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Nous nous intéressons aux interactions entre espèces. Les données d'interactions sont représentées par un réseau dont la structure est étudiée pour comprendre l'organisation de l'écosystème. Pour ce faire, nous présentons les modèles à blocs stochastiques qui sont des modèles de mélange adaptés aux réseaux et qui permettent d'obtenir une classification des espèces sur la base de leurs patrons d'interactions.

Generalized multipartite networks consist in the joint observation of several networks implying some common pre-specified groups of individuals. Such complex networks arise commonly in social sciences, biology, ecology, etc. We propose a flexible probabilistic model named Multipartite Block Model (MBM) able to unravel the topology of multipartite networks by identifying clusters (blocks) of nodes sharing the same patterns of connectivity across the collection of networks they are involved in. The model parameters are estimated through a variational version of the Expectation–Maximization algorithm. The numbers of blocks are chosen using an Integrated Completed Likelihood criterion specifically designed for our model. A simulation study illustrates the robustness of the inference strategy. Finally, two datasets respectively issued from ecology and ethnobiology are analyzed with the MBM in order to illustrate its flexibility and its relevance for the analysis of real datasets. The inference procedure is implemented in an R -package GREMLIN , available on Github ( https://github.com/Demiperimetre/GREMLINhttps://github.com/Demiperimetre/GREMLIN ).

International audience; The robustness of an ecological network quantifies the resilience of the ecosystem it represents to species loss. It corresponds to the proportion of species that are disconnected from the rest of the network when extinctions occur sequentially. Classically, the robustness is calculated for a given network, from the simulation of a large number of extinction sequences. The link between network structure and robustness remains an open question. Setting a joint probabilistic model on the network and the extinction sequences allows the analysis of this relation. Bipartite stochastic block models have proven their ability to model bipartite networks e.g. plant-pollinator networks: species are divided into blocks and interaction probabilities are determined by the blocks of membership. Analytical expressions of the expectation and variance of robustness are obtained under this model, for different distributions of primary extinction sequences. The impact of the network structure on the robustness is analyzed through a set of properties and numerical illustrations. The analysis of a collection of bipartite ecological networks allows us to compare the empirical approach to our probabilistic approach, and illustrates the relevance of the latter when it comes to computing the robustness of a partially observed or incompletely sampled network.

Background and AimsFunctional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost. In this paper, we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light.MethodsWe used a Bayesian calibration method to estimate the values of 5 parameters of the WALTer model by fitting the model outputs to tillering dynamics data. The method presented in this paper is based on the Efficient Global Optimisation algorithm. It involves the use of Gaussian process metamodels to generate fast approximations of the model outputs. To account for the uncertainty associated with the metamodels approximations, an adaptive design was used. The efficacy of the method was first assessed using simulated data. The calibration was then applied to experimental data.Key ResultsThe method presented here performed well on both simulated and experimental data. In particular, the use of an adaptive design proved to be a very efficient method to improve the quality of the metamodels predictions, especially by reducing the uncertainty in areas of the parameter space that were of interest for the fitting. Moreover, we showed the necessity to have a diversity of field data in order to be able to calibrate the parameters.ConclusionsThe method presented in this paper, based on an adaptive design and Gaussian process metamodels, is an efficient approach for the calibration of WALTer and could be of interest for the calibration of other functional-structural plant models.

AMS subject classifications. 62K99, 62L05, 60G15; Making good predictions of a physical system using a computer code requires the inputs to be carefully specified. Some of these inputs, called control variables, reproduce physical conditions, whereas other inputs, called parameters, are specific to the computer code and most often uncertain. The goal of statistical calibration consists in reducing their uncertainty with the help of a statistical model which links the code outputs with the field measurements. In a Bayesian setting, the posterior distribution of these parameters is typically sampled using Markov Chain Monte Carlo methods. However, they are impractical when the code runs are highly time-consuming. A way to circumvent this issue consists of replacing the computer code with a Gaussian process emulator, then sampling a surrogate posterior distribution based on it. Doing so, calibration is subject to an error which strongly depends on the numerical design of experiments used to fit the emulator. Under the assumption that there is no code discrepancy, we aim to reduce this error by constructing a sequential design by means of the expected improvement criterion. Numerical illustrations in several dimensions assess the efficiency of such sequential strategies.

In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer models -- providing a catalogue of statistical methods for practitioners, an introductory view for statisticians (whether familiar with deterministic computer models or not), and an emphasis on open questions of relevance to practitioners and statisticians. Gaussian process surrogate models take center stage in this review, and these, along with several extensions needed for stochastic settings, are explained. The basic issues of designing a stochastic computer experiment and calibrating a stochastic computer model are prominent in the discussion. Instructive examples, with data and code, are used to describe the implementation of, and results from, various methods., 48 pages, 8 figures

The maternal protein diet during the perinatal period can program the health of adult offspring. This study in rats evaluated the effects of protein quantity and quality in the maternal diet during gestation and lactation on weight and adiposity in female offspring. Six groups of dams were fed a high-protein (HP; 47% protein) or normal-protein (NP; 19% protein) isocaloric diet during gestation (G) using either cow's milk (M), pea (P) or turkey (T) proteins. During lactation, all dams received the NP diet (protein source unchanged). From postnatal day (PND) 28 until PND70, female pups (n=8) from the dam milk groups were exposed to either an NP milk diet (NPMW) or to dietary self-selection (DSS). All other pups were only exposed to DSS. The DSS design was a choice between five food cups containing HPM, HPP, HPT, carbohydrates or lipids. The weights and food intakes of the animals were recorded throughout the study, and samples from offspring were collected on PND70. During the lactation and postweaning periods, body weight was lower in the pea and turkey groups (NPG and HPG) versus the milk group (PW group (PG groups, total adipose tissue was increased (P=.03) associated with higher fasting plasma leptin (P

Fetal and early postnatal nutritional environments contribute to lifelong health. High-protein (HP) intake in early life can increase obesity risk in response to specific feeding conditions after weaning. This study investigated the effects of a maternal HP diet during pregnancy and/or lactation on the metabolic health of offspring. Three groups of dams received a normal-protein (NP, 20E% proteins) diet during gestation and lactation (Control group), an HP diet (55E% proteins) during gestation (HPgest group), or an HP diet during lactation (HPlact group). From weaning until 10 weeks, female pups were exposed to the NP, the HP or the western (W) diet. HPgest pups had more adipocytes (p = 0.009), more subcutaneous adipose tissue (p = 0.04) and increased expression of genes involved in liver fatty acid synthesis at 10 weeks (p &lt, 0.05). HPgest rats also showed higher food intake and adiposity under the W diet compared to the Control and HPlact rats (p &le, 0.04). The post-weaning HP diet reduced weight (p &lt, 0.0001), food intake (p &lt, 0.0001), adiposity (p &lt, 0.0001) and glucose tolerance (p &lt, 0.0001) compared to the NP and W diets, this effect was enhanced in the HPgest group (p = 0.04). These results show that a maternal HP diet during gestation, but not lactation, leads to a higher susceptibility to obesity and glucose intolerance in female offspring.

Background The intake of specific amino acids (AA) has been associated with cardiovascular health, but amino acids are consumed together as dietary protein. Here we investigated the association between identified patterns of amino acid intake and cardiovascular mortality. Methods A total of 2216 cardiovascular deaths among 79 838 men and women from the Adventist Health Study-2 were included in our analysis. Baseline dietary patterns based on the participants' amino acids intakes were derived by factor analysis. Using Cox regression analyses, we estimated multivariate-adjusted hazard ratios (HRs) adjusted for sociodemographic and lifestyle factors and other dietary components. Results Three patterns of amino acids were identified. Factor 1 was positively associated with cardiovascular disease (CVD) mortality [hazard ratio (HR)Q5-Q1: 1.62, 98.75% confidence interval (CI): 1.15, 2.28; P-trend Conclusions Indispensable AA have a positive and some non-indispensable AA have a negative, independent, strong association with the risk of cardiovascular mortality.

Meteorological ensemble members are a collection of scenarios for future weather issued by a meteorological center. Such ensembles nowadays form the main source of valuable information for probabilistic forecasting which aims at producing a predictive probability distribution of the quantity of interest instead of a single best guess point-wise estimate. Unfortunately, ensemble members cannot generally be considered as a sample from such a predictive probability distribution without a preliminary post-processing treatment to re-calibrate the ensemble. Two main families of post-processing methods, either competing such as the BMA or collaborative such as the EMOS, can be found in the literature. This paper proposes a mixed-effect model belonging to the collaborative family. The structure of the model is formally justified by Bruno de Finetti’s representation theorem which shows how to construct operational statistical models of ensemble based on judgments of invariance under the relabeling of the members. Its interesting specificities are as follows: (1) exchangeability contributes to parsimony, with an interpretation of the latent pivot of the ensemble in terms of a statistical synthesis of the essential meteorological features of the ensemble members, (2) a multiensemble implementation is straightforward, allowing to take advantage of various information so as to increase the sharpness of the forecasting procedure. Focus is cast onto normal statistical structures, first with a direct application for temperatures, then with its very convenient Tobit extension for precipitation. Inference is performed by expectation maximization (EM) algorithms with both steps leading to explicit analytic expressions in the Gaussian temperature case, and recourse is made to stochastic conditional simulations in the zero-inflated precipitation case. After checking its good behavior on artificial data, the proposed post-processing technique is applied to temperature and precipitation ensemble forecasts produced for lead times from 1 to 9 days over five river basins managed by Hydro-Quebec, which ranks among the world’s largest electric companies. These ensemble forecasts, provided by three meteorological global forecast centers (Canadian, USA and European), were extracted from the THORPEX Interactive Grand Global Ensemble (TIGGE) database. The results indicate that post-processed ensembles are calibrated and generally sharper than the raw ensembles for the five watersheds under study. Supplementary materials accompanying this paper appear on-line.

International audience; In many epidemiologic models, a disease is assumed to spread along a contact network. We aim to infer this network, in addition to the epidemiologic model parameters, from the binary status of individuals observed throughout time. We perform an exact evaluation of the probability for each edge to be part of the network by using the matrix-tree theorem on the set of vertices made of the individual status at all times. This leads to a computational complexity of order O(mn2), where n is the number of individuals and m the length of the time series. Simulations are provided to demonstrate the efficiency of the proposed method, and it is applied on data concerning seed choices by farmers in India and on data on a measles outbreak.

International audience; This article deals with nonobserved dyads during the sampling of a network and consecutive issues in the inference of the stochastic block model (SBM). We review sampling designs and recover missing at random (MAR) and not missing at random (NMAR) conditions for the SBM. We introduce variants of the variational EM algorithm for inferring the SBM under various sampling designs (MAR and NMAR) all available as an R package. Model selection criteria based on integrated classification likelihood are derived for selecting both the number of blocks and the sampling design. We investigate the accuracy and the range of applicability of these algorithms with simulations. We explore two real-world networks from ethnology (seed circulation network) and biology (protein–protein interaction network), where the interpretations considerably depend on the sampling designs considered. Supplementary materials for this article are available online.

Diet of mothers during gestation may impact offspring phenotype. This study evaluated the consequences of a maternal High-Protein (HP) diet during gestation on food preferences and phenotypic characteristics in adult rat offspring. Dams were fed a HP or a Normal-Protein (NP) isocaloric diet during gestation only. Weaned female pups were divided into 3 diet groups: NP control or one of two dietary self-selection (DSS) conditions. In DSS1, offspring had a free choice between proteins (100%) or a mix of carbohydrates (88%) and lipids (12%). In DSS2, the choice was between proteins (100%), carbohydrate (100%) or lipids (100%). DSS2 groups consumed more of their energy from protein and lipids, with a decreased carbohydrate intake (p &lt, 0.0001) compared to NP groups, regardless of the maternal diet. Offspring from HP gestation dams fed the DSS2 diet (HPDSS2) had a 41.2% increase of total adiposity compared to NPDSS2 (p &lt, 0.03). Liver Insulin receptor and Insulin substrate receptor 1 expression was decreased in offspring from HP compared to NP gestation dams. These results showed the specific effects of DSS and maternal diet and data suggested that adult, female offspring exposed to a maternal HP diet during foetal life were more prone to adiposity development, in response to postweaning food conditions.

International audience; Modelling complex systems such as agroecosystems often requires the quantification of a large number of input factors. Sensitivity analyses are useful to fix the appropriate spatial and temporal resolution of models and to reduce the number of factors to be measured or estimated accurately. Comprehensive spatial and dynamic sensitivity analyses were applied to the NitroScape model, a deterministic spatially distributed model describing nitrogen transfers and transformations in rural landscapes. Simulations were led on a virtual landscape that represented five years of intensive farm management and covering an area of 3 $km^2$. Cluster analyses were applied to summarize the results of the sensitivity analysis on the ensemble of model outcomes. The 29 studied output variables were split into five different clusters that grouped outcomes with similar response to input factors. Among the 11 studied factors, model outcomes were mainly sensitive to the inputs characterizing the type of fertilization and the hydrological features of soils. The total amount of nitrogen in the catchment discharge was the type of nitrogen used in fertilization, while nitrogen concentration in catchment discharge was mainly driven by soil porosity and lateral water transmissivity. The vertical resolution of the model had a significant impact on the ammonium surface content and on the nitrate groundwater concentration, while the model horizontal resolution had a significant impact on the dynamic and spatial distributions of model outcomes, but it did not significantly affect nitrogen variables when they were spatially- and temporally-aggregated. The methodology we applied should prove useful to synthesise sensitivity analyses of models with multiple space-time input and output variables.

Background and Aims Branching is a main morphogenetic process involved in the adaptation of plants to the environment. In grasses, tillering is divided into three phases: tiller emergence, cessation of tillering and tiller regression. Understanding and prediction of the tillering process is a major challenge to better control cereal yields. In this paper, we present and evaluate WALTer, an individual-based model of wheat built on simple self-adaptive rules for predicting the tillering dynamics at contrasting sowing densities. Methods WALTer simulates the three-dimensional (3-D) development of the aerial architecture of winter wheat. Tillering was modelled using two main hypotheses: (H1) a plant ceases to initiate new tillers when a critical Green Area Index (GAIc) is reached, and (H2) the regression of a tiller occurs if its interception of light is below a threshold (PARt). The development of vegetative organs follows descriptive rules adapted from the literature. A sensitivity analysis was performed to evaluate the impact of each parameter on tillering and GAI dynamics. WALTer was parameterized and evaluated using an initial dataset providing an extensive description of GAI dynamics, and another dataset describing tillering dynamics under a wide range of sowing densities. Key Results Sensitivity analysis indicated the predominant importance of GAIc and PARt. Tillering and GAI dynamics of expt 1 were well fit by WALTer. Once calibrated based on the agronomic density of expt 2, tillering parameters allowed an adequate prediction of tillering dynamics at contrasting sowing densities. Conclusions Using simple rules and a small number of parameters, WALTer efficiently simulated the wheat tillering dynamics observed at contrasting densities in experimental data. These results show that the definition of a critical GAI and a threshold of PAR is a relevant way to represent, respectively, cessation of tillering and tiller regression under competition for light.

In this paper, we propose a practical Bayesian framework for the calibration and validation of a computer code, and apply it to a case study concerning the energy consumption forecasting of a building. Validation allows to quantify forecasting uncertainties in view of the code's final use. Here we explore the situation where an energy provider promotes new energy contracts for residential buildings, tailored to each customer's needs, and including a guarantee of energy performance. Based on power field measurements, collected from an experimental building cell over a certain time period, the code is calibrated, effectively reducing the epistemic uncertainty affecting some code parameters (here albedo, thermal bridge factor and convective coefficient). Validation is conducted by testing the goodness of fit of the code with respect to field measures, and then by propagating the a posteriori parametric uncertainty through the code, yielding probabilistic forecasts of the average electric power delivered inside the cell over a given time period. To illustrate the benefits of the proposed Bayesian validation framework, we address the decision problem for an energy supplier offering a new type of contract, wherein the customer pays a fixed fee chosen in advance, based on an overall energy consumption forecast. According to Bayesian decision theory, we show how to choose such a fee optimally from the point of view of the supplier, in order to balance short-terms benefits with customer loyalty., Comment: 20 pages, 9 figures

Background Current evidence suggests that plant and animal proteins are intimately associated with specific large nutrient clusters that may explain part of their complex relation with cardiovascular health. We aimed at evaluating the association between specific patterns of protein intake with cardiovascular mortality. Methods We selected 81 337 men and women from the Adventist Health Study-2. Diet was assessed between 2002 and 2007, by using a validated food frequency questionnaire. Dietary patterns based on the participants’ protein consumption were derived by factor analysis. Cox regression analysis was used to estimate multivariate-adjusted hazard ratios (HRs) adjusted for sociodemographic and lifestyle factors and dietary components. Results There were 2276 cardiovascular deaths during a mean follow-up time of 9.4 years. The HRs for cardiovascular mortality were 1.61 [98.75% confidence interval (CI), 1.12 2.32; P-trend Conclusions Associations between the ‘Meat’ and ‘Nuts & Seeds’ protein factors and cardiovascular outcomes were strong and could not be ascribed to other associated nutrients considered to be important for cardiovascular health. Healthy diets can be advocated based on protein sources, preferring low contributions of protein from meat and higher intakes of plant protein from nuts and seeds.

Probabilistic forecasting aims at producing a predictive distribution of the quantity of interest instead of a single best guess point-wise estimate. With regard to water flow forecasts, the two main sources of uncertainty stem from unknown future rainfall and temperature (input error, i.e., meteorological uncertainty) and from the inadequacy of the deterministic simulator mimicking the rainfall-runoff (RR) transformation (hydrological uncertainty or RR error). These two sources of uncertainty can be dealt with separately and only the latter will be considered here. Only hydrological uncertainty is at stake when recorded meteorological data (instead of meteorological forecasts) are used as inputs to feed the RR simulator (RRS) for probabilistic predictions. The predictive performance of the RRS may strongly depend on the hydrological regimes: rapid flood variations induce large errors of anticipation but a series of dry events will translate into a much more smoother sequence of river levels due to the easily predictable behavior of the soil reservoir emptying. Consequently, a model with several regimes adapted to different error structures appears as a solution to cope with the issue of unstationary predictive variance. The river regime is modeled as a latent variable, the distribution of which is based on additional outputs of the RRS to be selected. Inference is performed by the EM algorithm with both steps leading to explicit analytic expressions. Asymptotic confidence regions for the estimates are provided within the same EM framework. Model selection is also performed, including the length of the model memory as well as the choice of explanatory variables for the latent regimes. The model is applied to a series of water flow forecasts routinely issued by two hydroelectricity producers in France and in Qu,bec and compared with their present operational forecasting methods.

Summary Modelling relationships between individuals is a classical question in social sciences and clustering individuals according to the observed patterns of interactions allows us to uncover a latent structure in the data. The stochastic block model is a popular approach for grouping individuals with respect to their social comportment. When several relationships of various types can occur jointly between individuals, the data are represented by multiplex networks where more than one edge can exist between the nodes. We extend stochastic block models to multiplex networks to obtain a clustering based on more than one kind of relationship. We propose to estimate the parameters—such as the marginal probabilities of assignment to groups (blocks) and the matrix of probabilities of connections between groups—through a variational expectation–maximization procedure. Consistency of the estimates is studied. The number of groups is chosen by using the integrated completed likelihood criterion, which is a penalized likelihood criterion. Multiplex stochastic block models arise in many situations but our applied example is motivated by a network of French cancer researchers. The two possible links (edges) between researchers are a direct connection or a connection through their laboratories. Our results show strong interactions between these two kinds of connection and the groups that are obtained are discussed to emphasize the common features of researchers grouped together.

Introduction et but de l’etude Le controle de la consommation de proteines, qui representent une composante du regime strictement indispensable a la survie, est un element cle dans les processus de controle de choix et de l’ingestion alimentaire chez l’humain. On ignore cependant les criteres decisionnels, sensoriels notamment, sur lesquels nous basons nos choix alimentaires en vue d’assurer l’adequation de notre regime a nos besoins proteiques. Un regime deficient en proteines est connu pour entrainer une recherche non consciente de proteines alimentaires. En comparant les choix d’une cohorte de sujets en deficience proteique a ceux des meme sujets consommant les proteines a un niveau habituel, nous avons cherche a identifier les criteres implicites d’evaluation de la teneur en proteine des aliments. Materiel et methodes Vingt-trois sujets sains (dont 13 femmes) ont ete recrutes, âges entre 18 et 35 ans, l’IMC compris entre 18 et 25 kg/m2 et ne suivant pas de regime particulier. Ces sujets ont suivi deux phases d’intervention successives, durant lesquelles ils ont consomme des regimes apportant soit 0,5 g de proteine/kg/j, soit 1,4 g/kg/j. Les deux regimes etaient isoenergetiques et la variation de la teneur en proteines etait compensee par une variation de la teneur en lipides et en glucides. Chaque periode de regime a dure 4j, les periodes etant separees par 10 j ou les sujets ont retrouve leur alimentation habituelle. A la fin de chaque periode de regime, les sujets ont complete un test lors duquel il leur a ete demande de choisir l’aliment prefere parmi des paires d’images d’aliments differant par leur teneur en proteines, taille de portion, ou qualites sensorielles (total de 153 comparaisons). Le score de preference a ete estime pour les differents aliments evalues a l’aide des modeles de Bradley-Terry. L’effet du regime sur ces scores de preference, ainsi que son interaction avec les qualites nutritionnelles et sensorielles des aliments, a ete ensuite etudie. Resultats L’analyse des tests de preference a revele un effet significatif du regime sur les preferences alimentaires des sujets (p = 0,0047). En particulier, le facteur regime a montre une interaction significative avec le facteur taille de portion (p = 0,0002) : en effet, le poids accorde au facteur taille de portion etait significativement plus important pour les preferences observees apres regime a teneur en proteines plus basse. Les autres facteurs relatifs aux qualites des aliments, a savoir, la teneur en proteines des aliments et leur gout sale ou sucre, n’ont pas manifeste d’interaction avec le facteur regime (p = 0,11 et p = 0,14, respectivement). Conclusion Cette etude a permis de mettre en evidence des modifications de preferences induites par une diminution de consommation proteique, et ce des 4 j de regime reduit en proteines. Les sujets soumis a une restriction proteique semblent orienter leurs choix vers des tailles de portion plus importantes. Tandis qu’une telle strategie pourrait effectivement augmenter les apports proteiques, elle pose des problemes potentiels de depassement de consommation calorique. Il convient de s’interroger sur les mecanismes causant ces choix, afin d’etudier la possibilite d’orienter les consommateurs vers des aliments plus riches en proteines par exemple.

International audience; Introduction et but de l'étude : L'association entre protéines végétales, protéines animales et maladies cardiovasculaires fait débat. Les données actuelles suggèrent un effet protecteur des protéines végétales, mais il est difficile d'isoler le rôle spécifique des protéines. En effet, les sources protéiques animales et végétales sont diverses et apportent en quantités variables un large panel d'autres nutriments qui pourraient également agir favorablement ou défavorablement sur la santé cardiovasculaire. Cette étude a pour but d'évaluer les relations entre protéines et mortalité cardiovasculaire, tout en considérant la diversité des sources protéiques mais aussi le régime alimentaire dans sa globalité. Matériel et méthodes : A partir des données de l'étude de cohorte Adventist Heath Study 2, 81 337 participants exempts de maladies cardiovasculaires ont été retenus pour l'analyse. Les apports en différents types de protéines animales et végétales ont été estimés. Des profils de consommation en protéines-caractérisés par la contribution relative des groupes protéiques à l'apport total en protéines-ont été identifiés au moyen d'une analyse factorielle exploratoire. Le lien entre les profils protéiques et la mortalité cardiovasculaire a été évalué par des modèles à risques proportionnels de Cox. Résultats et Analyse statistique : La mortalité cardiovasculaire n'était pas associée à la consommation totale de protéines animales et végétales après ajustement mutuel et ajustement par les facteurs de confusion potentiels. En revanche, les cinq profils protéiques types extraits par analyse factorielle ont révélés des associations contrastées et fortes avec la mortalité cardiovasculaire. Après ajustement pour les facteurs de confusion potentiels, le profil protéique caractérisé par de forts apports en protéines provenant de la viande ('Viande') était positivement associé à la mortalité cardiovasculaire (HR Q5vsQ1 1.61, 98.75% CI, 1.12–2.32; P-trend

This paper looks at the effect of identifying alters as direct competitors on their selection as advisors. We differentiate between two kinds of competition: cut-throat vs friendly. We argue that, unlike cut-throat competition, friendly competition makes collective learning possible as a social process: when knowledge is built in interactions that are able to mitigate the negative effects of status competition and take place in homophilous social niches; and when the quality of this knowledge is guaranteed by members with epistemic status in these niches. Social niches and status facilitate advice seeking and collective learning because advice seeking between direct competitors is not obvious even when members have a common interest in sharing advice – a learning-related dilemma of collective action. We apply this reasoning to a network dataset combining identification of direct competitors and selection of advisors among the elite of cancer researchers in France. We use a procedure of multiplex stochastic block-modeling designed by Barbillon et al. (2015) to measure the effect of these identifications of direct competitors on the structure of the advice network. Results obtained with this dataset support our theory.

International audience; ABSTRACTWe present a detailed ethnographic case study of sorghum seed acquisitions in a smallholder farming society in northern Cameroon. The effects of variability in household demographics and socioeconomic status on observed patterns of seed provisioning are explored alongside other variables such as age and gender. Our data set comprised 223 seed acquisition events. Independence tests (Pearson’s chi-square and Fisher’s exact tests) were carried out to test for significant dependencies between individual- or household-level characteristics and properties of seed acquisition events (categories of seed source, social relationship of exchange, and type of landrace). Results indicate that wealth is a structuring factor of the local seed circulation network in as much as it is highly correlated with household composition and size. Members from wealthy households benefit from a more diverse set of seed sources. Their greater number of coresidents and the importance of intrahousehold dynamics of seed transactions also play a role in making wealthy farmers more seed secure than others. The methodological implications of our findings indicate that when documenting seed exchange networks, the collection of data through a single informant or the undertaking of social network analyses at the household level may induce important biases.

International audience; AbstractAmong soil processes governing the clay size fraction distribution with depth, illuviation is not only widespread but also particularly poorly understood. Using a micromorphological approach, this study aims at i) quantifying the intensity of illuviation independently of all other competing soil processes, ii) testing the sensitivity of illuviation to different land uses, and iii) assessing the relative contribution of illuviation to the genesis of textural differentiation. Two Luvisols developed from loess in Northern France were selected: the first under a deciduous forest and the second under conventional agricultural management, both with no change in land use for at least 100 years. In addition to classical mass balance calculations, clay illuviation features such as limpid and dusty clay coatings were used as diagnostic features of the illuviation process and were quantified by a point-counting approach in large thin sections. In the two Luvisols under study illuviation was found to be responsible for 75% and 86% of the textural contrast, respectively in the cultivated soil and the forested one. Illuviation was thus by far the dominant factor causing textural differentiation, even if accessory processes involving the clay size fraction were also at play such as lithological discontinuity, clay neoformation or biological and physical reworking. The recognition of dusty clay coatings considered as an indicator of cultivation in the soil under forest underlines the sensitivity of illuviation to past periods of cultivation and the legacy of anthropedogenesis on the present soil properties. A qualitative change in the nature of the translocated particles without significant changes in the intensity of illuviation was identified in the soil under cultivation in comparison with the soil under forest. These results support the conclusion that illuviation is an active process in soils under present climatic conditions and must no more be considered as a fossil soil process.

WOS:000367701100004; International audience; Background: Early-life nutrition has a programming effect on later metabolic health; however, the impact of exposure to a high-protein (HP) diet is still being investigated. Objective: This study evaluated the consequences on pup phenotype of an HP diet during gestation and lactation and after weaning. Methods: Wistar rat dams were separated into 2 groups fed an HP (55% protein) or normal protein (NP) (control; 20% protein) isocaloric diet during gestation, and each group subsequently was separated into 2 subgroups that were fed an HP or NP diet during lactation. After weaning, male and female pups from each mother subgroup were separated into 2 groups that were fed either an NP or HP diet until they were 6 wk old. Measurements included weight, food intake, body composition, blood glucose, insulin, glucagon, leptin, insulin-like growth factor I, and lipids. Results: Feeding mothers the HP diet during gestation or lactation induced lower postweaning pup weight (gestation diet x time, P \textless 0.0001; lactation diet x time, P \textless 0.0001). Regardless of dams' diets, pups receiving HP compared with NP diet after weaning had 7% lower weight (NP, 135.0 +/- 2.6 g; HP, 124.4 +/- 2.5 g; P \textless 0.0001), 16% lower total energy intake (NP, 777 +/- 14 kcal; HP, 649 13 kcal; P \textless 0.0001) and 31% lower adiposity (P \textless 0.0001). Pups receiving HP compared with NP diet after weaning had increased blood glucose, insulin, and glucagon when food deprived (P \textless 0.0001 for all). The HP compared with the NP diet during gestation induced higher blood glucose in food-deprived rats (NP, 83.2 +/- 2.1 mg/dL; HP, 91.2 +/- 2.1 mg/dL; P = 0.046) and increased plasma insulin in fed pups receiving the postweaning NP diet (gestation diet x postweaning diet, P = 0.02). Conclusion: Increasing the protein concentration of the rat dims' diet during gestation, and to a lesser extent during lactation, and of the pups' diet after weaning influenced pup phenotype, including body weight, fat accumulation, food intake, and glucose tolerance at 6 wk of age.

International audience; Complex biological processes are usually experimented along time among a collection of individuals. Longitudinal data are then available and the statistical challenge is to better understand the underlying biological mechanisms. The standard statistical approach is mixed-effects model, with regression functions that are now highly-developed to describe precisely the biological processes (solutions of multi-dimensional ordinary differential equations or of partial differential equation). When there is no analytical solution, a classical estimation approach relies on the coupling of a stochastic version of the EM algorithm (SAEM) with a MCMC algorithm. This procedure needs many evaluations of the regression function which is clearly prohibitive when a time-consuming solver is used for computing it. In this work a meta-model relying on a Gaussian process emulator is proposed to replace this regression function. The new source of uncertainty due to this approximation can be incorporated in the model which leads to what is called a mixed meta-model. A control on the distance between the maximum likelihood estimates in this mixed meta-model and the maximum likelihood estimates obtained with the exact mixed model is guaranteed. Eventually, numerical simulations are performed to illustrate the efficiency of this approach.