Policy Credibility and Delegation to Independent Regulatory Agencies: A Comparative Empirical Analysis

Independent regulatory agencies are one of the main institutional features of the 'rising regulatory state' in Western Europe. Governments are increasingly willing to abandon their regulatory competencies and to delegate them to specialized institutions that are at least partially beyond their control. This article examines the empirical consistency of one particular explanation of this phenomenon, namely the credibility hypothesis, claiming that governments delegate powers so as to enhance the credibility of their policies. Three observable implications are derived from the general hypothesis, linking credibility and delegation to veto players, complexity and interdependence. An independence index is developed to measure agency independence, which is then used in a multivariate analysis where the impact of credibility concerns on delegation is tested. The analysis relies on an original data set comprising independence scores for thirty-three regulators. Results show that the credibility hypothesis can explain a good deal of the variation in delegation. The economic nature of regulation is a strong determinant of agency independence, but is mediated by national institutions in the form of veto players.

Patterns of Subject Mix in Higher Education Institutions : A First Empirical Analysis Using the AQUAMETH Database

Teaching and research are organised differently between subject domains: attempts to construct typologies of higher education institutions, however, often do not include quantitative indicators concerning subject mix which would allow systematic comparisons of large numbers of higher education institutions among different countries, as the availability of data for such indicators is limited. In this paper, we present an exploratory approach for the construction of such indicators. The database constructed in the AQUAMETH project, which includes also data disaggregated at the disciplinary level, is explored with the aim of understanding patterns of subject mix. For six European countries, an exploratory and descriptive analysis of staff composition divided in four large domains (medical sciences, engineering and technology, natural sciences and social sciences and humanities) is performed, which leads to a classification distinguishing between specialist and generalist institutions. Among the latter, a further distinction is made based on the presence or absence of a medical department. Preliminary exploration of this classification and its comparison with other indicators show the influence of long term dynamics on the subject mix of individual higher education institutions, but also underline disciplinary differences, for example regarding student to staff ratios, as well as national patterns, for example regarding the number of PhD degrees per 100 undergraduate students. Despite its many limitations, this exploratory approach allows defining a classification of higher education institutions that accounts for a large share of differences between the analysed higher education institutions.

Risk assessment of recurrence in sporadic retinoblastoma using a molecular-based algorithm

Le rétinoblastome (Rb) est une tumeur provenant des cellules rétiniennes progénitrices des photorécepteurs. C'est la tumeur pédiatrique maligne la plus fréquente avec une incidence par naissance évaluée entre 1/15Ό00 et 1/20Ό00. Les enfants atteints de Rb sont diagnostiqué dans leur grande majorité avant l'âge de 4 ans, soit le temps nécessaire à la différentiation et à la maturation des photorécepteurs et donc à la disparition de la cellule d'origine du Rb. La survie du patient, la sauvegarde oculaire et le pronostic visuel restent excellents pour autant que le traitement ne soit pas différé. Dans sa variante non héréditaire (60%) le Rb est toujours unilatéral et sporadique. Le Rb héréditaire de transmission dominante autosomique (40%), se décline sous toutes les formes, familiale (10%) ou sporadique (30%), que l'atteinte soit unilatérale ou bilatérale. La majorité des mutations causales sont uniques et distribuées de façon aléatoire sur la totalité du gène RB1 sans région prédisposante. La détection de ces mutations est couteuse et chronophage, tout en présentant un taux de détection relativement bas; surtout dans les cas de Rb sporadiques unilatéraux. Dans le but d'identifier les patients présentant un risque réel de développer un Rb, et de réduire le nombre d'examens sous narcose requis pour le dépistage de la maladie chez les sujets à risque, nous avons développé une stratégie sensible, rapide, efficace et peu couteuse basée sur une analyse de l'haplotype intragénique. Cet algorithme prend en compte a) la perte d'hétérozygotie intratumorale du gène RB1, b) l'origine paternelle préférentielle des nouvelles mutations germinales et c) un risque a priori dérivé des données empiriques de Vogel. Pendant la période allant de janvier 1994 à décembre 2006, nous avons comparé l'apparition de nouveau Rb parmi la fratrie et la descendance de patient atteints au nombre de nouveaux cas attendus calculé par notre algorithme. 134 familles ont été étudiées. L'analyse moléculaire a été effectuée chez 570 personnes dont 99 patients âgés de moins de 4 ans et donc à risque de développer un Rb. Parmi cette cohorte, nous avons observé l'apparition d'un cas de Rb, alors que les risques cumulés a posteriori calculé par notre algorithme prédisait l'apparition de 1.77 nouveau cas. Dans cette étude, nous avons pu valider notre algorithme prédisant la récurrence de Rb chez les parents de 1er degré de patients atteints. Cet outil devrait grandement faciliter le conseil génétique ainsi que le suivi des patients à risque de développer un Rb, surtout dans les cas ou le séquençage direct du gène RB1 n'est pas disponible ou est resté non informatif. - Purpose: Most RBI mutations are unique and distributed throughout the RBI gene. Their detection can be time-consuming and the yield especially low in cases of conservatively-treated sporadic unilateral retinoblas-toma (Rb) patients. In order to identify patients with true risk of developing Rb, and to reduce the number of unnecessary examinations under anesthesia in all other cases, we developed a universal sensitive, efficient and cost-effective strategy based on intragenic haplotype analysis. Methods: This algorithm allows the calculation of the a posteriori risk of developing Rb and takes into account (a) RBI loss of heterozygosity in tumors, (b) preferential paternal origin of new germline mutations, (c) a priori risk derived from empirical data by Vogel, and (d) disease penetrance of 90% in most cases. We report the occurrence of Rb in first degree relatives of patients with sporadic Rb who visited the Jules Gonin Eye Hospital, Lausanne, Switzerland, from January 1994 to December 2006 compared to expected new cases of Rb using our algorithm. Results: A total of 134 families with sporadic Rb were enrolled; testing was performed in 570 individuals and 99 patients younger than 4 years old were identified. We observed one new case of Rb. Using our algorithm, the cumulated total a posteriori risk of recurrence was 1.77. Conclusions: This is the first time that linkage analysis has been validated to monitor the risk of recurrence in sporadic Rb. This should be a useful tool in genetic counseling, especially when direct RBI screening for mutations leaves a negative result or is unavailable.

Risk assessment of recurrence in sporadic retinoblastoma using a molecular-based algorithm.

PURPOSE: Most RB1 mutations are unique and distributed throughout the RB1 gene. Their detection can be time-consuming and the yield especially low in cases of conservatively-treated sporadic unilateral retinoblastoma (Rb) patients. In order to identify patients with true risk of developing Rb, and to reduce the number of unnecessary examinations under anesthesia in all other cases, we developed a universal sensitive, efficient and cost-effective strategy based on intragenic haplotype analysis. METHODS: This algorithm allows the calculation of the a posteriori risk of developing Rb and takes into account (a) RB1 loss of heterozygosity in tumors, (b) preferential paternal origin of new germline mutations, (c) a priori risk derived from empirical data by Vogel, and (d) disease penetrance of 90% in most cases. We report the occurrence of Rb in first degree relatives of patients with sporadic Rb who visited the Jules Gonin Eye Hospital, Lausanne, Switzerland, from January 1994 to December 2006 compared to expected new cases of Rb using our algorithm. RESULTS: A total of 134 families with sporadic Rb were enrolled; testing was performed in 570 individuals and 99 patients younger than 4 years old were identified. We observed one new case of Rb. Using our algorithm, the cumulated total a posteriori risk of recurrence was 1.77. CONCLUSIONS: This is the first time that linkage analysis has been validated to monitor the risk of recurrence in sporadic Rb. This should be a useful tool in genetic counseling, especially when direct RB1 screening for mutations leaves a negative result or is unavailable.

Design and analysis of ChIP-Seq experiments for nuclear factor I DNA-binding proteins

SUMMARY : Eukaryotic DNA interacts with the nuclear proteins using non-covalent ionic interactions. Proteins can recognize specific nucleotide sequences based on the sterical interactions with the DNA and these specific protein-DNA interactions are the basis for many nuclear processes, e.g. gene transcription, chromosomal replication, and recombination. New technology termed ChIP-Seq has been recently developed for the analysis of protein-DNA interactions on a whole genome scale and it is based on immunoprecipitation of chromatin and high-throughput DNA sequencing procedure. ChIP-Seq is a novel technique with a great potential to replace older techniques for mapping of protein-DNA interactions. In this thesis, we bring some new insights into the ChIP-Seq data analysis. First, we point out to some common and so far unknown artifacts of the method. Sequence tag distribution in the genome does not follow uniform distribution and we have found extreme hot-spots of tag accumulation over specific loci in the human and mouse genomes. These artifactual sequence tags accumulations will create false peaks in every ChIP-Seq dataset and we propose different filtering methods to reduce the number of false positives. Next, we propose random sampling as a powerful analytical tool in the ChIP-Seq data analysis that could be used to infer biological knowledge from the massive ChIP-Seq datasets. We created unbiased random sampling algorithm and we used this methodology to reveal some of the important biological properties of Nuclear Factor I DNA binding proteins. Finally, by analyzing the ChIP-Seq data in detail, we revealed that Nuclear Factor I transcription factors mainly act as activators of transcription, and that they are associated with specific chromatin modifications that are markers of open chromatin. We speculate that NFI factors only interact with the DNA wrapped around the nucleosome. We also found multiple loci that indicate possible chromatin barrier activity of NFI proteins, which could suggest the use of NFI binding sequences as chromatin insulators in biotechnology applications. RESUME : L'ADN des eucaryotes interagit avec les protéines nucléaires par des interactions noncovalentes ioniques. Les protéines peuvent reconnaître les séquences nucléotidiques spécifiques basées sur l'interaction stérique avec l'ADN, et des interactions spécifiques contrôlent de nombreux processus nucléaire, p.ex. transcription du gène, la réplication chromosomique, et la recombinaison. Une nouvelle technologie appelée ChIP-Seq a été récemment développée pour l'analyse des interactions protéine-ADN à l'échelle du génome entier et cette approche est basée sur l'immuno-précipitation de la chromatine et sur la procédure de séquençage de l'ADN à haut débit. La nouvelle approche ChIP-Seq a donc un fort potentiel pour remplacer les anciennes techniques de cartographie des interactions protéine-ADN. Dans cette thèse, nous apportons de nouvelles perspectives dans l'analyse des données ChIP-Seq. Tout d'abord, nous avons identifié des artefacts très communs associés à cette méthode qui étaient jusqu'à présent insoupçonnés. La distribution des séquences dans le génome ne suit pas une distribution uniforme et nous avons constaté des positions extrêmes d'accumulation de séquence à des régions spécifiques, des génomes humains et de la souris. Ces accumulations des séquences artéfactuelles créera de faux pics dans toutes les données ChIP-Seq, et nous proposons différentes méthodes de filtrage pour réduire le nombre de faux positifs. Ensuite, nous proposons un nouvel échantillonnage aléatoire comme un outil puissant d'analyse des données ChIP-Seq, ce qui pourraient augmenter l'acquisition de connaissances biologiques à partir des données ChIP-Seq. Nous avons créé un algorithme d'échantillonnage aléatoire et nous avons utilisé cette méthode pour révéler certaines des propriétés biologiques importantes de protéines liant à l'ADN nommés Facteur Nucléaire I (NFI). Enfin, en analysant en détail les données de ChIP-Seq pour la famille de facteurs de transcription nommés Facteur Nucléaire I, nous avons révélé que ces protéines agissent principalement comme des activateurs de transcription, et qu'elles sont associées à des modifications de la chromatine spécifiques qui sont des marqueurs de la chromatine ouverte. Nous pensons que lés facteurs NFI interagir uniquement avec l'ADN enroulé autour du nucléosome. Nous avons également constaté plusieurs régions génomiques qui indiquent une éventuelle activité de barrière chromatinienne des protéines NFI, ce qui pourrait suggérer l'utilisation de séquences de liaison NFI comme séquences isolatrices dans des applications de la biotechnologie.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

A modular approach for integrative analysis of large-scale gene-expression and drug-response data

High-throughput technologies are now used to generate more than one type of data from the same biological samples. To properly integrate such data, we propose using co-modules, which describe coherent patterns across paired data sets, and conceive several modular methods for their identification. We first test these methods using in silico data, demonstrating that the integrative scheme of our Ping-Pong Algorithm uncovers drug-gene associations more accurately when considering noisy or complex data. Second, we provide an extensive comparative study using the gene-expression and drug-response data from the NCI-60 cell lines. Using information from the DrugBank and the Connectivity Map databases we show that the Ping-Pong Algorithm predicts drug-gene associations significantly better than other methods. Co-modules provide insights into possible mechanisms of action for a wide range of drugs and suggest new targets for therapy

Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data.

Despite the central role of quantitative PCR (qPCR) in the quantification of mRNA transcripts, most analyses of qPCR data are still delegated to the software that comes with the qPCR apparatus. This is especially true for the handling of the fluorescence baseline. This article shows that baseline estimation errors are directly reflected in the observed PCR efficiency values and are thus propagated exponentially in the estimated starting concentrations as well as 'fold-difference' results. Because of the unknown origin and kinetics of the baseline fluorescence, the fluorescence values monitored in the initial cycles of the PCR reaction cannot be used to estimate a useful baseline value. An algorithm that estimates the baseline by reconstructing the log-linear phase downward from the early plateau phase of the PCR reaction was developed and shown to lead to very reproducible PCR efficiency values. PCR efficiency values were determined per sample by fitting a regression line to a subset of data points in the log-linear phase. The variability, as well as the bias, in qPCR results was significantly reduced when the mean of these PCR efficiencies per amplicon was used in the calculation of an estimate of the starting concentration per sample.

Policy Diffusion: An Agent-Based Approach

Methods like Event History Analysis can show the existence of diffusion and part of its nature, but do not study the process itself. Nowadays, thanks to the increasing performance of computers, processes can be studied using computational modeling. This thesis presents an agent-based model of policy diffusion mainly inspired from the model developed by Braun and Gilardi (2006). I first start by developing a theoretical framework of policy diffusion that presents the main internal drivers of policy diffusion - such as the preference for the policy, the effectiveness of the policy, the institutional constraints, and the ideology - and its main mechanisms, namely learning, competition, emulation, and coercion. Therefore diffusion, expressed by these interdependencies, is a complex process that needs to be studied with computational agent-based modeling. In a second step, computational agent-based modeling is defined along with its most significant concepts: complexity and emergence. Using computational agent-based modeling implies the development of an algorithm and its programming. When this latter has been developed, we let the different agents interact. Consequently, a phenomenon of diffusion, derived from learning, emerges, meaning that the choice made by an agent is conditional to that made by its neighbors. As a result, learning follows an inverted S-curve, which leads to partial convergence - global divergence and local convergence - that triggers the emergence of political clusters; i.e. the creation of regions with the same policy. Furthermore, the average effectiveness in this computational world tends to follow a J-shaped curve, meaning that not only time is needed for a policy to deploy its effects, but that it also takes time for a country to find the best-suited policy. To conclude, diffusion is an emergent phenomenon from complex interactions and its outcomes as ensued from my model are in line with the theoretical expectations and the empirical evidence.Les méthodes d'analyse de biographie (event history analysis) permettent de mettre en évidence l'existence de phénomènes de diffusion et de les décrire, mais ne permettent pas d'en étudier le processus. Les simulations informatiques, grâce aux performances croissantes des ordinateurs, rendent possible l'étude des processus en tant que tels. Cette thèse, basée sur le modèle théorique développé par Braun et Gilardi (2006), présente une simulation centrée sur les agents des phénomènes de diffusion des politiques. Le point de départ de ce travail met en lumière, au niveau théorique, les principaux facteurs de changement internes à un pays : la préférence pour une politique donnée, l'efficacité de cette dernière, les contraintes institutionnelles, l'idéologie, et les principaux mécanismes de diffusion que sont l'apprentissage, la compétition, l'émulation et la coercition. La diffusion, définie par l'interdépendance des différents acteurs, est un système complexe dont l'étude est rendue possible par les simulations centrées sur les agents. Au niveau méthodologique, nous présenterons également les principaux concepts sous-jacents aux simulations, notamment la complexité et l'émergence. De plus, l'utilisation de simulations informatiques implique le développement d'un algorithme et sa programmation. Cette dernière réalisée, les agents peuvent interagir, avec comme résultat l'émergence d'un phénomène de diffusion, dérivé de l'apprentissage, où le choix d'un agent dépend en grande partie de ceux faits par ses voisins. De plus, ce phénomène suit une courbe en S caractéristique, poussant à la création de régions politiquement identiques, mais divergentes au niveau globale. Enfin, l'efficacité moyenne, dans ce monde simulé, suit une courbe en J, ce qui signifie qu'il faut du temps, non seulement pour que la politique montre ses effets, mais également pour qu'un pays introduise la politique la plus efficace. En conclusion, la diffusion est un phénomène émergent résultant d'interactions complexes dont les résultats du processus tel que développé dans ce modèle correspondent tant aux attentes théoriques qu'aux résultats pratiques.

Modular analysis of gene expression data with R.

SUMMARY: Large sets of data, such as expression profiles from many samples, require analytic tools to reduce their complexity. The Iterative Signature Algorithm (ISA) is a biclustering algorithm. It was designed to decompose a large set of data into so-called 'modules'. In the context of gene expression data, these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different 'resolutions' of the modular mapping. In this short note, we introduce two BioConductor software packages written in GNU R: The isa2 package includes an optimized implementation of the ISA and the eisa package provides a convenient interface to run the ISA, visualize its output and put the biclusters into biological context. Potential users of these packages are all R and BioConductor users dealing with tabular (e.g. gene expression) data. AVAILABILITY: http://www.unil.ch/cbg/ISA CONTACT: sven.bergmann@unil.ch

The attractiveness of nations in global competition : an empirical assessment of the effects of country attractiveness on the success of strategy for hosting international sports events, 1990-2012

This dissertation aims to investigate empirical evidence on the importance and influence of attractiveness of nations in global competition. The notion of country attractiveness, which has been widely developed in the research areas of international business, tourism and migration, is a multi-dimensional construct to measure a country's characteristics with regard to its market or destination that attract international investors, tourists and migrants. This analytical concept provides an account of the mechanism as to how potential stakeholders evaluate more attractive countries based on certain criteria. Thus, in the field of international sport-event bidding, do international sport event owners also have specific country attractiveness for their sport event hosts? The dissertation attempts to address this research question by statistically assessing the effects of country attractiveness on the success of strategy for hosting international sports events. Based on theories of signaling and soft power, country attractiveness is defined and measured as the three dimensions of sustainable development: economic, social, and environmental attractiveness. This thesis proceeds to examine the concept of sport-event-hosting strategy and explore multi-level factors affecting the success in international sport-event bidding. By exploring past history of the Olympic Movement from theoretical perspectives, the thesis proposes and tests the hypotheses that economic, social and environmental attractiveness of a country may be correlated with its bid wins or the success of sport-event-hosting strategy. Quantitative analytical methods with various robustness checks are employed with using collected data on bidding results of major events in Olympic sports during the period from 1990 to 2012. The analysis results reveal that event owners of international Olympic sports are likely to prefer countries that have higher economic, social, and environmental attractiveness. The empirical assessment of this thesis suggests that high country attractiveness can be an essential element of prerequisites for a city/country to secure in order to bid with an increased chance of success.

Which European Public Sphere? Normative Standards and Empirical Insights from Multilingual Switzerland

Since the beginning of the 1990s, the EU has been increasingly criticised for its democratic deficit, which is intrinsically linked to the absence of a public sphere at the European level. Whereas scholars consider the emergence of such a public sphere as a necessary requirement for the democratisation of the EU, they disagree on the conceptualisation and normative requirements for a meaningful public sphere at the European level. This article takes an empirical perspective and draws on the nation-state context of multilingual Switzerland to get insights into what a European public sphere might realistically look like. Based on a content analysis of the leading quality paper from each German- and French-speaking Switzerland by means of political claims analysis, it shows that three of the most often cited criteria for a European public sphere - horizontal openness and interconnectedness, shared meaning structures, and inclusiveness - are hardly met in the Swiss context. On this basis, it concludes that the normative barrier for finding a European public sphere might be unrealistically high and should be reconsidered.