Traps of multi-level governance. Lessons from the implementation of the Water Framework Directive in Italy

During recent decades, different patterns of multi-level governance (MLG) have spread across Europe as a consequence of Europeanisation of public policies, which have increasingly adopted decentralized and participatory procedures conceived as a tool of more effective and accountable policy-making. It appears, however, that the implementation of operational designs based on MLG may be rather problematic and it does not necessarily bring to the expected performance improvements. Referring to the case of the EU Water Framework Directive (2000/60/EC), which conceives the creation of new multi-level institutional settings as a key tool for enacting a new holistic approach to water management and protection, this article explores the difficulties that the implementation of such settings has brought in Italy, despite some favorable pre-conditions existing in the country. Evidence is provided that along with institutional and agency variables, the implementation effectiveness of MLG arrangements promoted by the EU can be challenged by their inherent characteristics

Biodiversity-productivity relationships are key to nature-based climate solutions

The global impacts of biodiversity loss and climate change are interlinked, but the feedbacks between them are rarely assessed. Areas with greater tree diversity tend to be more productive, providing a greater carbon sink, and biodiversity loss could reduce these natural carbon sinks. Here, we quantify how tree and shrub species richness could affect biomass production on biome, national and regional scales. We find that GHG mitigation could help maintain tree diversity and thereby avoid a 9–39% reduction in terrestrial primary productivity across different biomes, which could otherwise occur over the next 50 years. Countries that will incur the greatest economic damages from climate change stand to benefit the most from conservation of tree diversity and primary productivity, which contribute to climate change mitigation. Our results emphasize an opportunity for a triple win for climate, biodiversity and society, and highlight that these co-benefits should be the focus of reforestation programmes

Enhancement and suppression effects resulting from information structuring in sentences

Information structuring through the use of cleft sentences increases the processing efficiency of references to elements within the scope of focus. Furthermore, there is evidence that putting certain types of emphasis on individual words not only enhances their subsequent processing, but also protects these words from becoming suppressed in the wake of subsequent information, suggesting mechanisms of enhancement and suppression. In Experiment 1, we showed that clefted constructions facilitate the integration of subsequent sentences that make reference to elements within the scope of focus, and that they decrease the efficiency with reference to elements outside of the scope of focus. In Experiment 2, using an auditory text-change-detection paradigm, we showed that focus has similar effects on the strength of memory representations. These results add to the evidence for enhancement and suppression as mechanisms of sentence processing and clarify that the effects occur within sentences having a marked focus structure

Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'

<p>Abstract</p> <p>Background</p> <p>Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks.</p> <p>Results</p> <p>We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition) and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property.</p> <p>Conclusions</p> <p>The package 'ddepn' is freely available on R-Forge and CRAN <url>http://ddepn.r-forge.r-project.org</url>, <url>http://cran.r-project.org</url>. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.</p

Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data

We present a Bayesian approach to the problem of determining parameters for coalescing binary systems observed with laser interferometric detectors. By applying a Markov Chain Monte Carlo (MCMC) algorithm, specifically the Gibbs sampler, we demonstrate the potential that MCMC techniques may hold for the computation of posterior distributions of parameters of the binary system that created the gravity radiation signal. We describe the use of the Gibbs sampler method, and present examples whereby signals are detected and analyzed from within noisy data.Comment: 21 pages, 10 figure