Special study: Legal transition programme review

This study is an evaluation of the European Bank for Reconstruction and Development's Legal Transition Programme’s activities from 2001-2011, through a review of a sample of 30 legal reform projects and advisory projects in Armenia, Hungary, Mongolia, Russia and Serbia. It was conducted by the Evaluation department in conjunction with three external experts: Professor Douglas Arner (University of Hong Kong), Professor Charles Booth (University of Hawaii) and Professor Gordon Walker (LaTrobe University). Overall the programme was found to be successful due to its compatibility with the Bank’s activities and highly relevant due to its support of the Bank’s investments through contributions to legal improvements. The programme’s projects have made a core contribution to the transition process, influencing domestic policy formulation and contributing to stronger free market economies. The transition impact and sustainability of the programme was found to be excellent.published_or_final_versio

A practical approach for applying Bayesian logic to determine the probabilities of subsurface scenarios: example from an offshore oilfield

During appraisal of an undeveloped segment of a producing offshore oilfield, three well penetrations revealed unexpected complexity and compartmentalization. Business decisions on whether and how to develop this segment depended on understanding the possible interpretations of the subsurface. This was achieved using the following steps that incorporated a novel practical application of Bayesian logic. 1. Scenarios were identified to span the full range of possible subsurface interpretations. This was achieved through a facilitated cross-disciplinary exercise including external participants. The exercise generated 12 widely differing subsurface scenarios, which could be grouped into 4 types of mechanisms: slumping, structural, depositional, and diagenetic. 2. Prior probabilities were assigned to each scenario. These probabilities were elicited from the same subsurface team and external experts who performed step 1, using their diverse knowledge and experience. 3. The probabilities of each scenario were updated by evaluating them sequentially with 21 individual pieces of evidence, progressively down-weighting belief in scenarios that were inconsistent with the evidence. For each piece of evidence, the likelihood (chance that the scenario could produce the evidence) was estimated qualitatively by the same team using a “traffic-light” high-medium-low assessment. Offline, these were converted to numerical likelihood values. Posterior probabilities were derived by multiplying the priors by the likelihoods and renormalizing to sum to unity across all of the scenarios. 4. The most probable scenarios were selected for quantitative reservoir modeling, to evaluate the potential outcomes of business decisions, given each scenario. Of the 12 scenarios identified in step 1, most were strongly down-weighted by the sequential revisions against evidence in step 3; after this, only scenarios in the “slumping” group retained significant posterior probabilities. The data showed minimal sensitivity to the initial assumption of prior probability in step 2. This process had several benefits. First, it encouraged the subsurface team to imagine a full range of scenarios that were likely to bracket the actual subsurface “truth,” something that is critical for subsequent decision-making. Second, it allowed belief in the probability of each scenario to be updated systematically in a way that was strongly conditioned to the evidence, so that the choice of scenarios to take through to reservoir modeling was more objective and evidence-based. Third, it allowed an assessment of the usefulness of individual pieces of evidence, which could be used to guide value-of-information assessments for subsequent data acquisition. Finally, the process enabled rigorous Bayesian revision methods to be applied in a simple practical way that engaged the subsurface team without exposing them to the underlying mathematics. During field appraisal and development, when the subsurface is revealed gradually as more data are acquired and studied, the process outlined here provides a practical way of generating and modifying belief in a range of subsurface scenarios while minimizing exposure to potential biases and logical fallacies that could affect subsequent decision quality. It also helps to decide which scenarios are sufficiently probable that they need to be represented by detailed reservoir models

Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances

This article is a review of the use, by regulatory agencies and authorities, of quantitative structure–activity relationships (QSARs) to predict ecologic effects and environmental fate of chemicals. For many years, the U.S. Environmental Protection Agency has been the most prominent regulatory agency using QSARs to predict the ecologic effects and environmental fate of chemicals. However, as increasing numbers of standard QSAR methods are developed and validated to predict ecologic effects and environmental fate of chemicals, it is anticipated that more regulatory agencies and authorities will find them to be acceptable alternatives to chemical testing

Evidence for variation in the effective population size of animal mitochondrial DNA

Background: It has recently been shown that levels of diversity in mitochondrial DNA are remarkably constant across animals of diverse census population sizes and ecologies, which has led to the suggestion that the effective population of mitochondrial DNA may be relatively constant. Results: Here we present several lines of evidence that suggest, to the contrary, that the effective population size of mtDNA does vary, and that the variation can be substantial. First, we show that levels of mitochondrial and nuclear diversity are correlated within all groups of animals we surveyed. Second, we show that the effectiveness of selection on non-synonymous mutations, as measured by the ratio of the numbers of non-synonymous and synonymous polymorphisms, is negatively correlated to levels of mitochondrial diversity. Finally, we estimate the effective population size of mitochondrial DNA in selected mammalian groups and show that it varies by at least an order of magnitude. Conclusions: We conclude that there is variation in the effective population size of mitochondria. Furthermore we suggest that the relative constancy of DNA diversity may be due to a negative correlation between the effective population size and the mutation rate per generation

CO(2) sensitivity of Southern Ocean phytoplankton

The Southern Ocean exerts a strong impact on marine biogeochemical cycles and global air-sea CO(2) fluxes. Over the coming century, large increases in surface ocean CO(2) levels, combined with increased upper water column temperatures and stratification, are expected to diminish Southern Ocean CO(2) uptake. These effects could be significantly modulated by concomitant CO(2)-dependent changes in the region\u27s biological carbon pump. Here we show that CO(2) concentrations affect the physiology, growth and species composition of phytoplankton assemblages in the Ross Sea, Antarctica. Field results from in situ sampling and ship-board incubation experiments demonstrate that inorganic carbon uptake, steady-state productivity and diatom species composition are sensitive to CO(2) concentrations ranging from 100 to 800 ppm. Elevated CO(2) led to a measurable increase in phytoplankton productivity, promoting the growth of larger chain-forming diatoms. Our results suggest that CO(2) concentrations can influence biological carbon cycling in the Southern Ocean, thereby creating potential climate feedbacks

Cognitive and environmental interventions to encourage healthy eating: evidence-based recommendations for public health policy

This is the final version. Available on open access from the Royal Society via the DOI in this recordPolicymakers are focused on reducing the public health burden of obesity. The UK average percentage of adults classified as obese is 26%, which is double that of the global average. Over a third of UK adults report using at least one weight management aid. Yet, many people still struggle to change their diet-related behaviour, despite having the awareness, intention and capability to do so. This 'intention-behaviour gap' may be because most existing dietary-choice interventions focus on individual decision-making, ignoring the effects of environmental cues on human behaviour. Behaviour change interventions that 'nudge' people into making healthier choices by modifying the food environment have been shown to be effective. However, this type of intervention is typically challenging for policymakers to implement for economic, ethical and public accessibility reasons. To overcome these concerns, policymakers should consider 'boosting' interventions. Boosting involves enhancing competences that help people make decisions consistent with their goals. Here, we outline cognitive training as a boosting intervention to tackle obesity. We synthesize the evidence for one type of cognitive training (go/no-go training) that may be effective at modifying food-related decisions and reducing body weight. We offer evidence-based recommendations for an obesity-focused Public Health Wales behaviour change programme.European Research Council (ERC

Practical approaches to Bayesian sample size determination in non-inferiority trials with binary outcomes.

Bayesian analysis of a non-inferiority trial is advantageous in allowing direct probability statements to be made about the relative treatment difference rather than relying on an arbitrary and often poorly justified non-inferiority margin. When the primary analysis will be Bayesian, a Bayesian approach to sample size determination will often be appropriate for consistency with the analysis. We demonstrate three Bayesian approaches to choosing sample size for non-inferiority trials with binary outcomes and review their advantages and disadvantages. First, we present a predictive power approach for determining sample size using the probability that the trial will produce a convincing result in the final analysis. Next, we determine sample size by considering the expected posterior probability of non-inferiority in the trial. Finally, we demonstrate a precision-based approach. We apply these methods to a non-inferiority trial in antiretroviral therapy for treatment of HIV-infected children. A predictive power approach would be most accessible in practical settings, because it is analogous to the standard frequentist approach. Sample sizes are larger than with frequentist calculations unless an informative analysis prior is specified, because appropriate allowance is made for uncertainty in the assumed design parameters, ignored in frequentist calculations. An expected posterior probability approach will lead to a smaller sample size and is appropriate when the focus is on estimating posterior probability rather than on testing. A precision-based approach would be useful when sample size is restricted by limits on recruitment or costs, but it would be difficult to decide on sample size using this approach alone

Stiffness Imaging With a Continuum Appendage: Real-Time Shape and Tip Force Estimation From Base Load Readings

In this paper, we propose benefiting from load readings at the base of a continuum appendage for real-time forward integration of Cosserat rod model with application in configuration and tip load estimation. The application of this method is successfully tested for stiffness imaging of a soft tissue, using a 3-DOF hydraulically actuated braided continuum appendage. Multiple probing runs with different actuation pressures are used for mapping the tissue surface shape and directional linear stiffness, as well as detecting non-homogeneous regions, e.g. a hard nodule embedded in a soft silicon tissue phantom. Readings from a 6-axis force sensor at the tip is used for comparison and verification. As a result, the tip force is estimated with 0.016-0.037 N (7-20%) mean error in the probing and 0.02-0.1 N (6-12%) in the indentation direction, 0.17 mm (14%) mean error is achieved in estimating the surface profile, and 3.4-15 [N/m] (10-16%) mean error is observed in evaluating tissue directional stiffness, depending on the appendage actuation. We observed that if the appendage bends against the slider motion (toward the probing direction), it provides better horizontal stiffness estimation and better estimation in the perpendicular direction is achieved when it bends toward the slider motion (against the probing direction). In comparison with a rigid probe, ≈ 10 times smaller stiffness and ≈ 7 times larger mean standard deviation values were observed, suggesting the importance of a probe stiffness in estimation the tissue stiffness

Statistical Inference of Selection and Divergence from a Time-Dependent Poisson Random Field Model

We apply a recently developed time-dependent Poisson random field model to aligned DNA sequences from two related biological species to estimate selection coefficients and divergence time. We use Markov chain Monte Carlo methods to estimate species divergence time and selection coefficients for each locus. The model assumes that the selective effects of non-synonymous mutations are normally distributed across genetic loci but constant within loci, and synonymous mutations are selectively neutral. In contrast with previous models, we do not assume that the individual species are at population equilibrium after divergence. Using a data set of 91 genes in two Drosophila species, D. melanogaster and D. simulans, we estimate the species divergence time (or 1.68 million years, assuming the haploid effective population size years) and a mean selection coefficient per generation . Although the average selection coefficient is positive, the magnitude of the selection is quite small. Results from numerical simulations are also presented as an accuracy check for the time-dependent model