New methodology for assessing the probability of contaminating Mars

Methodology is proposed to assess the probability that the planet Mars will be contaminated by terrestrial microorganisms aboard a spacecraft. The present NASA methods are extended to permit utilization of detailed information on microbial characteristics, the lethality of release and transport mechanisms, and of other information about the Martian environment. Different types of microbial release are distinguished, and for each release mechanism a probability of growth is computed. Using this new methodology, an assessment was carried out for the 1975 Viking landings on Mars. The resulting probability of contamination for each Viking lander is 6 x 10 to the -6 power, and is amenable to revision as additional information becomes available

Assessment of the probability of contaminating Mars

New methodology is proposed to assess the probability that the planet Mars will by biologically contaminated by terrestrial microorganisms aboard a spacecraft. Present NASA methods are based on the Sagan-Coleman formula, which states that the probability of contamination is the product of the expected microbial release and a probability of growth. The proposed new methodology extends the Sagan-Coleman approach to permit utilization of detailed information on microbial characteristics, the lethality of release and transport mechanisms, and of other information about the Martian environment. Three different types of microbial release are distinguished in the model for assessing the probability of contamination. The number of viable microbes released by each mechanism depends on the bio-burden in various locations on the spacecraft and on whether the spacecraft landing is accomplished according to plan. For each of the three release mechanisms a probability of growth is computed, using a model for transport into an environment suited to microbial growth

From Anti-equilibrium to The Socialist System and Beyond

This essay attempts to understand János Kornai’s works from a political economy perspective. It argues that Kornai has significantly contributed to the formation of a new paradigm of political economy. The main endeavor of Kornai has been the combination of analytical concepts of economics with the empirical description of real economies. After a certain period of theoretical experimentation János Kornai formulated his research program that can be called the shortage economy explanation of the socialist system. The Economics of Shortage and The Socialist System have created a new theoretical paradigm in a framework in which it has become possible to establish a connection between the analytical and empirical, universal and historical aspects of the theory studying the socialist system as a real economic entity. János Kornai has built his analysis of the socialist system on the primary role of politics in the creation of economic institutions. In his present work on capitalism he has extended this thesis to the capitalist system. This seems to be an important contribution of his to a new political economy paradigm that is just in the process of formation

Tackling concentrated worklessness: integrating governance and policy across and within spatial scales

Spatial concentrations of worklessness remained a key characteristic of labour markets in advanced industrial economies, even during the period of decline in aggregate levels of unemployment and economic inactivity evident from the late 1990s to the economic downturn in 2008. The failure of certain localities to benefit from wider improvements in regional and national labour markets points to a lack of effectiveness in adopted policy approaches, not least in relation to the governance arrangements and policy delivery mechanisms that seek to integrate residents of deprived areas into wider local labour markets. Through analysis of practice in the British context, we explore the difficulties of integrating economic and social policy agendas within and across spatial scales to tackle problems of concentrated worklessness. We present analysis of a number of selected case studies aimed at reducing localised worklessness and identify the possibilities and constraints for effective action given existing governance arrangements and policy priorities to promote economic competitiveness and inclusion

CARBOTRAF: A decision Support system for reducing pollutant emissions by adaptive traffic management

Traffic congestion with frequent “stop & go” situations causes substantial pollutant emissions. Black carbon (BC) is a good indicator of combustion-related air pollution and results in negative health effects. Both BC and CO2 emissions are also known to contribute significantly to global warming. Current traffic control systems are designed to improve traffic flow and reduce congestion. The CARBOTRAF system combines real-time monitoring of traffic and air pollution with simulation models for emission and local air quality prediction in order to deliver on-line recommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimizing the traffic flows. The system is implemented and evaluated in two pilot cities, Graz and Glasgow. Model simulations link traffic states to emission and air quality levels. A chain of models combines micro-scale traffic simulations, traffic volumes, emission models and air quality simulations. This process is completed for several ITS scenarios and a range of traffic boundary conditions. The real-time DSS system uses all these model simulations to select optimal traffic and air quality scenarios. Traffic and BC concentrations are simultaneously monitored. In this paper the effects of ITS measures on air quality are analysed with a focus on BC

Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association

<p>Abstract</p> <p>Background</p> <p>Debate remains as to the optimal method for utilising genotype data obtained from multiple markers in case-control association studies. I and colleagues have previously described a method of association analysis using artificial neural networks (ANNs), whose performance compared favourably to single-marker methods. Here, the perfomance of ANN analysis is compared with other multi-marker methods, comprising different haplotype-based analyses and locus-based analyses.</p> <p>Results</p> <p>Of several methods studied and applied to simulated SNP datasets, heterogeneity testing of estimated haplotype frequencies using asymptotic <it>p </it>values rather than permutation testing had the lowest power of the methods studied and ANN analysis had the highest power. The difference in power to detect association between these two methods was statistically significant (<it>p </it>= 0.001) but other comparisons between methods were not significant. The raw <it>t </it>statistic obtained from ANN analysis correlated highly with the empirical statistical significance obtained from permutation testing of the ANN results and with the <it>p </it>value obtained from the heterogeneity test.</p> <p>Conclusion</p> <p>Although ANN analysis was more powerful than the standard haplotype-based test it is unlikely to be taken up widely. The permutation testing necessary to obtain a valid <it>p </it>value makes it slow to perform and it is not underpinned by a theoretical model relating marker genotypes to disease phenotype. Nevertheless, the superior performance of this method does imply that the widely-used haplotype-based methods for detecting association with multiple markers are not optimal and efforts could be made to improve upon them. The fact that the <it>t </it>statistic obtained from ANN analysis is highly correlated with the statistical significance does suggest a possibility to use ANN analysis in situations where large numbers of markers have been genotyped, since the <it>t</it> value could be used as a proxy for the <it>p </it>value in preliminary analyses.</p

Supporting Coaches to Learn Through and From Their Everyday Experiences: A 1:1 Coach Development Workflow for Performance Sport

This paper overviews an intensive 1:1 coach development workflow developed and used in U.K. performance and high-performance sport. The workflow has been field tested with over 60 coaches in mainly Olympic and Paralympic settings in a variety of sports. The workflow proposes six main stages: “beginning new relationships,” “seeking first to understand,” “preparing for reflective conversations,” “engaging in reflective conversations,” “working with difference,” and “supporting change.” The stages are tailored pragmatically to context, and the workflow does not suggest a fixed sequence. The application of the workflow requires adaptive expertise based on considerable coach development experience and a breadth and depth of coaching and coach development knowledge. The workflow suggests the need for coach developers to build and support trusting, collaborative, and supportive relationships with the coach, as a foundation for the coach development task. Coach development practices and the workflow are continually being developed and refined in a U.K. context, and future work will provide case studies, evidence of outcomes, and refinements to the work

Spinal cord stimulation for predominant low back pain in failed back surgery syndrome: study protocol for an international multicenter randomized controlled trial (PROMISE study)

Background: Although results of case series support the use of spinal cord stimulation in failed back surgery syndrome patients with predominant low back pain, no confirmatory randomized controlled trial has been undertaken in this patient group to date. PROMISE is a multicenter, prospective, randomized, open-label, parallel-group study designed to compare the clinical effectiveness of spinal cord stimulation plus optimal medical management with optimal medical management alone in patients with failed back surgery syndrome and predominant low back pain. Method/Design: Patients will be recruited in approximately 30 centers across Canada, Europe, and the United States. Eligible patients with low back pain exceeding leg pain and an average Numeric Pain Rating Scale score >= 5 for low back pain will be randomized 1:1 to spinal cord stimulation plus optimal medical management or to optimal medical management alone. The investigators will tailor individual optimal medical management treatment plans to their patients. Excluded from study treatments are intrathecal drug delivery, peripheral nerve stimulation, back surgery related to the original back pain complaint, and experimental therapies. Patients randomized to the spinal cord stimulation group will undergo trial stimulation, and if they achieve adequate low back pain relief a neurostimulation system using the Specify (R) 5-6-5 multi-column lead (Medtronic Inc., Minneapolis, MN, USA) will be implanted to capture low back pain preferentially in these patients. Outcome assessment will occur at baseline (pre-randomization) and at 1, 3, 6, 9, 12, 18, and 24 months post randomization. After the 6-month visit, patients can change treatment to that received by the other randomized group. The primary outcome is the proportion of patients with >= 50% reduction in low back pain at the 6-month visit. Additional outcomes include changes in low back and leg pain, functional disability, health-related quality of life, return to work, healthcare utilization including medication usage, and patient satisfaction. Data on adverse events will be collected. The primary analysis will follow the intention-to-treat principle. Healthcare use data will be used to assess costs and long-term cost-effectiveness. Discussion: Recruitment began in January 2013 and will continue until 2016

Air quality impact of a decision support system for reducing pollutant emissions: CARBOTRAF

Traffic congestion with frequent “stop & go” situations causes substantial pollutant emissions. Black carbon (BC) is a good indicator of combustion-related air pollution and results in negative health effects. Both BC and CO2 emissions are also known to contribute significantly to global warming. Current traffic control systems are designed to improve traffic flow and reduce congestion. The CARBOTRAF system combines real-time monitoring of traffic and air pollution with simulation models for emission and local air quality prediction in order to deliver on-line recommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimizing the traffic flows. The system is implemented and evaluated in two pilot cities, Graz and Glasgow. Model simulations link traffic states to emission and air quality levels. A chain of models combines micro-scale traffic simulations, traffic volumes, emission models and air quality simulations. This process is completed for several ITS scenarios and a range of traffic boundary conditions. The real-time DSS system uses these off-line model simulations to select optimal traffic and air quality scenarios. Traffic and BC concentrations are simultaneously monitored. In this paper the effects of ITS measures on air quality are analysed with a focus on BC