Measures of fit in multiple correspondence analysis of crisp and fuzzy coded data

When continuous data are coded to categorical variables, two types of coding are possible: crisp coding in the form of indicator, or dummy, variables with values either 0 or 1; or fuzzy coding where each observation is transformed to a set of "degrees of membership" between 0 and 1, using co-called membership functions. It is well known that the correspondence analysis of crisp coded data, namely multiple correspondence analysis, yields principal inertias (eigenvalues) that considerably underestimate the quality of the solution in a low-dimensional space. Since the crisp data only code the categories to which each individual case belongs, an alternative measure of fit is simply to count how well these categories are predicted by the solution. Another approach is to consider multiple correspondence analysis equivalently as the analysis of the Burt matrix (i.e., the matrix of all two-way cross-tabulations of the categorical variables), and then perform a joint correspondence analysis to fit just the off-diagonal tables of the Burt matrix - the measure of fit is then computed as the quality of explaining these tables only. The correspondence analysis of fuzzy coded data, called "fuzzy multiple correspondence analysis", suffers from the same problem, albeit attenuated. Again, one can count how many correct predictions are made of the categories which have highest degree of membership. But here one can also defuzzify the results of the analysis to obtain estimated values of the original data, and then calculate a measure of fit in the familiar percentage form, thanks to the resultant orthogonal decomposition of variance. Furthermore, if one thinks of fuzzy multiple correspondence analysis as explaining the two-way associations between variables, a fuzzy Burt matrix can be computed and the same strategy as in the crisp case can be applied to analyse the off-diagonal part of this matrix. In this paper these alternative measures of fit are defined and applied to a data set of continuous meteorological variables, which are coded crisply and fuzzily into three categories. Measuring the fit is further discussed when the data set consists of a mixture of discrete and continuous variables.

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.

Genetic Variations and Diseases in UniProtKB/Swiss-Prot: The Ins and Outs of Expert Manual Curation.

During the last few years, next-generation sequencing (NGS) technologies have accelerated the detection of genetic variants resulting in the rapid discovery of new disease-associated genes. However, the wealth of variation data made available by NGS alone is not sufficient to understand the mechanisms underlying disease pathogenesis and manifestation. Multidisciplinary approaches combining sequence and clinical data with prior biological knowledge are needed to unravel the role of genetic variants in human health and disease. In this context, it is crucial that these data are linked, organized, and made readily available through reliable online resources. The Swiss-Prot section of the Universal Protein Knowledgebase (UniProtKB/Swiss-Prot) provides the scientific community with a collection of information on protein functions, interactions, biological pathways, as well as human genetic diseases and variants, all manually reviewed by experts. In this article, we present an overview of the information content of UniProtKB/Swiss-Prot to show how this knowledgebase can support researchers in the elucidation of the mechanisms leading from a molecular defect to a disease phenotype.

Common genetic variation and the control of HIV-1 in humans.

To extend the understanding of host genetic determinants of HIV-1 control, we performed a genome-wide association study in a cohort of 2,554 infected Caucasian subjects. The study was powered to detect common genetic variants explaining down to 1.3% of the variability in viral load at set point. We provide overwhelming confirmation of three associations previously reported in a genome-wide study and show further independent effects of both common and rare variants in the Major Histocompatibility Complex region (MHC). We also examined the polymorphisms reported in previous candidate gene studies and fail to support a role for any variant outside of the MHC or the chemokine receptor cluster on chromosome 3. In addition, we evaluated functional variants, copy-number polymorphisms, epistatic interactions, and biological pathways. This study thus represents a comprehensive assessment of common human genetic variation in HIV-1 control in Caucasians.

Limited genetic diversity and high differentiation among the remnant adder (Vipera berus) populations in the Swiss and French Jura Mountains

Although the adder (Vipera berus) has a large distribution area, this species is particularly threatened in Western Europe due to high habitat fragmentation and human persecution. We developed 13 new microsatellite markers in order to evaluate population structure and genetic diversity in the Swiss and French Jura Mountains, where the species is limited to only a few scattered populations. We found that V. berus exhibits a considerable genetic differentiation among populations (global F-ST = 0.269), even if these are not geographically isolated. Moreover, the genetic diversity within populations in the Jura Mountains and in the less perturbed Swiss Alps is significantly lower than in other French populations, possibly due to post-glacial recolonisation processes. Finally, in order to minimize losses of genetic diversities within isolated populations, suggestions for the conservation of this species in fragmented habitats are proposed.

Genetic diversity and ecological success of Staphylococcus aureus strains colonizing humans.

The genetic determinants and phenotypic traits which make a Staphylococcus aureus strain a successful colonizer are largely unknown. The genetic diversity and population structure of 133 S. aureus isolates from healthy, generally risk-free adult carriers were investigated using four different typing methods: multilocus sequence typing (MLST), amplified fragment length polymorphism analysis (AFLP), double-locus sequence typing (DLST), and spa typing were compared. Carriage isolates displayed great genetic diversity which could only be revealed fully by DLST. Results of AFLP and MLST were highly concordant in the delineation of genotypic clusters of closely related isolates, roughly equivalent to clonal complexes. spa typing and DLST provided considerably less phylogenetic information. The resolution of spa typing was similar to that of AFLP and inferior to that of DLST. AFLP proved to be the most universal method, combining a phylogeny-building capacity similar to that of MLST with a much higher resolution. However, it had a lower reproducibility than sequencing-based MLST, DLST, and spa typing. We found two cases of methicillin-resistant S. aureus colonization, both of which were most likely associated with employment at a health service. Of 21 genotypic clusters detected, 2 were most prevalent: cluster 45 and cluster 30 each colonized 24% of the carrier population. The number of bacteria found in nasal samples varied significantly among the clusters, but the most prevalent clusters were not particularly numerous in the nasal samples. We did not find much evidence that genotypic clusters were associated with different carrier characteristics, such as age, sex, medical conditions, or antibiotic use. This may provide empirical support for the idea that genetic clusters in bacteria are maintained in the absence of adaptation to different niches. Alternatively, carrier characteristics other than those evaluated here or factors other than human hosts may exert selective pressure maintaining genotypic clusters.

Genetic polymorphisms and drug interactions modulating CYP2D6 and CYP3A activities have a major effect on oxycodone analgesic efficacy and safety

Background and purpose: The major drug-metabolizing enzymes for the oxidation of oxycodone are CYP2D6 and CYP3A. A high interindividual variability in the activity of these enzymes because of genetic polymorphisms and/or drug-drug interactions is well established. The possible role of an active metabolite in the pharmacodynamics of oxycodone has been questioned and the importance of CYP3A-mediated effects on the pharmacokinetics and pharmacodynamics of oxycodone has been poorly explored. Experimental approach: We conducted a randomized crossover (five arms) double-blind placebo-controlled study in 10 healthy volunteers genotyped for CYP2D6. Oral oxycodone (0.2 mg·kg−1) was given alone or after inhibition of CYP2D6 (with quinidine) and/or of CYP3A (with ketoconazole). Experimental pain (cold pressor test, electrical stimulation, thermode), pupil size, psychomotor effects and toxicity were assessed. Key results: CYP2D6 activity was correlated with oxycodone experimental pain assessment. CYP2D6 ultra-rapid metabolizers experienced increased pharmacodynamic effects, whereas cold pressor test and pupil size were unchanged in CYP2D6 poor metabolizers, relative to extensive metabolizers. CYP2D6 blockade reduced subjective pain threshold (SPT) for oxycodone by 30% and the response was similar to placebo. CYP3A4 blockade had a major effect on all pharmacodynamic assessments and SPT increased by 15%. Oxymorphone Cmax was correlated with SPT assessment (ρS= 0.7) and the only independent positive predictor of SPT. Side-effects were observed after CYP3A4 blockade and/or in CYP2D6 ultra-rapid metabolizers. Conclusions and implications: The modulation of CYP2D6 and CYP3A activities had clear effects on oxycodone pharmacodynamics and these effects were dependent on CYP2D6 genetic polymorphism.

Synchronization, diversity, and topology of networks of integrate and fire oscillators

We study synchronization dynamics of a population of pulse-coupled oscillators. In particular, we focus our attention on the interplay between topological disorder and synchronization features of networks. First, we analyze synchronization time T in random networks, and find a scaling law which relates T to network connectivity. Then, we compare synchronization time for several other topological configurations, characterized by a different degree of randomness. The analysis shows that regular lattices perform better than a disordered network. This fact can be understood by considering the variability in the number of links between two adjacent neighbors. This phenomenon is equivalent to having a nonrandom topology with a distribution of interactions and it can be removed by an adequate local normalization of the couplings.

Genetic basis and variable phenotypic expression of Kallmann syndrome: towards a unifying theory.

Idiopathic hypogonadotropic hypogonadism (IHH) is defined by absent or incomplete puberty and characterised biochemically by low levels of sex steroids, with low or inappropriately normal gonadotropin hormones. IHH is frequently accompanied by non-reproductive abnormalities, most commonly anosmia, which is present in 50-60% of cases and defines Kallmann syndrome. The understanding of IHH has undergone rapid evolution, both in respect of genetics and breadth of phenotype. Once considered in monogenic Mendelian terms, it is now more coherently understood as a complex genetic condition. Oligogenic and complex genetic-environmental interactions have now been identified, with physiological and environmental factors interacting in genetically susceptible individuals to alter the clinical course and phenotype. These potentially link IHH to ancient evolutionary pressures on the ancestral human genome.

Genetic polymorphisms and drug interactions modulating CYP2D6 and CYP3A activities have a major effect on oxycodone analgesic efficacy and safety.

BACKGROUND AND PURPOSE: The major drug-metabolizing enzymes for the oxidation of oxycodone are CYP2D6 and CYP3A. A high interindividual variability in the activity of these enzymes because of genetic polymorphisms and/or drug-drug interactions is well established. The possible role of an active metabolite in the pharmacodynamics of oxycodone has been questioned and the importance of CYP3A-mediated effects on the pharmacokinetics and pharmacodynamics of oxycodone has been poorly explored. EXPERIMENTAL APPROACH: We conducted a randomized crossover (five arms) double-blind placebo-controlled study in 10 healthy volunteers genotyped for CYP2D6. Oral oxycodone (0.2 mg x kg(-1)) was given alone or after inhibition of CYP2D6 (with quinidine) and/or of CYP3A (with ketoconazole). Experimental pain (cold pressor test, electrical stimulation, thermode), pupil size, psychomotor effects and toxicity were assessed. KEY RESULTS: CYP2D6 activity was correlated with oxycodone experimental pain assessment. CYP2D6 ultra-rapid metabolizers experienced increased pharmacodynamic effects, whereas cold pressor test and pupil size were unchanged in CYP2D6 poor metabolizers, relative to extensive metabolizers. CYP2D6 blockade reduced subjective pain threshold (SPT) for oxycodone by 30% and the response was similar to placebo. CYP3A4 blockade had a major effect on all pharmacodynamic assessments and SPT increased by 15%. Oxymorphone C(max) was correlated with SPT assessment (rho(S)= 0.7) and the only independent positive predictor of SPT. Side-effects were observed after CYP3A4 blockade and/or in CYP2D6 ultra-rapid metabolizers. CONCLUSIONS AND IMPLICATIONS: The modulation of CYP2D6 and CYP3A activities had clear effects on oxycodone pharmacodynamics and these effects were dependent on CYP2D6 genetic polymorphism.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.