective improvements are proposed. In a simulation study the performances of the different normalisation methods are compared with respect to their impact on the ability to correctly detect differentially expressed features. Furthermore, we apply the different normalisation methods to a pancreatic cancer data set to assess the impact on the classification power.</p> <p>Conclusions</p> <p>The simulation study and the data application demonstrate the superior performance of the improved invariant selection algorithms in comparison to other normalisation methods, especially in situations where the assumptions of the usual global loess normalisation are violated.</p

A semi-nonparametric mixture model for selecting functionally consistent proteins

Background High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins. Results We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions. Conclusions We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein

Energy Efficiency Analysis: Biomass-to-Wheel Efficiency Related with Biofuels Production, Fuel Distribution, and Powertrain Systems

BACKGROUND: Energy efficiency analysis for different biomass-utilization scenarios would help make more informed decisions for developing future biomass-based transportation systems. Diverse biofuels produced from biomass include cellulosic ethanol, butanol, fatty acid ethyl esters, methane, hydrogen, methanol, dimethyether, Fischer-Tropsch diesel, and bioelectricity; the respective powertrain systems include internal combustion engine (ICE) vehicles, hybrid electric vehicles based on gasoline or diesel ICEs, hydrogen fuel cell vehicles, sugar fuel cell vehicles (SFCV), and battery electric vehicles (BEV). METHODOLOGY/PRINCIPAL FINDINGS: We conducted a simple, straightforward, and transparent biomass-to-wheel (BTW) analysis including three separate conversion elements--biomass-to-fuel conversion, fuel transport and distribution, and respective powertrain systems. BTW efficiency is a ratio of the kinetic energy of an automobile's wheels to the chemical energy of delivered biomass just before entering biorefineries. Up to 13 scenarios were analyzed and compared to a base line case--corn ethanol/ICE. This analysis suggests that BEV, whose electricity is generated from stationary fuel cells, and SFCV, based on a hydrogen fuel cell vehicle with an on-board sugar-to-hydrogen bioreformer, would have the highest BTW efficiencies, nearly four times that of ethanol-ICE. SIGNIFICANCE: In the long term, a small fraction of the annual US biomass (e.g., 7.1%, or 700 million tons of biomass) would be sufficient to meet 100% of light-duty passenger vehicle fuel needs (i.e., 150 billion gallons of gasoline/ethanol per year), through up to four-fold enhanced BTW efficiencies by using SFCV or BEV. SFCV would have several advantages over BEV: much higher energy storage densities, faster refilling rates, better safety, and less environmental burdens