Microsatellites can be misleading: an empirical and simulation study.

It has been long recognized that highly polymorphic genetic markers can lead to underestimation of divergence between populations when migration is low. Microsatellite loci, which are characterized by extremely high mutation rates, are particularly likely to be affected. Here, we report genetic differentiation estimates in a contact zone between two chromosome races of the common shrew (Sorex araneus), based on 10 autosomal microsatellites, a newly developed Y-chromosome microsatellite, and mitochondrial DNA. These results are compared to previous data on proteins and karyotypes. Estimates of genetic differentiation based on F- and R-statistics are much lower for autosomal microsatellites than for all other genetic markers. We show by simulations that this discrepancy stems mainly from the high mutation rate of microsatellite markers for F-statistics and from deviations from a single-step mutation model for R-statistics. The sex-linked genetic markers show that all gene exchange between races is mediated by females. The absence of male-mediated gene flow most likely results from male hybrid sterility.

Which prior knowledge? Quantification of in vivo brain 13C MR spectra following 13C glucose infusion using AMARES.

The recent developments in high magnetic field 13C magnetic resonance spectroscopy with improved localization and shimming techniques have led to important gains in sensitivity and spectral resolution of 13C in vivo spectra in the rodent brain, enabling the separation of several 13C isotopomers of glutamate and glutamine. In this context, the assumptions used in spectral quantification might have a significant impact on the determination of the 13C concentrations and the related metabolic fluxes. In this study, the time domain spectral quantification algorithm AMARES (advanced method for accurate, robust and efficient spectral fitting) was applied to 13 C magnetic resonance spectroscopy spectra acquired in the rat brain at 9.4 T, following infusion of [1,6-(13)C2 ] glucose. Using both Monte Carlo simulations and in vivo data, the goal of this work was: (1) to validate the quantification of in vivo 13C isotopomers using AMARES; (2) to assess the impact of the prior knowledge on the quantification of in vivo 13C isotopomers using AMARES; (3) to compare AMARES and LCModel (linear combination of model spectra) for the quantification of in vivo 13C spectra. AMARES led to accurate and reliable 13C spectral quantification similar to those obtained using LCModel, when the frequency shifts, J-coupling constants and phase patterns of the different 13C isotopomers were included as prior knowledge in the analysis.

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.

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.

Climate-based empirical models show biased predictions of butterfly communities along environmental gradients

A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate-based species distribution models (S-SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate-based S-SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate-based S-SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S-SDMs were more accurate in plant-rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate-based S-SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate-based S-SDMs.

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.

Authors' response to 'Limitless longevity': the contribution of rectangularization to the secular increase in life expectancy: an empirical study.

Related article : Letter to the Editor: Karin Modig, Sven Drefahl, and Anders Ahlbon.Limitless longevity: Comment on the Contribution of rectangularization to the secular increase of life expectancy

Quantification of in vivo short echo-time proton magnetic resonance spectra at 14.1 T using two different approaches of modelling the macromolecule spectrum

Reliable quantification of the macromolecule signals in short echo-time H-1 MRS spectra is particularly important at high magnetic fields for an accurate quantification of metabolite concentrations (the neurochemical profile) due to effectively increased spectral resolution of the macromolecule components. The purpose of the present study was to assess two approaches of quantification, which take the contribution of macromolecules into account in the quantification step. H-1 spectra were acquired on a 14.1 T/26 cm horizontal scanner on five rats using the ultra-short echo-time SPECIAL (spin echo full intensity acquired localization) spectroscopy sequence. Metabolite concentrations were estimated using LCModel, combined with a simulated basis set of metabolites using published spectral parameters and either the spectrum of macromolecules measured in vivo, using an inversion recovery technique, or baseline simulated by the built-in spline function. The fitted spline function resulted in a smooth approximation of the in vivo macromolecules, but in accordance with previous studies using Subtract-QUEST could not reproduce completely all features of the in vivo spectrum of macromolecules at 14.1 T. As a consequence, the measured macromolecular 'baseline' led to a more accurate and reliable quantification at higher field strengths.

Understanding consumers repurchase in the context of online shopping: an empirical study

L'utilisation de l'Internet comme medium pour faire ses courses et achats a vu une croissance exponentielle. Cependant, 99% des nouveaux business en ligne échouent. La plupart des acheteurs en ligne ne reviennent pas pour un ré-achat et 60% abandonnent leur chariot avant de conclure l'achat. En effet, après le premier achat, la rétention du consommateur en ligne devient critique au succès du vendeur de commerce électronique. Retenir des consommateurs peut sauver des coûts, accroître les profits, et permet de gagner un avantage compétitif.Les recherches précédentes ont identifié la loyauté comme étant le facteur le plus important dans la rétention du consommateur, et l'engagement ("commitment") comme étant un des facteurs les plus importants en marketing relationnel, offrant une réflexion sur la loyauté. Pourtant, nous n'avons pu trouver d'étude en commerce électronique examinant l'impact de la loyauté en ligne et de l'engagement en ligne ("online commitment") sur le ré-achat en ligne. Un des avantages de l'achat en ligne c'est la capacité à chercher le meilleur prix avec un clic. Pourtant, nous n'avons pu trouver de recherche empirique en commerce électronique qui examinait l'impact de la perception post-achat du prix sur le ré-achat en ligne.L'objectif de cette recherche est de développer un modèle théorique visant à comprendre le ré-achat en ligne, ou la continuité d'achat ("purchase continuance") du même magasin en ligne.Notre modèle de recherche a été testé dans un contexte de commerce électronique réel, sur un échantillon total de 1,866 vrais acheteurs d'un même magasin en ligne. L'étude est centrée sur le ré-achat. Par conséquent, les répondants sélectionnés aléatoirement devaient avoir acheté au moins une fois de ce magasin en ligne avant le début de l'enquête. Cinq mois plus tard, nous avons suivi les répondants pour voir s'ils étaient effectivement revenus pour un ré-achat.Notre analyse démontre que l'intention de ré-achat en ligne n'a pas d'impact significatif sur le ré-achat en ligne. La perception post-achat du prix en ligne ("post-purchase Price perception") et l'engagement normatif en ligne ("Normative Commitment") n'ont pas d'impact significatif sur l'intention de ré-achat en ligne. L'engagement affectif en ligne ("Affective Commitment"), l'attitude loyale en ligne ("Attitudinal Loyalty"), le comportement loyal en ligne ("Behavioral Loyalty"), l'engagement calculé en ligne ("Calculative Commitment") ont un impact positif sur l'intention de ré-achat en ligne. De plus, l'attitude loyale en ligne a un effet de médiation partielle entre l'engagement affectif en ligne et l'intention de ré-achat en ligne. Le comportement loyal en ligne a un effet de mediation partielle entre l'attitude loyale en ligne et l'intention de ré-achat en ligne.Nous avons réalisé deux analyses complémentaires : 1) Sur un échantillon de premiers acheteurs, nous trouvons que la perception post-achat du prix en ligne a un impact positif sur l'intention de ré-achat en ligne. 2) Nous avons divisé l'échantillon de l'étude principale entre des acheteurs répétitifs Suisse-Romands et Suisse-Allemands. Les résultats démontrent que les Suisse-Romands montrent plus d'émotions durant l'achat en ligne que les Suisse-Allemands. Nos résultats contribuent à la recherche académique mais aussi aux praticiens de l'industrie e-commerce.AbstractThe use of the Internet as a shopping and purchasing medium has seen exceptional growth. However, 99% of new online businesses fail. Most online buyers do not comeback for a repurchase, and 60% abandon their shopping cart before checkout. Indeed, after the first purchase, online consumer retention becomes critical to the success of the e-commerce vendor. Retaining existing customers can save costs, increase profits, and is a means of gaining competitive advantage.Past research identified loyalty as the most important factor in achieving customer retention, and commitment as one of the most important factors in relationship marketing, providing a good description of what type of thinking leads to loyalty. Yet, we could not find an e-commerce study investing the impact of both online loyalty and online commitment on online repurchase. One of the advantages of online shopping is the ability of browsing for the best price with one click. Yet, we could not find an e- commerce empirical research investigating the impact of post-purchase price perception on online repurchase.The objective of this research is to develop a theoretical model aimed at understanding online repurchase, or purchase continuance from the same online store.Our model was tested in a real e-commerce context with an overall sample of 1, 866 real online buyers from the same online store.The study focuses on repurchase. Therefore, randomly selected respondents had purchased from the online store at least once prior to the survey. Five months later, we tracked respondents to see if they actually came back for a repurchase.Our findings show that online Intention to repurchase has a non-significant impact on online Repurchase. Online post-purchase Price perception and online Normative Commitment have a non-significant impact on online Intention to repurchase, whereas online Affective Commitment, online Attitudinal Loyalty, online Behavioral Loyalty, and online Calculative Commitment have a positive impact on online Intention to repurchase. Furthermore, online Attitudinal Loyalty partially mediates between online Affective Commitment and online Intention to repurchase, and online Behavioral Loyalty partially mediates between online Attitudinal Loyalty and online Intention to repurchase.We conducted two follow up analyses: 1) On a sample of first time buyers, we find that online post-purchase Price perception has a positive impact on Intention. 2) We divided the main study's sample into Swiss-French and Swiss-German repeated buyers. Results show that Swiss-French show more emotions when shopping online than Swiss- Germans. Our findings contribute to academic research but also to practice.

An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.