High pressure pre-treatments promote higher rate and degree of enzymatic hydrolysis of cellulose

The effect of high pressure (HP) pre-treatments on the subsequent enzymatic hydrolysis of cellulose from bleached kraft Eucalyptus globulus pulp by cellulase from Tricoderma viride was evaluated. Pressure pre-treatments of 300 and 400 MPa during 5–45 min, lead to both an increased rate and degree of hydrolysis, reaching values ranging from 1.5- to 1.9-fold, quantified by the formation of reducing sugars. Both the pressure and time under pressure influenced the enzymatic hydrosability of the cellulosic pulps, with the former being more important. The results indicate that the pressure pre-treatments promoted an increased accessibility of cellulose towards cellulase in the cell wall. The results obtained open promising possibilities, to contribute to overcome conventional limitations of enzymatic cellulose hydrolysis for the production of fermentable glucose, for the production of second generation bioethanol and chemicals by enhancement of both rate and yield of hydrolysis. The results are also of interest for the preparation of “pressure engineered” celullose with incremented tailored hydrolysis patterns

Direct nitrous oxide emissions from oilseed rape cropping - a meta-analysis

Oilseed rape is one of the leading feedstocks for biofuel production in Europe. The climate change mitigation effect of rape methyl ester (RME) is particularly challenged by the greenhouse gas (GHG) emissions during crop production, mainly as nitrous oxide (N2O) from soils. Oilseed rape requires high nitrogen fertilization and crop residues are rich in nitrogen, both potentially causing enhanced N2O emissions. However, GHG emissions of oilseed rape production are often estimated using emission factors that account for crop-type specifics only with respect to crop residues. This meta-analysis therefore aimed to assess annual N2O emissions from winter oilseed rape, to compare them to those of cereals and to explore the underlying reasons for differences. For the identification of the most important factors, linear mixed effects models were fitted with 43 N2O emission data points deriving from 12 different field sites. N2O emissions increased exponentially with N-fertilization rates, but interyear and site-specific variability were high and climate variables or soil parameters did not improve the prediction model. Annual N2O emissions from winter oilseed rape were 22% higher than those from winter cereals fertilized at the same rate. At a common fertilization rate of 200 kg N ha−1 yr−1, the mean fraction of fertilizer N that was lost as N2O-N was 1.27% for oilseed rape compared to 1.04% for cereals. The risk of high yield-scaled N2O emissions increased after a critical N surplus of about 80 kg N ha−1 yr−1. The difference in N2O emissions between oilseed rape and cereal cultivation was especially high after harvest due to the high N contents in oilseed rape's crop residues. However, annual N2O emissions of winter oilseed rape were still lower than predicted by the Stehfest and Bouwman model. Hence, the assignment of oilseed rape to the crop-type classes of cereals or other crops should be reconsidered

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

Integrated supply chain design for commodity chemicals production via woody biomass fast pyrolysis and upgrading

This study investigates the optimal supply chain design for commodity chemicals (BTX, etc.) production via woody biomass fast pyrolysis and hydroprocessing pathway. The locations and capacities of distributed preprocessing hubs and integrated biorefinery facilities are optimized with a mixed integer linear programming model. In this integrated supply chain system, decisions on the biomass chipping methods (roadside chipping vs. facility chipping) are also explored. The economic objective of the supply chain model is to maximize the profit for a 20-year chemicals production system. In addition to the economic objective, the model also incorporates an environmental objective of minimizing life cycle greenhouse gas emissions, analyzing the trade-off between the economic and environmental considerations. The capital cost, operating cost, and revenues for the biorefinery facilities are based on techno-economic analysis, and the proposed approach is illustrated through a case study of Minnesota, with Minneapolis-St. Paul serving as the chemicals distribution hub

A Prospective Comparison of Younger and Older Patients' Preferences for Adjuvant Chemotherapy and Hormonal Therapy in Early Breast Cancer

AbstractBackgroundIt is unknown what minimal benefit in disease-free survival older patients with breast cancer require from adjuvant systemic therapy, and if this differs from that required by younger patients. We prospectively examined patients' preferences for adjuvant chemotherapy (aCT) and adjuvant hormonal therapy (aHT), factors related to minimally-required benefit, and patients' self-reported motivations.Patients and MethodsFifty-two younger (40-64 years) and 29 older (≥ 65 years) women with a first primary, invasive tumor were interviewed post-surgery, prior to receiving aCT/aHT recommendation.ResultsThe proportions of younger versus older participants who would accept, refuse, or were undecided about therapy were 92% versus 62%, 4% versus 24%, and 4% versus 14% for aCT, and 92% versus 59%, 8% versus 17%, and 0% versus 24% for aHT. The proportion of older participants who would refuse rather than accept aCT was larger than that of younger participants (P = .005). No significant difference was found for aHT (P = .12). Younger and older participants' minimally-required benefit, in terms of additional 10-year disease-free survival, to accept aCT (median, 5% vs. 4%; P = .13) or aHT (median, 10% vs. 8%; P = .15) did not differ. Being single/divorced/widowed (odds ratio [OR], 0.16; P = .005), presence of geriatric condition (inability to perform daily activities, incontinence, severe sensory impairment, depression, polypharmacy, difficulties with walking; OR, 0.27; P = .047), and having a preference to make the treatment decision either alone or after considering the clinician's opinion (active role; OR, 0.15; P = .012) were independently related to requiring larger benefits from aCT. The most frequent motivations for/against therapy included the wish to survive/avoid recurrence, clinician's recommendation, side effects, and treatment duration (only aHT).ConclusionWhereas older participants were less willing to accept aCT than younger participants, no significant difference was found for aHT. However, a majority of older participants would still accept both therapies. Adjuvant systemic therapy should be discussed with eligible patients regardless of age

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