The policy-making structure of European regulatory networks and the domestic adoption of standards

European regulatory networks (ERNs) constitute the main governance instrument for the informal co-ordination of public regulation at the European Union (EU) level. They are in charge of co-ordinating national regulators and ensuring the implementation of harmonized regulatory policies across the EU, while also offering sector-specific expertise to the Commission. To this aim, ERNs develop 'best practices' and benchmarking procedures in the form of standards, norms and guidelines to be adopted in member states. In this paper, we focus on the Committee of European Securities Regulators and examine the consequences of the policy-making structure of ERNs on the domestic adoption of standards. We find that the regulators of countries with larger financial industries tend to occupy more central positions in the network, especially among newer member states. In turn, network centrality is associated with a more prompt domestic adoption of standards.

The Politics of Network Governance in Europe: The Case of Energy Regulation

Networks are considered increasingly important for policy-making. The literature on new modes of governance in Europe suggests that their horizontal coordination capacity and flexible and informal structures are particularly suitable for governing the multilevel architecture of the European polity. However, empirical evidence about the effects of networks on policy-making and public policies is still quite limited. This article uses the case of the European network of energy regulators to explore the determinants of the position of network members and, in turn, the domestic adoption of soft rules developed within this network. The empirical analysis, based on multivariate statistics and semi-directive interviews, supports the expectation that institutional complementarities increase actors' centrality in networks, while arguments based on organisational resources and age are disproved. Furthermore, results show that the overall level of adoption is considerable and that centrality might have a small positive effect on domestic adoption.

Inference about the number of contributors to a DNA mixture: Comparative analyses of a Bayesian network approach and the maximum allele count method.

In the forensic examination of DNA mixtures, the question of how to set the total number of contributors (N) presents a topic of ongoing interest. Part of the discussion gravitates around issues of bias, in particular when assessments of the number of contributors are not made prior to considering the genotypic configuration of potential donors. Further complication may stem from the observation that, in some cases, there may be numbers of contributors that are incompatible with the set of alleles seen in the profile of a mixed crime stain, given the genotype of a potential contributor. In such situations, procedures that take a single and fixed number contributors as their output can lead to inferential impasses. Assessing the number of contributors within a probabilistic framework can help avoiding such complication. Using elements of decision theory, this paper analyses two strategies for inference on the number of contributors. One procedure is deterministic and focuses on the minimum number of contributors required to 'explain' an observed set of alleles. The other procedure is probabilistic using Bayes' theorem and provides a probability distribution for a set of numbers of contributors, based on the set of observed alleles as well as their respective rates of occurrence. The discussion concentrates on mixed stains of varying quality (i.e., different numbers of loci for which genotyping information is available). A so-called qualitative interpretation is pursued since quantitative information such as peak area and height data are not taken into account. The competing procedures are compared using a standard scoring rule that penalizes the degree of divergence between a given agreed value for N, that is the number of contributors, and the actual value taken by N. Using only modest assumptions and a discussion with reference to a casework example, this paper reports on analyses using simulation techniques and graphical models (i.e., Bayesian networks) to point out that setting the number of contributors to a mixed crime stain in probabilistic terms is, for the conditions assumed in this study, preferable to a decision policy that uses categoric assumptions about N.

Networking the unemployed : Can policy interventions facilitate access to employment through informal channels ?

It is widely known that informal contacts and networks constitute a major advantage when searching for a job. Unemployed people are likely to benefit from such informal contacts, but building and sustaining a network can be particularly difficult when out of employment. Interventions that allow unemployed people to effectively strengthen their networking capability could as a result be promising. Against this background, this article provides some hints in relation to the direction that such interventions could take. First, on the basis of data collected on a sample of 4,600 newly-unemployed people in the Swiss Canton of Vaud, it looks at the factors that influence jobseekers' decisions to turn to informal contacts for their job search. The article shows that many unemployed people are not making use of their network because they are unaware of the importance of this method. Second, it presents an impact analysis of an innovative intervention designed to raise awareness of the importance of networks which is tested in a randomized controlled trial setting.

Network governance and the domestic adoption of soft rules

European regulatory networks (ERNs) are in charge of producing and disseminating non-bindings standards, guidelines and recommendations in a number of important domains, such as banking and finance, electricity and gas, telecommunications, and competition regulation. The goal of these soft rules is to promote 'best practices', achieve co-ordination among regulatory authorities and ensure the consistent application of harmonized pro-competition rules across Europe. This contribution examines the domestic adoption of the soft rules developed within the four main ERNs. Different factors are expected to influence the process of domestic adoption: the resources of regulators; the existence of a review panel; and the interdependence of the issues at stake. The empirical analysis supports hypotheses about the relevance of network-level factors: monitoring and public reporting procedures increase the final level of adoption, while soft rules concerning highly interdependent policy areas are adopted earlier.

Uses of cancer registries for public health and clinical research in Europe : results of the European Network of Cancer Registries survey among 161 population-based cancer registries during 2010-2012

AIM: To provide insight into cancer registration coverage, data access and use in Europe. This contributes to data and infrastructure harmonisation and will foster a more prominent role of cancer registries (CRs) within public health, clinical policy and cancer research, whether within or outside the European Research Area. METHODS: During 2010-12 an extensive survey of cancer registration practices and data use was conducted among 161 population-based CRs across Europe. Responding registries (66%) operated in 33 countries, including 23 with national coverage. RESULTS: Population-based oncological surveillance started during the 1940-50s in the northwest of Europe and from the 1970s to 1990s in other regions. The European Union (EU) protection regulations affected data access, especially in Germany and France, but less in the Netherlands or Belgium. Regular reports were produced by CRs on incidence rates (95%), survival (60%) and stage for selected tumours (80%). Evaluation of cancer control and quality of care remained modest except in a few dedicated CRs. Variables evaluated were support of clinical audits, monitoring adherence to clinical guidelines, improvement of cancer care and evaluation of mass cancer screening. Evaluation of diagnostic imaging tools was only occasional. CONCLUSION: Most population-based CRs are well equipped for strengthening cancer surveillance across Europe. Data quality and intensity of use depend on the role the cancer registry plays in the politico, oncomedical and public health setting within the country. Standard registration methodology could therefore not be translated to equivalent advances in cancer prevention and mass screening, quality of care, translational research of prognosis and survivorship across Europe. Further European collaboration remains essential to ensure access to data and comparability of the results.

The local configuration of a science and innovation policy : a city into the nanoworld

Local trajectories and arrangements play a significant role because the development of a research field, such as nanoscience and nanotechnology, requires substantial investments in human and instrumental resources. But why are there often concentrated in a limited number of places? What dynamics lead to such concentration? The hypothesis is that there is an assemblage of heterogeneous resources through the action of local actors. The chapter will explore, from an Actor Network Theory (ANT) perspective, how the local emergence of research dynamics from: the revival of local traditions, the local and national action of institutional entrepreneurs, controversial dynamics, and researchers' arrangements to involve other actors. It will examine how they connect up with each other and mutually commit themselves to the development of new technologies. It will focus on the role of narratives in this assembling: how were the local narratives of the past mobilized and to what effect.

Trapping of naive lymphocytes triggers rapid growth and remodeling of the fibroblast network in reactive murine lymph nodes.

Adaptive immunity is initiated in T-cell zones of secondary lymphoid organs. These zones are organized in a rigid 3D network of fibroblastic reticular cells (FRCs) that are a rich cytokine source. In response to lymph-borne antigens, draining lymph nodes (LNs) expand several folds in size, but the fate and role of the FRC network during immune response is not fully understood. Here we show that T-cell responses are accompanied by the rapid activation and growth of FRCs, leading to an expanded but similarly organized network of T-zone FRCs that maintains its vital function for lymphocyte trafficking and survival. In addition, new FRC-rich environments were observed in the expanded medullary cords. FRCs are activated within hours after the onset of inflammation in the periphery. Surprisingly, FRC expansion depends mainly on trapping of naïve lymphocytes that is induced by both migratory and resident dendritic cells. Inflammatory signals are not required as homeostatic T-cell proliferation was sufficient to trigger FRC expansion. Activated lymphocytes are also dispensable for this process, but can enhance the later growth phase. Thus, this study documents the surprising plasticity as well as the complex regulation of FRC networks allowing the rapid LN hyperplasia that is critical for mounting efficient adaptive immunity.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.