Predicting Cell Cycle Regulated Genes by Causal Interactions

The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e. g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first.

A biofuel strategy for Ireland with an emphasis on production of biomethane and minimization of land-take

Increasing energy consumption has exerted great pressure on natural resources; this has led to a move towards sustainable energy resources to improve security of supply and to reduce greenhouse gas emissions. However, the rush to the cure may have been made in haste. Biofuels in particular, have a bad press both in terms of competition with good agricultural land for food, and also in terms of the associated energy balance with the whole life cycle analysis of the biofuel system. The emphasis is now very much on sustainable biofuel production; biofuels from wastes and lignocellulosic material are now seen as good sustainable biofuels that affect significantly better greenhouse gas balances as compared with first generation biofuels. Ireland has a significant resource of organic waste that could be a potential source of energy through anaerobic digestion. Ireland has 8% of the cattle population of the EU with less than 1% of the human population; as a result 91% of agricultural land in Ireland is under grass. Residues such as slurries and slaughter waste together with energy crops such as grass have an excellent potential to produce biogas that may be upgraded to biomethane. This biomethane may be used as a natural gas substitute; bio-compressed natural gas may then be an avenue for a biofuel strategy. It is estimated that a maximum potential of 33% of natural gas may be substituted by 2020 with a practical obtainable level of 7.5% estimated. Together with biodiesel from residues the practical obtainable level of this strategy may effect greater than a 5% substitution by energy of transport. The residues considered in this strategy to produce biofuel (excluding grass) have the potential to save 93,000 ha of agricultural land (23% of Irish arable land) when compared to a rapeseed biodiesel strategy. © 2009 Elsevier Ltd. All rights reserved.

Welfare risk assessment:The benefits and common pitfalls

Risk is defined as a situation involving exposure to danger. Risk assessment by nature characterises the probability of a negative event occurring and quantifies the consequences of such an event. Risk assessment is increasingly being used in the field of animal welfare as a means of drawing comparisons between multiple welfare problems within and between species and identifying those that should be prioritised by policy-makers, either because they affect a large proportion of the population or because they have particularly severe consequences for those affected. The assessment of risk is typically based on three fundamental factors: intensity of consequences, duration affected by consequences and prevalence. However, it has been recognised that these factors alone do not give a complete picture of a hazard and its associated consequences. Rather, to get a complete picture, it is important to also consider information about the hazard itself: probability of exposure to the hazard and duration of exposure to the hazard. The method has been applied to a variety of farmed species (eg poultry, dairy cows, farmed fish), investigating housing, husbandry and slaughter procedures, as well as companion animals, where it has been used to compare inherited defects in pedigree dogs and horses. To what extent can we trust current risk assessment methods to get the priorities straight? How should we interpret the results produced by such assessments? Here, the potential difficulties and pitfalls of the welfare risk assessment method will be discussed: (i) the assumption that welfare hazards are independent; (ii) the problem of quantifying the model parameters; and (iii) assessing and incorporating variability and uncertainty into welfare risk assessments. © 2012 Universities Federation for Animal Welfare.

Technological Learning in Developing Countries

Technological learning refers to the learning processes involved in improving the productive capabilities of an enterprise, sector or economy to enable it to produce higher quality goods or services with increasing levels of efficiency. Approaches to the study of technological learning include case studies of particular countries, sectors and firms; measures of export sophistication; and composite indicators of innovation and competitiveness. The present review draws on these approaches to provide an overview of the policies and practices that have been successful in different regions (East-Asia and Latin America) ; contexts (import substitution and liberalization) ; sectors (pulp and paper, IT services, electronics and passenger cars); and firms (Embrear and Lenovo). While it is clear that there is strong complementarity between domestic technological capability and the ability to absorb foreign technology, there is no simple policy recipe which is appropriate for all times, industries or places. Technological learning builds on and is shaped by what is already known. It requires time, space and resources all of which are influenced by the wider domestic and international context. The current international context is challenging but countries and firms have to find ways of moving forward despite the limited strategy space.

Cost performance of Irish credit unions

There are 424 credit unions in Ireland with assets under their control of €14.3bn and a membership of 2.5m which equates to about 66% of the economically active population, the highest penetration level of any country. That said, the Irish movement sits at a critical development stage, well behind mature markets such as Canada and the US in terms of product provision, technological sophistication, fragmentation of trade bodies and regulatory environment. This study analyses relative cost efficiency or performance of Irish credit unions using the popular frontier approach which measures an entity’s efficiency relative to a frontier of best practice. Parametric techniques are utilised, with variation in inefficiency being attributed to credit union-specific factors. The stochastic cost frontier parameters and the credit-union specific parameters are simultaneously estimated to produce valid statistical inferences. The study finds that the majority of Irish credit unions are not operating at optimal levels. It further highlights the factors which drive efficiency variation across credit unions and they include technological sophistication, ‘sponsor donated’ resources, interest rate differentials and the levels of bad debt written off