Vertical zonation of testate amoebae in the Elatia Mires, northern Greece : palaeoecological evidence for a wetland response to recent climate change or autogenic processes?

The Elatia Mires of northern Greece are unique ecosystems of high conservation value. The mires are climatically marginal and may be sensitive to changing hydroclimate, while northern Greece has experienced a significant increase in aridity since the late twentieth century. To investigate the impact of recent climatic change on the hydrology of the mires, the palaeoecological record was investigated from three near-surface monoliths extracted from two sites. Testate amoebae were analysed as sensitive indicators of hydrology. Results were interpreted using transfer function models to provide quantitative reconstructions of changing water table depth and pH. AMS radiocarbon dates and 210Pb suggest the peats were deposited within the last c. 50 years, but do not allow a secure chronology to be established. Results from all three profiles show a distinct shift towards a more xerophilic community particularly noted by increases in Euglypha species. Transfer function results infer a distinct lowering of water tables in this period. A hydrological response to recent climate change is a tenable hypothesis to explain this change; however other possible explanations include selective test decay, vertical zonation of living amoebae, ombrotrophication and local hydrological change. It is suggested that a peatland response to climatic change is the most probable hypothesis, showing the sensitivity of marginal peatlands to recent climatic change

Single clad coiled optical fibre for high power lasers and amplifiers

We demonstrate a new concept in development of high power fibre lasers based on single clad coiled fibres. Combination of a highly efficient Yb-doped fibre and glass-air waveguide for multi-point pump injection allows to reduce device length to less than 3

Multi-membership gene regulation in pathway based microarray analysis

This article is available through the Brunel Open Access Publishing Fund. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. Results: We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. Conclusions: We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.The work was sponsored by the studentship scheme of the School of Information Systems, Computing and Mathematics, Brunel Universit

Uncertainties in projecting climate-change impacts in marine ecosystems

Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet theyare inevitablyassociated withuncertainty.Identifying,quantifying,andcommunicatingthisuncertaintyis keytobothevaluatingtherisk associated with a projection and building confidence in its robustness. Wereview howuncertainties in such projections are handled in marine science. We employan approach developedin climatemodelling by breaking uncertainty down into(i) structural (model) uncertainty,(ii) initialization and internalvariabilityuncertainty,(iii)parametricuncertainty,and(iv)scenariouncertainty.Foreachuncertaintytype,wethenexaminethecurrent state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gainsontheseasonal-to-decadaltime-scale.Weconcludethatallpartsofmarinesciencecouldbenefitfromagreaterexchangeofideas,particularly concerningsuchauniversalproblemsuchasthetreatmentofuncertainty.Finally,marinescienceshouldstrivetoreachthepointwherescenario uncertainty is the dominant uncertainty in our projections

Simulation of decompressive craniectomy for ischaemic stroke treatment: A computational study

To evaluate the effectiveness of DC in treating brain tissue swelling using computational study based on capillary filtration and poroelastic theory

Simulation of decompressive craniectomy for ischaemic stroke treatment: A conceptual modeling study

Decompressive craniectomy is a treatment in which part of the skull is removed so as to reduce the intracranial pressure in the skull, especially during brain tissue swelling. Computational modeling studies may be used to understand the efficiency of this treatment in ischaemic stroke for advance clinical decision making. Thus, we performed a simulation using a mathematical model based on poroelastic theory and capillary filtration to see the effects of craniectomy in treating brain tissue swelling using 3D brain geometry. The results show that performing craniectomy can reduce intracranial pressure and reduce the effect of herniation. However, part of the brain is bulging out from the surgical hole and exerts a small amount of stress on the tissue by the surgical edge. This mathematical modeling framework can be used for further investigation of finding the suitable parameters for a decompressive craniectom

Multiscale Modelling of 3-Dimensional Brain Tissue Using Ideal Capillary Model

This project aims to investigate the effects of capillary size and shape toward the brain tissue poroelastic properties model using asymptotic expansion homogenization (AEH). Applying AEH to the existing poroelastic governing equations (GE) results in a new GE consists of 6 macroscale equations and 4 microscale cell problems. The cell problems are solved on a microstructure geometry of brain tissue with capillary embedded to obtain effective parametric tensors, namely the capillary and interstitial hydraulic conductivity (K and G ), capillary and interstitial homogenous Biot’s coefficient (αc and αt ), Young’s modulus (E) and Poisson’s ratio (v). By varying the tortuosity, the percentage difference of K is 97.98%, shows that it is highly affected by tortuosity. The percentage difference of G is 0.25% implying that tortuosity insignificantly affecting G. Meanwhile, αc and αt decreases and increases with tortuosity, respectively. The percentage difference of E and v are 0.14% and 0.03% respectively, implying that both parameters does not affected by tortuosity. Besides, K is exponentially increases with the increase of radius. On the other hand, G decreases as the radius increases. Meanwhile αc and αt increases and decreases, respectively as radius increases. The percentage differences of E and v are 18.26% and 14.55% respectively, suggesting that they are significantly affected by the radius. In conclusion, capillary shape and size have significant impact on the simulation of human brain. Thus, both characteristics should be precisely emphasized in the development of the geometry so that accurate parameters can be obtained to solve macroscale equations in future

Risk measures for direct real estate investments with non-normal or unknown return distributions

The volatility of returns is probably the most widely used risk measure for real estate. This is rather surprising since a number of studies have cast doubts on the view that volatility can capture the manifold risks attached to properties and corresponds to the risk attitude of investors. A central issue in this discussion is the statistical properties of real estate returns—in contrast to neoclassical capital market theory they are mostly non-normal and often unknown, which render many statistical measures useless. Based on a literature review and an analysis of data from Germany we provide evidence that volatility alone is inappropriate for measuring the risk of direct real estate. We use a unique data sample by IPD, which includes the total returns of 939 properties across different usage types (56% office, 20% retail, 8% others and 16% residential properties) from 1996 to 2009, the German IPD Index, and the German Property Index. The analysis of the distributional characteristics shows that German real estate returns in this period were not normally distributed and that a logistic distribution would have been a better fit. This is in line with most of the current literature on this subject and leads to the question which indicators are more appropriate to measure real estate risks. We suggest that a combination of quantitative and qualitative risk measures more adequately captures real estate risks and conforms better with investor attitudes to risk. Furthermore, we present criteria for the purpose of risk classification

Structural and biochemical characterization of the exopolysaccharide deacetylase Agd3 required for Aspergillus fumigatus biofilm formation

The exopolysaccharide galactosaminogalactan (GAG) is an important virulence factor of the fungal pathogen Aspergillus fumigatus. Deletion of a gene encoding a putative deacetylase, Agd3, leads to defects in GAG deacetylation, biofilm formation, and virulence. Here, we show that Agd3 deacetylates GAG in a metal-dependent manner, and is the founding member of carbohydrate esterase family CE18. The active site is formed by four catalytic motifs that are essential for activity. The structure of Agd3 includes an elongated substrate-binding cleft formed by a carbohydrate binding module (CBM) that is the founding member of CBM family 87. Agd3 homologues are encoded in previously unidentified putative bacterial exopolysaccharide biosynthetic operons and in other fungal genomes. The exopolysaccharide galactosaminogalactan (GAG) is an important virulence factor of the fungal pathogen Aspergillus fumigatus. Here, the authors study an A. fumigatus enzyme that deacetylates GAG in a metal-dependent manner and constitutes a founding member of a new carbohydrate esterase family.Bio-organic Synthesi

Lung adenocarcinoma originates from retrovirus infection of proliferating type 2 pneumocytes during pulmonary post-natal development or tissue repair

Jaagsiekte sheep retrovirus (JSRV) is a unique oncogenic virus with distinctive biological properties. JSRV is the only virus causing a naturally occurring lung cancer (ovine pulmonary adenocarcinoma, OPA) and possessing a major structural protein that functions as a dominant oncoprotein. Lung cancer is the major cause of death among cancer patients. OPA can be an extremely useful animal model in order to identify the cells originating lung adenocarcinoma and to study the early events of pulmonary carcinogenesis. In this study, we demonstrated that lung adenocarcinoma in sheep originates from infection and transformation of proliferating type 2 pneumocytes (termed here lung alveolar proliferating cells, LAPCs). We excluded that OPA originates from a bronchioalveolar stem cell, or from mature post-mitotic type 2 pneumocytes or from either proliferating or non-proliferating Clara cells. We show that young animals possess abundant LAPCs and are highly susceptible to JSRV infection and transformation. On the contrary, healthy adult sheep, which are normally resistant to experimental OPA induction, exhibit a relatively low number of LAPCs and are resistant to JSRV infection of the respiratory epithelium. Importantly, induction of lung injury increased dramatically the number of LAPCs in adult sheep and rendered these animals fully susceptible to JSRV infection and transformation. Furthermore, we show that JSRV preferentially infects actively dividing cell in vitro. Overall, our study provides unique insights into pulmonary biology and carcinogenesis and suggests that JSRV and its host have reached an evolutionary equilibrium in which productive infection (and transformation) can occur only in cells that are scarce for most of the lifespan of the sheep. Our data also indicate that, at least in this model, inflammation can predispose to retroviral infection and cancer