Bogeymen, lunatics and fanatics: collective actions and the private enforcement of European competition law

The European desire to ensure that bearers of EU rights are adequately compensated for any infringement of these rights, particularly in cases where the harm is widely diffused, and perhaps not even noticed by those affected by it, collides with another desire: to avoid the perceived excesses of an American-style system of class actions. The excesses of these American class actions are in European discourse presented as a sort of bogeyman, which is a source of irrational fear, often presented by parental or other authority figures. But when looked at critically, the bogeyman disappears. In this paper, I examine the European (and UK) proposals for collective action. I compare them to the American regime. The flaws and purported excesses of the American regime, I argue, are exaggerated. A close, objective examination of the American regime shows this. I conclude that it is not the mythical bogeyman of a US class action that is the barrier to effective collective redress; rather, the barriers to effective, wide-ranging group actions lie within European legal culture and traditions, particularly those mandating individual control over litigation.

Inter-Agency Evidence Sharing in Competition Law Enforcement

While transnational antitrust enforcement is becoming only more common, the access to foreign-based evidence remains a considerable practical challenge. This article appraises considerations and concerns surrounding confidentiality, and looks into ways of their possible accommodation. It further identifies and critically evaluates the existing mechanisms allowing for inter-agency confidential information/ evidence sharing in competition law enforcement. The article outlines the shortcomings of the current framework and points to novel unilateral approaches. In the latter regard the focus is devoted to Australia, where the competition agency is empowered to share confidential information with foreign counterparts, also without any underlying bilateral agreement and on a non-reciprocal basis. This solution shows that a pragmatic and workable approach to inter-agency evidence sharing can be achieved.

Localized Flux Maxima of Arsenic, Lead, and Iron around Root Apices in Flooded Lowland Rice

In wetland-adapted plants, such as rice, it is typically root apexes, sites of rapid entry for water/nutrients, where radial oxygen losses (ROLs) are highest. Nutrient/toxic metal uptake therefore largely occurs through oxidized zones and pH microgradients. However, the processes controlling the acquisition of trace elements in rice have been difficult to explore experimentally because of a lack of techniques for simultaneously measuring labile trace elements and O2/pH. Here, we use new diffusive gradients in thin films (DGT)/planar optode sandwich sensors deployed in situ on rice roots to demonstrate a new geochemical niche of greatly enhanced As, Pb, and Fe(II) mobilization into solution immediately adjacent to the root tips characterized by O2 enrichment and low pH. Fe(II) mobilization was congruent to that of the peripheral edge of the aerobic root zone, demonstrating that the Fe(II) mobilization maximum only developed in a narrow O2 range as the oxidation front penetrates the reducing soil. The Fe flux to the DGT resin at the root apexes was 3-fold higher than the anaerobic bulk soil and 27 times greater than the aerobic rooting zone. These results provide new evidence for the importance of coupled diffusion and oxidation of Fe in modulating trace metal solubilization, dispersion, and plant uptake.

Synergy Between an Improved Covariate Unit Root Test and Cross-sectionally Dependent Panel Data Unit Root Tests, with two Applications

This paper proposes the use of an improved covariate unit root test which exploits the cross-sectional dependence information when the panel data null hypothesis of a unit root is rejected. More explicitly, to increase the power of the test, we suggest the utilization of more than one covariate and offer several ways to select the ‘best’ covariates from the set of potential covariates represented by the individuals in the panel. Employing our methods, we investigate the Prebish-Singer hypothesis for nine commodity prices. Our results show that this hypothesis holds for all but the price of petroleum.

Determinants of root-associated fungal communities within Asteraceae in a semi-arid grassland

Summary

1.While plant–fungal interactions are important determinants of plant community assembly and ecosystem functioning, the processes underlying fungal community composition are poorly understood.
2.Here, we studied for the first time the root-associated eumycotan communities in a set of co-occurring plant species of varying relatedness in a species-rich, semi-arid grassland in Germany. The study system provides an opportunity to evaluate the importance of host plants and gradients in soil type and landscape structure as drivers of fungal community structure on a relevant spatial scale. We used 454 pyrosequencing of the fungal internal transcribed spacer region to analyse root-associated eumycotan communities of 25 species within the Asteraceae, which were sampled at different locations within a soil type gradient. We partitioned the variance accounted for by three predictors (host plant phylogeny, spatial distribution and soil type) to quantify their relative roles in determining fungal community composition and used null model analyses to determine whether community composition was influenced by biotic interactions among the fungi.
3.We found a high fungal diversity (156 816 sequences clustered in 1100 operational taxonomic units (OTUs)). Most OTUs belonged to the phylum Ascomycota (35.8%); the most abundant phylotype best-matched Phialophora mustea. Basidiomycota were represented by 18.3%, with Sebacina as most abundant genus. The three predictors explained 30% of variation in the community structure of root-associated fungi, with host plant phylogeny being the most important variance component. Null model analysis suggested that many fungal taxa co-occurred less often than expected by chance, which demonstrates spatial segregation and indicates that negative interactions may prevail in the assembly of fungal communities.
4.Synthesis. The results show that the phylogenetic relationship of host plants is the most important predictor of root-associated fungal community assembly, indicating that fungal colonization of host plants might be facilitated by certain plant traits that may be shared among closely related plant species.

On convergence with hiccups: recent amendments to Poland's competition law

Discusses the amendments to the Polish Competition Act 2007 adopted in June 2014 which aim to enhance the effectiveness of antitrust enforcement, including the introduction of: (1) civil fines for individuals; (2) a "leniency plus" programme based on the US model; (3) a settlement procedure; and (4) extended inspection powers for the Competition Authority. Assesses the likely effectiveness of the reforms.

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.