sscMap: An extensible Java application for connecting small-molecule drugs using gene-expression signatures

Background
Connectivity mapping is a process to recognize novel pharmacological and toxicological properties in small molecules by comparing their gene expression signatures with others in a database. A simple and robust method for connectivity mapping with increased specificity and sensitivity was recently developed, and its utility demonstrated using experimentally derived gene signatures.

Results
This paper introduces sscMap (statistically significant connections' map), a Java application designed to undertake connectivity mapping tasks using the recently published method. The software is bundled with a default collection of reference gene-expression profiles based on the publicly available dataset from the Broad Institute Connectivity Map 02, which includes data from over 7000 Affymetrix microarrays, for over 1000 small-molecule compounds, and 6100 treatment instances in 5 human cell lines. In addition, the application allows users to add their custom collections of reference profiles and is applicable to a wide range of other 'omics technologies.

Conclusion
The utility of sscMap is two fold. First, it serves to make statistically significant connections between a user-supplied gene signature and the 6100 core reference profiles based on the Broad Institute expanded dataset. Second, it allows users to apply the same improved method to custom-built reference profiles which can be added to the database for future referencing. The software can be freely downloaded from http://purl.oclc.org/NET/sscMap

Are rural development programmes socially inclusive? Social inclusion, civic engagement, participation, and social capital: Exploring the differences

Considerable importance is attached to social exclusion/inclusion in recent EU rural development programmes. At the national/regional operation of these programmes groups of people who are not participating are often identified as ‘socially excluded groups’. This article contends that rural development programmes are misinterpreting the social processes of participation and consequently labelling some groups as socially excluded when they are not. This is partly because of the interchangeable and confused use of the concepts social inclusion, social capital and civic engagement, and partly because of the presumption that to participate is the default position. Three groups identified as socially excluded groups in Northern Ireland are considered. It is argued that a more careful analysis of what social inclusion means, what civic engagement means, and why participation is presumed to be the norm, leads to a different conclusion about who is excluded. This has both theoretical and policy relevance for the much used concept of social inclusion.

How the ship of fools was ship-wrecked - The Irish crisis and response

For a decade and half the Irish economy was the poster-boy of Europe. With substantial growth rates, an open economy, flexible labour markets and low levels of taxation, Ireland was seen as evidence of the success of neoliberal policies. Yet in the matter of a few short years Ireland has turned into a one of the peripheral black-holes (along with Greece and Portugal) that are threatening to bring down the whole Eurozone project. Given this context the paper will address two key questions. Firstly how did the much eulogised Celtic Tiger fall so far and so fast? And, secondly, what has been the government’s response to the fall and crash of the Irish economy? These two questions will be addressed through both a general historical analysis of the developments of Irish society up to the crash in 2008 and then the responses to it. Secondly by an analysis of two specific elements of that development; namely the much discussed low corporation tax rate and the failure of social housing to deliver decent affordable homes for those at the bottom of society. The third element is a review of the banking and sovereign debt crisis that led to the IMF/EU deal in November 2010 and a brief outlining of its implications for public finances, especially the question of default. The paper concludes by placing the Irish crisis in a global context.

Determination of the heat transfer coefficient at the metal–die interface for high pressure die cast AlSi9Cu3Fe

When simulating the High Pressure Die Casting ‘HPDC’ process, the heat transfer coefficient ‘HTC’ between the casting and the die is critical to accurately predict the quality of the casting. To determine the HTC at the metal–die interface a production die for an automotive engine bearing beam, Die 1, was instrumented with type K thermocouples. A Magmasoft® simulation model was generated with virtual thermocouple points placed in the same location as the production die. The temperature traces from the simulation model were compared to the instrumentation results. Using the default simulation HTC for the metal–die interface, a poor correlation was seen, with the temperature response being much less for the simulation model. Because of this, the HTC at the metal–die interface was modified in order to get a better fit. After many simulation iterations, a good fit was established using a peak HTC of 42,000 W/m2 K, this modified HTC was further validated by a second instrumented production die, proving that the modified HTC gives good correlation to the instrumentation trials. The updated HTC properties for the simulation model will improve the predictive capabilities of the casting simulation software and better predict casting defects.

Unfunded Pension Liabilities and the Corporate CDS Market

This article examines the impact of pension deficits on default risk as measured by the premia on corporate credit default swaps (CDS). We find highly significant evidence that unfunded pension liabilities raise one- and five-year CDS premia. However, this relation is not homogeneous across countries, with the U.S. CDS market leading its European counterparts in the pricing of defined-benefit pension risk.

Electric Vehicles - a well to wheel analysis

The European Union has set a target for 10% renewable energy in transport by 2020, which will be met using both biofuels and electric vehicles. In the case of biofuels, for the purposes of meeting the target, the biofuel must achieve greenhouse gas savings of 35% relative to the fossil fuel replaced. For biofuels, greenhouse gas savings can be calculated using life cycle analysis, or the European Union default values. In contrast, all electricity used in transport is considered to be the same, regardless of the source or the type of electric vehicle. However, the choice of the electric vehicle and electricity source will have a major impact on the greenhouse gas savings. This paper examines different electric-vehicle scenarios in terms of greenhouse gas savings, using a well-to-wheel life cycle analysis.

Force-induced activation of covalent bonds in mechanoresponsive polymeric materials

Mechanochemical transduction enables an extraordinary range of physiological processes such as the sense of touch, hearing, balance, muscle contraction, and the growth and remodelling of tissue and
bone1–6. Although biology is replete with materials systems that actively and functionally respond to mechanical stimuli, the default mechanochemical reaction of bulk polymers to large external stress is the unselective scission of covalent bonds, resulting in damage or failure7. An alternative to this degradation process is the rational molecular design of synthetic materials such that mechanical stress
favourably altersmaterial properties. A few mechanosensitive polymers with this property have been developed8–14; but their active response is mediated through non-covalent processes, which may
limit the extent to which properties can be modified and the longterm stability in structural materials. Previously, we have shown with dissolved polymer strands incorporating mechanically sensitive chemical groups—so-called mechanophores—that the directional nature of mechanical forces can selectively break and re-form covalent bonds15,16. We now demonstrate that such forceinduced covalent-bond activation can also be realized with mechanophore-linked elastomeric and glassy polymers, by using a mechanophore that changes colour as it undergoes a reversible electrocyclic ring-opening reaction under tensile stress and thus allows us to directly and locally visualize the mechanochemical reaction. We find that pronounced changes in colour and fluorescence emerge with the accumulation of plastic deformation, indicating that in these polymeric materials the transduction of mechanical force into the ring-opening reaction is an activated process. We anticipate that force activation of covalent bonds can serve as a general strategy for the development of new mechanophore building blocks that impart polymeric materials with desirable functionalities ranging from damage sensing to fully regenerative self-healing.

Neural correlates of task switching in paternal 15q11-q13 deletion Prader-Willi syndrome

We report a first study of brain activity linked to task switching in individuals with Prader-Willi syndrome (PWS) PWS individuals show a specific cognitive deficit in task switching which may be associated with the display of temper outbursts and repetitive questioning The performance of participants with PWS and typically developing controls was matched in a cued task switching procedure and brain activity was contrasted on switching and non switching blocks using SARI Individuals with PWS did not show the typical frontal-parietal pattern of neural activity associated with switching blocks, with significantly reduced activation in regions of the posterior parietal and ventromedial prefrontal cortices We suggest that this is linked to a difficulty in PWS in setting appropriate attentional weights to enable task set reconfiguration In addition to this, PWS individuals did not show the typical pattern of deactivation, with significantly less deactivation in an anterior region of the ventromedial prefrontal cortex One plausible explanation for this is that individuals with PWS show dysfunction within the default mode network which has been linked to attentional control The data point to functional changes in the neural circuitry supporting task switching in PWS even when behavioural performance is matched to controls and thus highlight neural mechanisms that may be involved in a specific pathway between genes cognition and behaviour (C) 2010 Elsevier B V All rights reserved

Causal knowledge and the development of inductive reasoning

We explored the development of sensitivity to causal relations in children’s inductive reasoning. Children (5-, 8-, and 12-year-olds) and adults were given trials in which they decided whether a property known to be possessed by members of one category was also possessed by members of (a) a taxonomically related category or (b) a causally related category. The direction of the causal link was either predictive (prey → predator) or diagnostic (predator → prey), and the property that participants reasoned about established either a taxonomic or causal context. There was a causal asymmetry effect across all age groups, with more causal choices when the causal link was predictive than when it was diagnostic. Furthermore, context-sensitive causal reasoning showed a curvilinear development, with causal choices being most frequent for 8-year-olds regardless of context. Causal inductions decreased thereafter because 12-year-olds and adults made more taxonomic choices when reasoning in the taxonomic context. These findings suggest that simple causal relations may often be the default knowledge structure in young children’s inductive reasoning, that sensitivity to causal direction is present early on, and that children over-generalize their causal knowledge when reasoning.

Optimisation of Kinetic Parameters for an After-treatment Catalyst

Mathematical modelling has become an essential tool in the design of modern catalytic systems. Emissions legislation is becoming increasingly stringent, and so mathematical models of aftertreatment systems must become more accurate in order to provide confidence that a catalyst will convert pollutants over the required range of conditions. 
Automotive catalytic converter models contain several sub-models that represent processes such as mass and heat transfer, and the rates at which the reactions proceed on the surface of the precious metal. Of these sub-models, the prediction of the surface reaction rates is by far the most challenging due to the complexity of the reaction system and the large number of gas species involved. The reaction rate sub-model uses global reaction kinetics to describe the surface reaction rate of the gas species and is based on the Langmuir Hinshelwood equation further developed by Voltz et al. [1] The reactions can be modelled using the pre-exponential and activation energies of the Arrhenius equations and the inhibition terms. 
The reaction kinetic parameters of aftertreatment models are found from experimental data, where a measured light-off curve is compared against a predicted curve produced by a mathematical model. The kinetic parameters are usually manually tuned to minimize the error between the measured and predicted data. This process is most commonly long, laborious and prone to misinterpretation due to the large number of parameters and the risk of multiple sets of parameters giving acceptable fits. Moreover, the number of coefficients increases greatly with the number of reactions. Therefore, with the growing number of reactions, the task of manually tuning the coefficients is becoming increasingly challenging. 
In the presented work, the authors have developed and implemented a multi-objective genetic algorithm to automatically optimize reaction parameters in AxiSuite®, [2] a commercial aftertreatment model. The genetic algorithm was developed and expanded from the code presented by Michalewicz et al. [3] and was linked to AxiSuite using the Simulink add-on for Matlab. 
The default kinetic values stored within the AxiSuite model were used to generate a series of light-off curves under rich conditions for a number of gas species, including CO, NO, C_{3H8 and C3H6. These light-off curves were used to generate an objective function.} 
This objective function was used to generate a measure of fit for the kinetic parameters. The multi-objective genetic algorithm was subsequently used to search between specified limits to attempt to match the objective function. In total the pre-exponential factors and activation energies of ten reactions were simultaneously optimized. 
The results reported here demonstrate that, given accurate experimental data, the optimization algorithm is successful and robust in defining the correct kinetic parameters of a global kinetic model describing aftertreatment processes.

Predicting the past: Understanding the causes of bank distress in the Netherlands in the 1920s

Why do some banks fail in financial crises while others survive? This article answers this question by analysing the effect of the Dutch financial crisis of the 1920s on 142 banks, of which 33 failed. We find that choices of balance sheet composition and product market strategy made in the lead-up to the crisis had a significant impact on banks’ subsequent chances of experiencing distress. We document that high-risk banks – those operating highly-leveraged portfolios and attracting large quantities of deposits – were more likely to fail. Branching and international activities also increased banks’ default probabilities. We measure the effects of board interlocks, which have been characterized in the extant literature as contributing to the Dutch crisis. We find that boards mattered: failing banks had smaller boards, shared directors with smaller and very profitable banks and had a lower concentration of interlocking directorates in non-financial firms.

A well-to-wheel analysis of electric vehicles in the all-island Single Electricity Market

The European Union has set a target for 10% renewable energy in transport by 2020 to be met using biofuels and electric vehicles. In the case of biofuels, the biofuel must achieve greenhouse gas savings of 35% relative to the fossil fuel replaced. For biofuels, greenhouse gas savings can be calculated using life cycle analysis or the European Union default values. In contrast, all electricity used in transport is considered to be the same, regardless of the source or the type of electric vehicle. However, the choice of the electric vehicle and electricity source will have a major impact on the greenhouse gas saving. In this paper the initial findings of a well-to-wheel analysis of electric vehicle deployment in Northern Ireland are presented. The key finding indicates that electric vehicles require least amount of energy per mile on a well-to-wheel basis, consume the fewest resources, even accommodating inefficient fuel production, in comparison to standard internal combustion engine and hybrid vehicles.

RMA with quantile normalization mixes biological signals between different sample groups in microarray data analysis

Quantile normalization (QN) is a technique for microarray data processing and is the default normalization method in the Robust Multi-array Average (RMA) procedure, which was primarily designed for analysing gene expression data from Affymetrix arrays. Given the abundance of Affymetrix microarrays and the popularity of the RMA method, it is crucially important that the normalization procedure is applied appropriately. In this study we carried out simulation experiments and also analysed real microarray data to investigate the suitability of RMA when it is applied to dataset with different groups of biological samples. From our experiments, we showed that RMA with QN does not preserve the biological signal included in each group, but rather it would mix the signals between the groups. We also showed that the Median Polish method in the summarization step of RMA has similar mixing effect. RMA is one of the most widely used methods in microarray data processing and has been applied to a vast volume of data in biomedical research. The problematic behaviour of this method suggests that previous studies employing RMA could have been misadvised or adversely affected. Therefore we think it is crucially important that the research community recognizes the issue and starts to address it. The two core elements of the RMA method, quantile normalization and Median Polish, both have the undesirable effects of mixing biological signals between different sample groups, which can be detrimental to drawing valid biological conclusions and to any subsequent analyses. Based on the evidence presented here and that in the literature, we recommend exercising caution when using RMA as a method of processing microarray gene expression data, particularly in situations where there are likely to be unknown subgroups of samples.