The potential of the European network of congenital anomaly registers (EUROCAT) for drug safety surveillance: a descriptive study.

BACKGROUND: European Surveillance of Congenital Anomalies (EUROCAT) is a network of population-based congenital anomaly registries in Europe surveying more than 1 million births per year, or 25% of the births in the European Union. This paper describes the potential of the EUROCAT collaboration for pharmacoepidemiology and drug safety surveillance. METHODS: The 34 full members and 6 associate members of the EUROCAT network were sent a questionnaire about their data sources on drug exposure and on drug coding. Available data on drug exposure during the first trimester available in the central EUROCAT database for the years 1996-2000 was summarised for 15 out of 25 responding full members. RESULTS: Of the 40 registries, 29 returned questionnaires (25 full and 4 associate members). Four of these registries do not collect data on maternal drug use. Of the full members, 15 registries use the EUROCAT drug code, 4 use the international ATC drug code, 3 registries use another coding system and 7 use a combination of these coding systems. Obstetric records are the most frequently used sources of drug information for the registries, followed by interviews with the mother. Only one registry uses pharmacy data. Percentages of cases with drug exposure (excluding vitamins/minerals) varied from 4.4% to 26.0% among different registries. The categories of drugs recorded varied widely between registries. CONCLUSIONS: Practices vary widely between registries regarding recording drug exposure information. EUROCAT has the potential to be an effective collaborative framework to contribute to post-marketing drug surveillance in relation to teratogenic effects, but work is needed to implement ATC drug coding more widely, and to diversify the sources of information used to determine drug exposure in each registry.

Evolutionary games on complex networks : the emergence and maintenance of cooperation

Abstract The object of game theory lies in the analysis of situations where different social actors have conflicting requirements and where their individual decisions will all influence the global outcome. In this framework, several games have been invented to capture the essence of various dilemmas encountered in many common important socio-economic situations. Even though these games often succeed in helping us understand human or animal behavior in interactive settings, some experiments have shown that people tend to cooperate with each other in situations for which classical game theory strongly recommends them to do the exact opposite. Several mechanisms have been invoked to try to explain the emergence of this unexpected cooperative attitude. Among them, repeated interaction, reputation, and belonging to a recognizable group have often been mentioned. However, the work of Nowak and May (1992) showed that the simple fact of arranging the players according to a spatial structure and only allowing them to interact with their immediate neighbors is sufficient to sustain a certain amount of cooperation even when the game is played anonymously and without repetition. Nowak and May's study and much of the following work was based on regular structures such as two-dimensional grids. Axelrod et al. (2002) showed that by randomizing the choice of neighbors, i.e. by actually giving up a strictly local geographical structure, cooperation can still emerge, provided that the interaction patterns remain stable in time. This is a first step towards a social network structure. However, following pioneering work by sociologists in the sixties such as that of Milgram (1967), in the last few years it has become apparent that many social and biological interaction networks, and even some technological networks, have particular, and partly unexpected, properties that set them apart from regular or random graphs. Among other things, they usually display broad degree distributions, and show small-world topological structure. Roughly speaking, a small-world graph is a network where any individual is relatively close, in terms of social ties, to any other individual, a property also found in random graphs but not in regular lattices. However, in contrast with random graphs, small-world networks also have a certain amount of local structure, as measured, for instance, by a quantity called the clustering coefficient. In the same vein, many real conflicting situations in economy and sociology are not well described neither by a fixed geographical position of the individuals in a regular lattice, nor by a random graph. Furthermore, it is a known fact that network structure can highly influence dynamical phenomena such as the way diseases spread across a population and ideas or information get transmitted. Therefore, in the last decade, research attention has naturally shifted from random and regular graphs towards better models of social interaction structures. The primary goal of this work is to discover whether or not the underlying graph structure of real social networks could give explanations as to why one finds higher levels of cooperation in populations of human beings or animals than what is prescribed by classical game theory. To meet this objective, I start by thoroughly studying a real scientific coauthorship network and showing how it differs from biological or technological networks using divers statistical measurements. Furthermore, I extract and describe its community structure taking into account the intensity of a collaboration. Finally, I investigate the temporal evolution of the network, from its inception to its state at the time of the study in 2006, suggesting also an effective view of it as opposed to a historical one. Thereafter, I combine evolutionary game theory with several network models along with the studied coauthorship network in order to highlight which specific network properties foster cooperation and shed some light on the various mechanisms responsible for the maintenance of this same cooperation. I point out the fact that, to resist defection, cooperators take advantage, whenever possible, of the degree-heterogeneity of social networks and their underlying community structure. Finally, I show that cooperation level and stability depend not only on the game played, but also on the evolutionary dynamic rules used and the individual payoff calculations. Synopsis Le but de la théorie des jeux réside dans l'analyse de situations dans lesquelles différents acteurs sociaux, avec des objectifs souvent conflictuels, doivent individuellement prendre des décisions qui influenceront toutes le résultat global. Dans ce cadre, plusieurs jeux ont été inventés afin de saisir l'essence de divers dilemmes rencontrés dans d'importantes situations socio-économiques. Bien que ces jeux nous permettent souvent de comprendre le comportement d'êtres humains ou d'animaux en interactions, des expériences ont montré que les individus ont parfois tendance à coopérer dans des situations pour lesquelles la théorie classique des jeux prescrit de faire le contraire. Plusieurs mécanismes ont été invoqués pour tenter d'expliquer l'émergence de ce comportement coopératif inattendu. Parmi ceux-ci, la répétition des interactions, la réputation ou encore l'appartenance à des groupes reconnaissables ont souvent été mentionnés. Toutefois, les travaux de Nowak et May (1992) ont montré que le simple fait de disposer les joueurs selon une structure spatiale en leur permettant d'interagir uniquement avec leurs voisins directs est suffisant pour maintenir un certain niveau de coopération même si le jeu est joué de manière anonyme et sans répétitions. L'étude de Nowak et May, ainsi qu'un nombre substantiel de travaux qui ont suivi, étaient basés sur des structures régulières telles que des grilles à deux dimensions. Axelrod et al. (2002) ont montré qu'en randomisant le choix des voisins, i.e. en abandonnant une localisation géographique stricte, la coopération peut malgré tout émerger, pour autant que les schémas d'interactions restent stables au cours du temps. Ceci est un premier pas en direction d'une structure de réseau social. Toutefois, suite aux travaux précurseurs de sociologues des années soixante, tels que ceux de Milgram (1967), il est devenu clair ces dernières années qu'une grande partie des réseaux d'interactions sociaux et biologiques, et même quelques réseaux technologiques, possèdent des propriétés particulières, et partiellement inattendues, qui les distinguent de graphes réguliers ou aléatoires. Entre autres, ils affichent en général une distribution du degré relativement large ainsi qu'une structure de "petit-monde". Grossièrement parlant, un graphe "petit-monde" est un réseau où tout individu se trouve relativement près de tout autre individu en termes de distance sociale, une propriété également présente dans les graphes aléatoires mais absente des grilles régulières. Par contre, les réseaux "petit-monde" ont, contrairement aux graphes aléatoires, une certaine structure de localité, mesurée par exemple par une quantité appelée le "coefficient de clustering". Dans le même esprit, plusieurs situations réelles de conflit en économie et sociologie ne sont pas bien décrites ni par des positions géographiquement fixes des individus en grilles régulières, ni par des graphes aléatoires. De plus, il est bien connu que la structure même d'un réseau peut passablement influencer des phénomènes dynamiques tels que la manière qu'a une maladie de se répandre à travers une population, ou encore la façon dont des idées ou une information s'y propagent. Ainsi, durant cette dernière décennie, l'attention de la recherche s'est tout naturellement déplacée des graphes aléatoires et réguliers vers de meilleurs modèles de structure d'interactions sociales. L'objectif principal de ce travail est de découvrir si la structure sous-jacente de graphe de vrais réseaux sociaux peut fournir des explications quant aux raisons pour lesquelles on trouve, chez certains groupes d'êtres humains ou d'animaux, des niveaux de coopération supérieurs à ce qui est prescrit par la théorie classique des jeux. Dans l'optique d'atteindre ce but, je commence par étudier un véritable réseau de collaborations scientifiques et, en utilisant diverses mesures statistiques, je mets en évidence la manière dont il diffère de réseaux biologiques ou technologiques. De plus, j'extrais et je décris sa structure de communautés en tenant compte de l'intensité d'une collaboration. Finalement, j'examine l'évolution temporelle du réseau depuis son origine jusqu'à son état en 2006, date à laquelle l'étude a été effectuée, en suggérant également une vue effective du réseau par opposition à une vue historique. Par la suite, je combine la théorie évolutionnaire des jeux avec des réseaux comprenant plusieurs modèles et le réseau de collaboration susmentionné, afin de déterminer les propriétés structurelles utiles à la promotion de la coopération et les mécanismes responsables du maintien de celle-ci. Je mets en évidence le fait que, pour ne pas succomber à la défection, les coopérateurs exploitent dans la mesure du possible l'hétérogénéité des réseaux sociaux en termes de degré ainsi que la structure de communautés sous-jacente de ces mêmes réseaux. Finalement, je montre que le niveau de coopération et sa stabilité dépendent non seulement du jeu joué, mais aussi des règles de la dynamique évolutionnaire utilisées et du calcul du bénéfice d'un individu.

Emergence of Cooperation on Static and Dynamic Networks

Game theory is a branch of applied mathematics used to analyze situation where two or more agents are interacting. Originally it was developed as a model for conflicts and collaborations between rational and intelligent individuals. Now it finds applications in social sciences, eco- nomics, biology (particularly evolutionary biology and ecology), engineering, political science, international relations, computer science, and philosophy. Networks are an abstract representation of interactions, dependencies or relationships. Net- works are extensively used in all the fields mentioned above and in many more. Many useful informations about a system can be discovered by analyzing the current state of a network representation of such system. In this work we will apply some of the methods of game theory to populations of agents that are interconnected. A population is in fact represented by a network of players where one can only interact with another if there is a connection between them. In the first part of this work we will show that the structure of the underlying network has a strong influence on the strategies that the players will decide to adopt to maximize their utility. We will then introduce a supplementary degree of freedom by allowing the structure of the population to be modified along the simulations. This modification allows the players to modify the structure of their environment to optimize the utility that they can obtain.

The rewards of cooperation: The effects of membership in European regulatory networks

Networks famously epitomize the shift from 'government' to 'governance' as governing structures for exercising control and coordination besides hierarchies and markets. Their distinctive features are their horizontality, the interdependence among member actors and an interactive decision-making style. Networks are expected to increase the problem-solving capacity of political systems in a context of growing social complexity, where political authority is increasingly fragmented across territorial and functional levels. However, very little attention has been given so far to another crucial implication of network governance - that is, the effects of networks on their members. To explore this important question, this article examines the effects of membership in European regulatory networks on two crucial attributes of member agencies, which are in charge of regulating finance, energy, telecommunications and competition: organisational growth and their regulatory powers. Panel analysis applied to data on 118 agencies during a ten-year period and semi-structured interviews provide mixed support regarding the expectation of organisational growth while strongly confirming the positive effect of networks on the increase of the regulatory powers attributed to member agencies.

Firm's network capability and innovation performance: evidences from China hi-tech industry

ABSTRACT : A firm's competitive advantage can arise from internal resources as well as from an interfirm network. -This dissertation investigates the competitive advantage of a firm involved in an innovation network by integrating strategic management theory and social network theory. It develops theory and provides empirical evidence that illustrates how a networked firm enables the network value and appropriates this value in an optimal way according to its strategic purpose. The four inter-related essays in this dissertation provide a framework that sheds light on the extraction of value from an innovation network by managing and designing the network in a proactive manner. The first essay reviews research in social network theory and knowledge transfer management, and identifies the crucial factors of innovation network configuration for a firm's learning performance or innovation output. The findings suggest that network structure, network relationship, and network position all impact on a firm's performance. Although the previous literature indicates that there are disagreements about the impact of dense or spare structure, as well as strong or weak ties, case evidence from Chinese software companies reveals that dense and strong connections with partners are positively associated with firms' performance. The second essay is a theoretical essay that illustrates the limitations of social network theory for explaining the source of network value and offers a new theoretical model that applies resource-based view to network environments. It suggests that network configurations, such as network structure, network relationship and network position, can be considered important network resources. In addition, this essay introduces the concept of network capability, and suggests that four types of network capabilities play an important role in unlocking the potential value of network resources and determining the distribution of network rents between partners. This essay also highlights the contingent effects of network capability on a firm's innovation output, and explains how the different impacts of network capability depend on a firm's strategic choices. This new theoretical model has been pre-tested with a case study of China software industry, which enhances the internal validity of this theory. The third essay addresses the questions of what impact network capability has on firm innovation performance and what are the antecedent factors of network capability. This essay employs a structural equation modelling methodology that uses a sample of 211 Chinese Hi-tech firms. It develops a measurement of network capability and reveals that networked firms deal with cooperation between, and coordination with partners on different levels according to their levels of network capability. The empirical results also suggests that IT maturity, the openness of culture, management system involved, and experience with network activities are antecedents of network capabilities. Furthermore, the two-group analysis of the role of international partner(s) shows that when there is a culture and norm gap between foreign partners, a firm must mobilize more resources and effort to improve its performance with respect to its innovation network. The fourth essay addresses the way in which network capabilities influence firm innovation performance. By using hierarchical multiple regression with data from Chinese Hi-tech firms, the findings suggest that there is a significant partial mediating effect of knowledge transfer on the relationships between network capabilities and innovation performance. The findings also reveal that the impacts of network capabilities divert with the environment and strategic decision the firm has made: exploration or exploitation. Network constructing capability provides a greater positive impact on and yields more contributions to innovation performance than does network operating capability in an exploration network. Network operating capability is more important than network constructing capability for innovative firms in an exploitation network. Therefore, these findings highlight that the firm can shape the innovation network proactively for better benefits, but when it does so, it should adjust its focus and change its efforts in accordance with its innovation purposes or strategic orientation.

Mutual trust and cooperation in the evolutionary hawks-doves game

Using a new dynamical network model of society in which pairwise interactions are weighted according to mutual satisfaction, we show that cooperation is the norm in the hawks-doves game when individuals are allowed to break ties with undesirable neighbors and to make new acquaintances in their extended neighborhood. Moreover, cooperation is robust with respect to rather strong strategy perturbations. We also discuss the empirical structure of the emerging networks, and the reasons that allow cooperators to thrive in the population. Given the metaphorical importance of this game for social interaction, this is an encouraging positive result as standard theory for large mixing populations prescribes that a certain fraction of defectors must always exist at equilibrium.

Meeting report of the European mouse complex genetics network SYSGENET.

The second scientific meeting of the European systems genetics network for the study of complex genetic human disease using genetic reference populations (SYSGENET) took place at the Center for Cooperative Research in Biosciences in Bilbao, Spain, December 10-12, 2012. SYSGENET is funded by the European Cooperation in the Field of Scientific and Technological Research (COST) and represents a network of scientists in Europe that use mouse genetic reference populations (GRPs) to identify complex genetic factors influencing disease phenotypes (Schughart, Mamm Genome 21:331-336, 2010). About 50 researchers working in the field of systems genetics attended the meeting, which consisted of 27 oral presentations, a poster session, and a management committee meeting. Participants exchanged results, set up future collaborations, and shared phenotyping and data analysis methodologies. This meeting was particularly instrumental for conveying the current status of the US, Israeli, and Australian Collaborative Cross (CC) mouse GRP. The CC is an open source project initiated nearly a decade ago by members of the Complex Trait Consortium to aid the mapping of multigenetic traits (Threadgill, Mamm Genome 13:175-178, 2002). In addition, representatives of the International Mouse Phenotyping Consortium were invited to exchange ongoing activities between the knockout and complex genetics communities and to discuss and explore potential fields for future interactions.

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.

Cooperation among selfish individuals in insect societies

Kin selection is the key to understanding the evolution of cooperation in insect societies. However, kin selection also predicts potential kin conflict, and understanding how these conflicts are resolved is a major goal of current research on social insects

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.

Primary pineal tumors: outcome and prognostic factors-a study from the Rare Cancer Network (RCN).

PURPOSE: To better define outcome and prognostic factors in primary pineal tumors. MATERIALS AND METHODS: Thirty-five consecutive patients from seven academic centers of the Rare Cancer Network diagnosed between 1988 and 2006 were included. Median age was 36 years. Surgical resection consisted of biopsy in 12 cases and resection in 21 (2 cases with unknown resection). All patients underwent radiotherapy and 12 patients received also chemotherapy. RESULTS: Histological subtypes were pineoblastoma (PNB) in 21 patients, pineocytoma (PC) in 8 patients and pineocytoma with intermediate differentiation in 6 patients. Six patients with PNB had evidence of spinal seeding. Fifteen patients relapsed (14 PNB and 1 PC) with PNB cases at higher risk (p = 0.031). Median survival time was not reached. Median disease-free survival was 82 months (CI 50 % 28-275). In univariate analysis, age younger than 36 years was an unfavorable prognostic factor (p = 0.003). Patients with metastases at diagnosis had poorer survival (p = 0.048). Late side effects related to radiotherapy were dementia, leukoencephalopathy or memory loss in seven cases, occipital ischemia in one, and grade 3 seizures in two cases. Side effects related to chemotherapy were grade 3-4 leucopenia in five cases, grade 4 thrombocytopenia in three cases, grade 2 anemia in two cases, grade 4 pancytopenia in one case, grade 4 vomiting in one case and renal failure in one case. CONCLUSIONS: Age and dissemination at diagnosis influenced survival in our series. The prevalence of chronic toxicity suggests that new adjuvant strategies are advisable.