The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients.

The success of combination antiretroviral therapy is limited by the evolutionary escape dynamics of HIV-1. We used Isotonic Conjunctive Bayesian Networks (I-CBNs), a class of probabilistic graphical models, to describe this process. We employed partial order constraints among viral resistance mutations, which give rise to a limited set of mutational pathways, and we modeled phenotypic drug resistance as monotonically increasing along any escape pathway. Using this model, the individualized genetic barrier (IGB) to each drug is derived as the probability of the virus not acquiring additional mutations that confer resistance. Drug-specific IGBs were combined to obtain the IGB to an entire regimen, which quantifies the virus' genetic potential for developing drug resistance under combination therapy. The IGB was tested as a predictor of therapeutic outcome using between 2,185 and 2,631 treatment change episodes of subtype B infected patients from the Swiss HIV Cohort Study Database, a large observational cohort. Using logistic regression, significant univariate predictors included most of the 18 drugs and single-drug IGBs, the IGB to the entire regimen, the expert rules-based genotypic susceptibility score (GSS), several individual mutations, and the peak viral load before treatment change. In the multivariate analysis, the only genotype-derived variables that remained significantly associated with virological success were GSS and, with 10-fold stronger association, IGB to regimen. When predicting suppression of viral load below 400 cps/ml, IGB outperformed GSS and also improved GSS-containing predictors significantly, but the difference was not significant for suppression below 50 cps/ml. Thus, the IGB to regimen is a novel data-derived predictor of treatment outcome that has potential to improve the interpretation of genotypic drug resistance tests.

An evolutionary analysis of the relationship between spite and altruism.

We investigate the selective pressures on a social trait when evolution occurs in a population of constant size. We show that any social trait that is spiteful simultaneously qualifies as altruistic. In other words, any trait that reduces the fitness of less related individuals necessarily increases that of related ones. Our analysis demonstrates that the distinction between "Hamiltonian spite" and "Wilsonian spite" is not justified on the basis of fitness effects. We illustrate this general result with an explicit model for the evolution of a social act that reduces the recipient's survival ("harming trait"). This model shows that the evolution of harming is favoured if local demes are of small size and migration is low (philopatry). Further, deme size and migration rate determine whether harming evolves as a selfish strategy by increasing the fitness of the actor, or as a spiteful/altruistic strategy through its positive effect on the fitness of close kin.

Large-scale DNA sequencing in taxonomy and conservation : a case study with the mayfly family Heptageniidae (Ephemeroptera) in the Alps and Madagascar

Les larves aquatiques d'éphémères (Ephemeroptera) colonisent toutes les eaux douces du monde et sont couramment utilisées comme bio-indicateurs de la qualité de l'eau. Le genre Rhithrogena (Heptageniidae) est le deuxième plus diversifié chez les éphémères, et plusieurs espèces européennes ont une distribution restreinte dans des environnements alpins sensibles. Les espèces de Rhithrogena ont été classées en "groupes d'espèces" faciles à identifier. Cependant, malgré leur importance écologique et en terme de conservation, beaucoup d'espèces présentent des différences morphologiques ambiguës, suggérant que lataxonomie actuelle ne refléterait pas correctement leur diversité évolutive. De plus, aucune information sur leurs relations, leur origine, le taux de spéciation ou les mécanismes ayant provoqué leur remarquable diversification dans les Alpes n'est disponible. Nous avons d'abord examiné le statut spécifique d'environ 50% des espèces européennes de Rhithrogena en utilisant un large échantillonnage de populations alpines incluant 22 localités typiques, ainsi qu'une analyse basée sur le modèle général mixte de Yule et de coalescence (GMYC) appliqué à un gène mitochondrial standard (coxl) et à un gène nucléaire développé spécifiquement pour cette étude. Nous avons observé un regroupement significatif des séquences coxl en 31 espèces potentielles, et nos résultats ont fortement suggéré la présence d'espèces cryptiques et de fractionnements taxonomiques excessifs chez les Rhithrogena. Nos analyses phylogénétiques ont démontré la monophylie de quatre des six groupes d'espèces reconnus présents dans notre échantillonnage. La taxonomie ADN développée dans cette étude pose les bases d'une future révision de ce genre important mais cryptique en Europe. Puis nous avons mené une étude phylogénétique multi-gènes entre les espèces européennes de Rhithrogena. Les données provenant de trois gènes nucléaires et de deux gènes mitochondriaux ont été largement concordantes, et les relations entre les espèces bien résolues au sein de la plupart des groupes d'espèces dans une analyse combinant tous les gènes. En l'absence de points de calibration extérieurs tels que des fossiles, nous avons appliqué à nos données mitochondriales une horloge moléculaire standard pour les insectes, suggérant une origine des Rhithrogena alpins à la limite Oligocène / Miocène. Nos résultats ont montré le rôle prépondérant qu'ont joué les glaciations du quaternaire dans leur diversification, favorisant la spéciation d'au moins la moitié des espèces actuelle dans les Alpes. La biodiversité et le taux d'endémisme à Madagascar, notamment au niveau de la faune des eaux douces, sont parmi les plus extraordinaires et les plus menacés au monde. On pense que beaucoup d'espèces d'éphémères sont restreintes à un seul bassin versant (microendémisme) dans les zones forestières, ce qui les rendrait particulièrement sensibles à la réduction et à la dégradation de leur habitat. Mis à part deux espèces décrites, Afronurus matitensis et Compsoneuria josettae, les Heptageniidae sont pratiquement inconnus à Madagascar. Les deux genres ont une distribution discontinue en Afrique, à Madagascar et en Asie du Sud-Est, et leur taxonomie complexe est régulièrement révisée. L'approche standard pour comprendre leur diversité, leur endémisme et leur origine requerrait un échantillonnage étendu sur plusieurs continents et des années de travaux taxonomiques. Pour accélérer le processus, nous avons utilisé des collections de musées ainsi que des individus fraîchement collectés, et appliqué une approche combinant taxonomie ADN et phylogénie. L'analyses GMYC du gène coxl a délimité 14 espèces potentielles à Madagascar, dont 70% vraisemblablement microendémiques. Une analyse phylogénique incluant des espèces africaines et asiatiques portant sur deux gènes mitochondriaux et quatre gènes nucléaires a montré que les Heptageniidae malgaches sont monophylétiques et groupe frère des Compsoneuria africains. L'existence de cette lignée unique, ainsi qu'un taux élevé de microendémisme, mettent en évidence leur importance en terme de conservation. Nos résultats soulignent également le rôle important que peuvent jouer les collections de musées dans les études moléculaires et en conservation. - Aquatic nymphs of mayflies (Ephemeroptera) colonize all types of freshwaters throughout the world and are extensively used as bio-indicators of water quality. Rhithrogena (Heptageniidae) is the second most species-rich genus of mayflies, and several European species have restricted distributions in sensitive Alpine environments and therefore are of conservation interest. The European Rhithrogena species are arranged into "species groups" that are easily identifiable. However, despite their ecological and conservation importance, ambiguous morphological differences among many species suggest that the current taxonomy may not accurately reflect their evolutionary diversity. Moreover, no information about their relationships, origin, timing of speciation and mechanisms promoting their successful diversification in the Alps is available. We first examined the species status of ca. 50% of European Rhithrogena diversity using a widespread sampling scheme of Alpine species that included 22 type localities, general mixed Yule- coalescent (GMYC) model analysis of one standard mitochondrial (coxl) and one newly developed nuclear marker. We observed significant clustering of coxl into 31 GMYC species, and our results strongly suggest the presence of both cryptic diversity and taxonomic oversplitting in Rhithrogena. Phylogenetic analyses recovered four of the six recognized species groups in our samples as monophyletic. The DNA taxonomy developed here lays the groundwork for a future revision of this important but cryptic genus in Europe. Then we conducted a species-level, multiple-gene phylogenetic study of European Rhithrogena. Data from three nuclear and two mitochondrial loci were broadly congruent, and species-level relationships were well resolved within most species groups in a combined analysis. In the absence of external calibration points like fossils, we applied a standard insect molecular clock hypothesis to our mitochondrial data, suggesting an origin of Alpine Rhithrogena in the Oligocene / Miocene boundary. Our results highlighted the preponderant role that quaternary glaciations played in their diversification, promoting speciation of at least half of the current diversity in the Alps. Madagascar's biodiversity and endemism are among the most extraordinary and endangered in the world. This includes the island's freshwater biodiversity, although detailed knowledge of the diversity, endemism, and biogeographic origin of freshwater invertebrates is lacking. Many mayfly species are thought to be restricted to single river basins (microendemic species) in forested areas, making them particularly sensitive to habitat reduction and degradation. The Heptageniidae are practically unknown in Madagascar except for two described species, Afronurus matitensis and Compsoneuria josettae. Both genera have a disjunct distribution in Africa, Madagascar and Southeast Asia, and a complex taxonomic status still in flux. The standard approach to understanding their diversity, endemism, and origin would require extensive field sampling on several continents and years of taxonomic work. Here we circumvent this using museum collections and freshly collected individuals in a combined approach of DNA taxonomy and phylogeny. The cox/-based GMYC analysis revealed 14 putative species on Madagascar, 70% of which potentially microendemics. A phylogenetic analysis that included African and Asian species and data from two mitochondrial and four nuclear loci indicated the Malagasy Heptageniidae are monophyletic and sister to African Compsoneuria. The observed monophyly and high microendemism highlight their conservation importance. Our results also underline the important role that museum collections can play in molecular studies, especially in critically endangered biodiversity hotspots like Madagascar.

Natural genetic variation in Arabidopsis: tools, traits and prospects for evolutionary ecology.

BACKGROUND: The model plant Arabidopsis thaliana (Arabidopsis) shows a wide range of genetic and trait variation among wild accessions. Because of its unparalleled biological and genomic resources, the potential of Arabidopsis for molecular genetic analysis of this natural variation has increased dramatically in recent years. SCOPE: Advanced genomics has accelerated molecular phylogenetic analysis and gene identification by quantitative trait loci (QTL) mapping and/or association mapping in Arabidopsis. In particular, QTL mapping utilizing natural accessions is now becoming a major strategy of gene isolation, offering an alternative to artificial mutant lines. Furthermore, the genomic information is used by researchers to uncover the signature of natural selection acting on the genes that contribute to phenotypic variation. The evolutionary significance of such genes has been evaluated in traits such as disease resistance and flowering time. However, although molecular hallmarks of selection have been found for the genes in question, a corresponding ecological scenario of adaptive evolution has been difficult to prove. Ecological strategies, including reciprocal transplant experiments and competition experiments, and utilizing near-isogenic lines of alleles of interest will be a powerful tool to measure the relative fitness of phenotypic and/or allelic variants. CONCLUSIONS: As the plant model organism, Arabidopsis provides a wealth of molecular background information for evolutionary genetics. Because genetic diversity between and within Arabidopsis populations is much higher than anticipated, combining this background information with ecological approaches might well establish Arabidopsis as a model organism for plant evolutionary ecology.

Novel method to estimate the phenotypic variation explained by genome-wide association studies reveals large fraction of the missing heritability.

Genome-wide association studies (GWAS) are conducted with the promise to discover novel genetic variants associated with diverse traits. For most traits, associated markers individually explain just a modest fraction of the phenotypic variation, but their number can well be in the hundreds. We developed a maximum likelihood method that allows us to infer the distribution of associated variants even when many of them were missed by chance. Compared to previous approaches, the novelty of our method is that it (a) does not require having an independent (unbiased) estimate of the effect sizes; (b) makes use of the complete distribution of P-values while allowing for the false discovery rate; (c) takes into account allelic heterogeneity and the SNP pruning strategy. We applied our method to the latest GWAS meta-analysis results of the GIANT consortium. It revealed that while the explained variance of genome-wide (GW) significant SNPs is around 1% for waist-hip ratio (WHR), the observed P-values provide evidence for the existence of variants explaining 10% (CI=[8.5-11.5%]) of the phenotypic variance in total. Similarly, the total explained variance likely to exist for height is estimated to be 29% (CI=[28-30%]), three times higher than what the observed GW significant SNPs give rise to. This methodology also enables us to predict the benefit of future GWA studies that aim to reveal more associated genetic markers via increased sample size.

Evolutionary patchwork of an insecticidal toxin shared between plant-associated pseudomonads and the insect pathogens Photorhabdus and Xenorhabdus.

BACKGROUND: Root-colonizing fluorescent pseudomonads are known for their excellent abilities to protect plants against soil-borne fungal pathogens. Some of these bacteria produce an insecticidal toxin (Fit) suggesting that they may exploit insect hosts as a secondary niche. However, the ecological relevance of insect toxicity and the mechanisms driving the evolution of toxin production remain puzzling. RESULTS: Screening a large collection of plant-associated pseudomonads for insecticidal activity and presence of the Fit toxin revealed that Fit is highly indicative of insecticidal activity and predicts that Pseudomonas protegens and P. chlororaphis are exclusive Fit producers. A comparative evolutionary analysis of Fit toxin-producing Pseudomonas including the insect-pathogenic bacteria Photorhabdus and Xenorhadus, which produce the Fit related Mcf toxin, showed that fit genes are part of a dynamic genomic region with substantial presence/absence polymorphism and local variation in GC base composition. The patchy distribution and phylogenetic incongruence of fit genes indicate that the Fit cluster evolved via horizontal transfer, followed by functional integration of vertically transmitted genes, generating a unique Pseudomonas-specific insect toxin cluster. CONCLUSIONS: Our findings suggest that multiple independent evolutionary events led to formation of at least three versions of the Mcf/Fit toxin highlighting the dynamic nature of insect toxin evolution.

Evolutionary dynamics of collective action in spatially structured populations.

Many models proposed to study the evolution of collective action rely on a formalism that represents social interactions as n-player games between individuals adopting discrete actions such as cooperate and defect. Despite the importance of spatial structure in biological collective action, the analysis of n-player games games in spatially structured populations has so far proved elusive. We address this problem by considering mixed strategies and by integrating discrete-action n-player games into the direct fitness approach of social evolution theory. This allows to conveniently identify convergence stable strategies and to capture the effect of population structure by a single structure coefficient, namely, the pairwise (scaled) relatedness among interacting individuals. As an application, we use our mathematical framework to investigate collective action problems associated with the provision of three different kinds of collective goods, paradigmatic of a vast array of helping traits in nature: "public goods" (both providers and shirkers can use the good, e.g., alarm calls), "club goods" (only providers can use the good, e.g., participation in collective hunting), and "charity goods" (only shirkers can use the good, e.g., altruistic sacrifice). We show that relatedness promotes the evolution of collective action in different ways depending on the kind of collective good and its economies of scale. Our findings highlight the importance of explicitly accounting for relatedness, the kind of collective good, and the economies of scale in theoretical and empirical studies of the evolution of collective action.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

CNDOL: A fast and reliable method for the calculation of electronic properties of very large systems. Applications to retinal binding pocket in rhodopsin and gas phase porphine.

Very large molecular systems can be calculated with the so called CNDOL approximate Hamiltonians that have been developed by avoiding oversimplifications and only using a priori parameters and formulas from the simpler NDO methods. A new diagonal monoelectronic term named CNDOL/21 shows great consistency and easier SCF convergence when used together with an appropriate function for charge repulsion energies that is derived from traditional formulas. It is possible to obtain a priori molecular orbitals and electron excitation properties after the configuration interaction of single excited determinants with reliability, maintaining interpretative possibilities even being a simplified Hamiltonian. Tests with some unequivocal gas phase maxima of simple molecules (benzene, furfural, acetaldehyde, hexyl alcohol, methyl amine, 2,5 dimethyl 2,4 hexadiene, and ethyl sulfide) ratify the general quality of this approach in comparison with other methods. The calculation of large systems as porphine in gas phase and a model of the complete retinal binding pocket in rhodopsin with 622 basis functions on 280 atoms at the quantum mechanical level show reliability leading to a resulting first allowed transition in 483 nm, very similar to the known experimental value of 500 nm of "dark state." In this very important case, our model gives a central role in this excitation to a charge transfer from the neighboring Glu(-) counterion to the retinaldehyde polyene chain. Tests with gas phase maxima of some important molecules corroborate the reliability of CNDOL/2 Hamiltonians.

Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair.

Menopause timing has a substantial impact on infertility and risk of disease, including breast cancer, but the underlying mechanisms are poorly understood. We report a dual strategy in ∼70,000 women to identify common and low-frequency protein-coding variation associated with age at natural menopause (ANM). We identified 44 regions with common variants, including two regions harboring additional rare missense alleles of large effect. We found enrichment of signals in or near genes involved in delayed puberty, highlighting the first molecular links between the onset and end of reproductive lifespan. Pathway analyses identified major association with DNA damage response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomization analyses supported a causal effect of later ANM on breast cancer risk (∼6% increase in risk per year; P = 3 × 10(-14)), likely mediated by prolonged sex hormone exposure rather than DDR mechanisms.

The pseudo-high-risk prevention strategy

Key Messages: A fundamental failure of high-risk prevention strategies is their inability to prevent disease in the large part of the population at a relatively small average risk and from which most cases of diseases originate. The development of individual predictive medicine and the widening of high-risk categories for numerous (chronic) conditions lead to the application of pseudo-high-risk prevention strategies. Widening the criteria justifying individual preventive interventions and the related pseudo-high-risk strategies lead to treating, individually, ever healthier and larger strata of the population. The pseudo-high-risk prevention strategies raise similar problems compared with high-risk strategies, however on a larger scale and without any of the benefit of population-based strategies. Some 30 years ago, the strengths and weaknesses of population-based and high-risk prevention strategies were brilliantly delineated by Geoffrey Rose in several seminal publications (Table 1).1,2 His work had major implications not only for epidemiology and public health but also for clinical medicine. In particular, Rose demonstrated the fundamental failure of high-risk prevention strategies, that is, by missing a large number of preventable cases.

Cardiovascular risk among hypertensive adolescents and the potential benefit of a screen-and-treat strategy.

To evaluate whether screening for hypertension should start early in life, information on the risk of diseases associated with the level of blood pressure in childhood or adolescence is needed. The study by Leiba et al. that is reported in the current issue of Pediatric Nephrology demonstrates convincingly that hypertensive adolescents are at higher risk of cardiovascular death than normotensive adolescents. Nevertheless, it can be shown that this excess risk is not sufficient to justify a screen-and-treat strategy. Since the large majority of cardiovascular deaths occur among normotensive adolescents, measures for primordial prevention of cardiovascular diseases could have a much larger impact at the population level.

Combining conservative and variable markers to inferthe evolutionary history of Prunus subgen. Amygdalus s.l.under domestication

The genus Prunus L. is large and economically important. However, phylogenetic relationships within Prunus at low taxonomic level, particularly in the subgenus Amygdalus L. s.l., remain poorly investigated. This paper attempts to document the evolutionary history of Amygdalus s.l. and establishes a temporal framework, by assembling molecular data from conservative and variable molecular markers. The nuclear s6pdh gene in combination with the plastid trnSG spacer are analyzed with bayesian and maximum likelihood methods. Since previous phylogenetic analysis with these markers lacked resolution, we additionally analyzed 13 nuclear SSR loci with the δµ2 distance, followed by an unweighted pair group method using arithmetic averages algorithm. Our phylogenetic analysis with both sequence and SSR loci confirms the split between sections Amygdalus and Persica, comprising almonds and peaches, respectively. This result is in agreement with biogeographic data showing that each of the two sections is naturally distributed on each side of the Central Asian Massif chain. Using coalescent based estimations, divergence times between the two sections strongly varied when considering sequence data only or combined with SSR. The sequence-only based estimate (5 million years ago) was congruent with the Central Asian Massif orogeny and subsequent climate change. Given the low level of differentiation within the two sections using both marker types, the utility of combining microsatellites and data sequences to address phylogenetic relationships at low taxonomic level within Amygdalus is discussed. The recent evolutionary histories of almond and peach are discussed in view of the domestication processes that arose in these two phenotypically-diverging gene pools: almonds and peaches were domesticated from the Amygdalus s.s. and Persica sections, respectively. Such economically important crops may serve as good model to study divergent domestication process in close genetic pool.

Care delivery value chains for ophthalmic clinics in Switzerland

Perceived patient value is often not aligned with the emerging expenses for health care services. In other words, the costs are often supposed as rising faster than the actual value for the patients. This fact is causing major concerns to governments, health plans, and individuals. Attempts to solve the problem have habitually been on the operational effectiveness side: increasing patient volume, minimizing costs, rationing, or closing hospitals, usually resulting in a zero-sum game. Only few approaches come from the strategic positioning side and "competition" among hospitals is still perceived rather as a danger than as a chance to create a positive-sum game and stimulate patient value. In their 2006 book, "Redefining Health Care", the renowned Harvard strategy professor Michael E. Porter and hospital management expert Professor Elizabeth Olmsted Teisberg approach the challenge from the positive-sum perspective: they propose to form Integrated Practice Units (IPUs) and manage hospitals in a modern, patient value oriented way. They argue that creating value-based competition on results should have the same effect on the health care sector like transparency and competition turned other industries with out-dated management models (like recently the inert telecommunication industry) into highly competitive and customer value creating businesses. The objective of this paper is to elaborate Care Delivery Value Chains for Integrated Practice Units in ophthalmic clinics and gather a first feedback from Swiss hospital managers, ophthalmologists, and patients, if such an approach could be a realistic way to improve health care management. First, Porter's definition of competitiveness (distinction between operational effectiveness and strategic positioning) is explained. Then, the Care Delivery Value Chain is introduced as a key element for understanding value-based management, followed by three practice examples for ophthalmic clinics. Finally, recommendations are given how the Care Delivery Value Chain can be managed efficiently and how the obstacles of becoming a patient-oriented organization can be overcome. The conclusion is that increased transparency and value-based competition on results has the potential to change the mindset of hospital managers-which will align patient value with the emerging health care expenses. Early adapters of this management approach will gain a competitive advantage. [Author, p. 6]