Éditeur(s) : HAL CCSD Springer Verlag
Résumé : International audience
Seasonal climate forecasts mainly rely on the atmospheric sensitivity to its lower boundary conditions and on their own predictability. Besides sea surface temperature (SST), soil moisture (SM) may be an additional source of climate predictability particularly during boreal summer in the mid-latitudes. In this work, we investigate the role of SM initial conditions on near-surface climate predictability during ten boreal summer seasons using three complementary ensembles of AMIP-type simulations performed with the Arpège-Climat atmospheric general circulation model. First we have conducted an assessment of the SM predictability itself through a comparison of simple empirical SM models with Arpège-Climat. The statistical and dynamical models reveal similar SM prediction skill patterns but the Arpège-Climat reaches higher scores suggesting that it is at least suitable to explore the influence of SM initialization on atmospheric predictability. Then we evaluate the relationships between SM predictability and some near surface atmospheric predictability. While SM initialization obviously improves the predictability of land surface evaporation, it has no systematic influence on the precipitation and near surface temperature skills. Nevertheless, the summer hindcast skill is clearly improved during specific years and over certain regions (mainly north America and eastern Europe in the Arpège-Climat model), when and where the SM forcing is sufficiently widespread and strong. In this case, a significant impact is also found on the occurrence of heat waves and heavy rains, whose predictability at the seasonal timescale is a crucial challenge for years to come.
ISSN: 0930-7575

meteo-00346948
https://hal-meteofrance.archives-ouvertes.fr/meteo-00346948
DOI : 10.1007/s00382-008-0375-9

Éditeur(s) : HAL CCSD
Résumé : International audience
In this work, we have led an analysis of the error of different global solar radiation prediction models according to the global solar radiation variability. Different predictions models where performed such as machine learning techniques (Neural Networks, Gaussian processes and support vector machines) in order to forecast the Global Horizontal solar Irradiance (GHI). We also include in this study a simple linear autoregressive (AR) model as well as two naive models based on persistence of the GHI and persistence of the clear sky index (denoted herein scaled persistence model). The models are calibrated and tested with data from three 3 French islands: Corsica (42.15°N ; 9.08°E), Guadeloupe (16.25°N ; 61.58°W) and Reunion (21.15°S ; 55.5°E). Guadeloupe and Reunion are located in a subtropical climatic zone whereas Corsica is in a tempered climatic zone. Hence the global solar radiation variation differs significantly. The output error of the different models was quantified by the nRSME. In order to quantify the influence of the global solar radiation variability on the forecasting models error we performed a classification of typical days according to different typical days. Each class of typical day is defined by a variation of global solar radiation rate. For each class and for each location, the selected forecasting models where performed and the error was quantified. With this analysis a global solar radiation forecasting models can be selected according to the location, the global solar radiation fluctuations and hence the meteorological conditions. INTRODUCTION Large and frequent variations of solar radiation can be observed in tropical climates with amplitudes reaching 800 W/m² and occurring within a short time interval, from few seconds to few minutes, according to the geographical location. Such fluctuations can be due for example to the dynamic of clouds which can be very complex and depend on cloud type, size, speed and spatial distribution and, more generally, due to some specific local meteorological conditions. Thus, the solar energy forecasting, a process used to predict the amount of solar energy available in the current and near terms, might be a difficult task. Some of the best predictors found in literature are Autoregressive and moving average (ARMA) [5,7,8], Bayesian inferences [9,10], Markov chains [11], k-Nearest-Neighbors predictors [12] or artificial intelligence techniques as the Artificial Neural Network (ANN) [9-11]. Although these methodologies are potentially good in many areas, we observed in our previous studies on global radiation prediction [9,13,14] that the simple model based on the persistence of the clear sky index gives often very good results with acceptable errors [15] for short term forecasting time horizon (<= 1 hour). The goal of this paper is to determinate the influence of solar radiation variability regarding different classes of days on the expected error provided by different forecasting methods that the modeller can possibly implement. The paper is organized as follow: Section 2 describes the data we have used. Section 3 exposes the classification methodology and the results obtained for the three studied locations. In the two following sections, the forecasting methods are exposed and then 3 the errors on the forecasting results for each location and for each class are exposed.
The Third Southern African Solar Energy Conference (SASEC2015)
Kruger National Park, South Africa

Droits : info:eu-repo/semantics/OpenAccess
hal-01099487
https://hal.archives-ouvertes.fr/hal-01099487
https://hal.archives-ouvertes.fr/hal-01099487/document
https://hal.archives-ouvertes.fr/hal-01099487/file/article%20SASEC.pdf

Éditeur(s) : HAL CCSD Nature Publishing Group
Résumé : International audience
Tropical cyclone (TC) waves can severely damage coral reefs. Models that predict where to find such damage (the 'damage zone') enable reef managers to: 1) target management responses after major TCs in near-real time to promote recovery at severely damaged sites; and 2) identify spatial patterns in historic TC exposure to explain habitat condition trajectories. For damage models to meet these needs, they must be valid for TCs of varying intensity, circulation size and duration. Here, we map damage zones for 46 TCs that crossed Australia's Great Barrier Reef from 1985–2015 using three models – including one we develop which extends the capability of the others. We ground truth model performance with field data of wave damage from seven TCs of varying characteristics. The model we develop (4MW) out-performed the other models at capturing all incidences of known damage. The next best performing model (AHF) both under-predicted and over-predicted damage for TCs of various types. 4MW and AHF produce strikingly different spatial and temporal patterns of damage potential when used to reconstruct past TCs from 1985–2015. The 4MW model greatly enhances both of the main capabilities TC damage models provide to managers, and is useful wherever TCs and coral reefs co-occur.
ISSN: 2045-2322

Droits : http://creativecommons.org/licenses/by/
hal-01330362
https://hal-univ-perp.archives-ouvertes.fr/hal-01330362
https://hal-univ-perp.archives-ouvertes.fr/hal-01330362/document
https://hal-univ-perp.archives-ouvertes.fr/hal-01330362/file/srep26009.pdf
DOI : 10.1038/srep26009

Éditeur(s) : HAL CCSD
Résumé : International audience
Semi-volatile organic compounds (SVOCs) partition indoors between the gas phase, airborne particles, settled dust, and other surfaces. Unknown concentrations of SVOCs in the gas phase (Cg) can be predicted from their measured concentrations in airborne particles. In previous studies, the prediction of Cg depended largely on choosing a specific equation for the calculation of the particle/gas partition coefficient. Moreover, the prediction of Cg is frequently performed at a reference temperature rather than the real indoor temperature. In this paper, a probabilistic approach based on Monte Carlo simulation was developed to predict the distribution of SVOCs' Cg from their concentrations in airborne particles at the target indoor temperature. Moreover, the distribution of the particle/gas partition coefficient of each SVOC at the target temperature was used. The approach was validated using two measured datasets in the literature: the predicted Cg from concentrations measured in airborne particles and the measured Cg were generally of the same order of magnitude. The distributions of the Cg of 66 SVOCs in the French housing stock were then predicted. The SVOCs with the highest median Cg, ranging from 1 ng/m3 to > 100 ng/m3, included 8 phthalates (DEP, DiBP, DBP, DEHP, BBP, DMP, DiNP, and DMEP), 4 polycyclic aromatic hydrocarbons (fluorene, phenanthrene, fluoranthene, and anthracene), 2 alkylphenols (4-tert-butylphenol and 4-tert-octylphenol), 2 synthetic musks (galaxolide and tonalide), tributyl phosphate, and heptachlor. The nationwide, representative, predicted Cg values of SVOCs are frequently of the same order of magnitude in Europe and North America, whereas these Cg values in Chinese and Indian dwellings and the Cg of polybrominated diphenyl ethers in U.S. dwellings are generally higher. © 2016 Elsevier B.V.
Science of the Total Environment

hal-01467547
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01467547
DOI : 10.1016/j.scitotenv.2016.10.074
PUBMED : 27788447

Éditeur(s) : HAL CCSD Wiley
Résumé : 1. Functional traits are posited to explain interspecific differences in performance, but these relationships are difficult to describe for long-lived organisms such as trees, which exhibit strong ontogenetic changes in demographic rates. Here, we use a size-dependent model of tree growth to test the extent to which of 17 functional traits related to leaf and stem economics, adult stature and seed size predict the ontogenetic trajectory of tree growth.[br/] 2. We used a Bayesian modelling framework to parameterize and contrast three size-dependent diameter growth models using 16 years of census data from 5524 individuals of 50 rain forest tree species: a size-dependent model, a size-dependent model with species-specific parameters and a size-dependent model based on functional traits.[br/] 3. Most species showed clear hump-shaped ontogenetic growth trajectories and, across species, maximum growth rate varied nearly tenfold, from 0.58 to 5.51 mm year(-1). Most species attained their maximum growth at 60% of their maximum size, whereas the magnitude of ontogenetic changes in growth rate varied widely among species.[br/] 4. The Trait-Model provided the best compromise between explained variance and model parsimony and needed considerably fewer parameters than the model with species terms.[br/] 5. Stem economics and adult stature largely explained interspecific differences in growth strategy. Maximum absolute diameter growth rates increased with increasing adult stature and leaf delta(13)C and decreased with increasing wood density. Species with light wood had the greatest potential to modulate their growth, resulting in hump-shaped ontogenetic growth curves. Seed size and leaf economics, generally thought to be of paramount importance for plant performance, had no significant relationships with the growth parameters.[br/] 6. Synthesis. Our modelling approach offers a promising way to link demographic parameters to their functional determinants and hence to predict growth trajectories in species-rich communities with little parameter inflation, bridging the gap between functional ecology and population demography.
ISSN: 0022-0477

hal-01032387
https://hal.archives-ouvertes.fr/hal-01032387
DOI : 10.1111/j.1365-2745.2011.01883.x

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
ABSTRACT: BACKGROUND: Climate change poses unprecedented challenges, ranging from global and local policy challenges to personal and social action. Heat-related deaths are largely preventable, but interventions for the most vulnerable populations need improvement. Therefore, the prior identification of high risk areas at the community level is required to better inform planning and prevention. We aimed to demonstrate a simple and flexible conceptual framework relying upon satellite thermal data and other digital data with the goal of easily reproducing this framework in a variety of urban configurations. RESULTS: The study area encompasses Rennes, a medium-sized French city. A Landsat ETM + image (60 m resolution) acquired during a localized heatwave (June 2001) was used to estimate land surface temperature (LST) and derive a hazard index. A land-use regression model was performed to predict the LST. Vulnerability was assessed through census data describing four dimensions (socio-economic status, extreme age, population density and building obsolescence). Then, hazard and vulnerability indices were combined to deliver a heatwave health risk index. The LST patterns were quite heterogeneous, reflecting the land cover mosaic inside the city boundary, with hotspots of elevated temperature mainly observed in the city center. A spatial error regression model was highly predictive of the spatial variation in the LST (R2 = 0.87) and was parsimonious. Three land cover descriptors (NDVI, vegetation and water fractions) were negatively linked with the LST. A sensitivity analysis (based on an image acquired on July 2000) yielded similar results. Southern areas exhibited the most vulnerability, although some pockets of higher vulnerability were observed northeast and west of the city. The heatwave health risk map showed evidence of infra-city spatial clustering, with the highest risks observed in a north--south central band. Another sensitivity analysis gave a very high correlation between 2000 and 2001 risk indices (r = 0.98, p < 10-12). CONCLUSIONS: Building on previous work, we developed a reproducible method that can provide guidance for local planners in developing more efficient climate impact adaptations. We recommend, however, using the health risk index together with hazard and vulnerability indices to implement tailored programs because exposure to heat and vulnerability do not require the same prevention strategies.
ISSN: 1476-072X

inserm-00762303
http://www.hal.inserm.fr/inserm-00762303
http://www.hal.inserm.fr/inserm-00762303/document
http://www.hal.inserm.fr/inserm-00762303/file/1476-072X-11-38.pdf
DOI : 10.1186/1476-072X-11-38

Éditeur(s) : HAL CCSD springer
Résumé : International audience
In this paper, we address the problem of flood prediction in complex situations. We present an original solution in order to achieve the main goals of accuracy, flexibility and readability. We propose the SM2D modular data driven approach that provides predictive models for each sub-process of a global hydrological process. We show that this solution improves the predictive accuracy regarding a global approach. The originality of our proposition is threefold: (1) the predictive model is defined as a set of aggregate variables that act as classifiers, (2) an evolutionary technique is implemented to find best juries of such classifiers and (3) the flood process complexity problem is addressed by searching for sub-models on sub-processes identified partly by spatial criteria. The solution has proved to perform well on flash flood phenomena of tropical areas known to be hardly predictable. It was indeed successfully applied on a real caribbean river dataset after both preprocessing and preliminary analysis steps presented in the paper.
Lecture notes in computer science
Singapour, Singapore

hal-00878780
https://hal.archives-ouvertes.fr/hal-00878780
DOI : 10.1007/978-3-642-40897-7_13

Résumé : Les travaux de recherche présentés dans ce mémoire s'inscrivent dans la lignée des approches de modélisation hydrologiques prédictives dirigées par les données. Nous avons particulièrement développé leur application sur le contexte difficile des phénomènes de crue éclairs caractéristiques des bassins versants de la région Caraïbe qui pose un dé fi sé.curi taire. En envisageant le problème de la prévision de crues comme un problème d'optimisation combinatoire difficile nous proposons d'utiliser la notion de métaneuristiques, à travers les algorithmes évolutionnaire notamment pour leur capacité à parcourir efficacement de grands espaces de recherche et fi fournir des solutions de bOlIDe qualité en des temps d'exécution raisonnables. Nous avons présenté l'approche de prédiction AV2D : Aggregate Variable Data Driven dom le concept central est la notion de variable agrégée. L'idée sous-jacente à ce concept est de considérer le pouvoir prédictif de nouvelles variables définies comme le résultat de fonctions tatistiques, dites d'agrégation calculées sur de donnée' correspondant à des périodes de temps précédent uo événem nt à prédire. Ces variable sont caractérisées par des ensembles de paramètres correspondant a leur pJ:opriétés. Nous avons imroduitle variables agrégées hydrométéorologiques permettant de répondre au problème de la classification d événements hydrologiques. La complexité du parcours de l'espace de recherche engendré par les paramètres définissant ces variables a été prise en compte grâce à la njse en oeuvre d'un algorithme évolutionnaire particulier dont les composants ont été spécifiquement définis pour ce problème. Nous avons montré, à travers une étude comparative avec d'autres approches de modélisation dirigées par les données, menée sur deux cas d'études de bassins versant caribéens, que l'approche AV2D est particulièrement bien adaptée à leur contexte. Nous étudions par la suite les bénéfices offerts par les approches de modélisation hydrologiques modulaires dirigées par les données, en définissant un procédé de division en sous-processus prenant en compte les caractéristiques paniculières des bassins versants auxquels nous nous intéressons. Nou avons proposé une extension des travaux précédents à travers la définition d'une approche de modélisation modulaire M2D: Spatial Modular Data Driven, consistant à considérer des sous-processus en divisant l'ensemble des exemples à classifier en sous-ensembles correspondant à des comportements hydrologiques homogènes. Nous avons montré à travers une étude comparative avec d autres approches dU'igées par les données mises en oeuvre sur les mêmes sous-ensembles de données que celte approche permet d améliorer les résultats de prédiction particulièrement à coun Lenne. Nous avons enfin proposé la modélisation d un outil de pi
The work presented in this thesis is in the area of data-driven hydrological modeling approaches. We particularly investigared their application on the difficult problem of flash flood phenomena typically observed in Caribbean watersheds. By considering the problem of flood prediction as a combinatorial optimization problem, we propose to use the notion of Oleraheuristics, through evolutionary algorithms, especially for their capacity ta visit effjciently large search space and to provide good solutions in reasonable execution times. We proposed the hydrological prediction approach AV2D: Aggregate Variable Data Driven which central concept is the notion of aggregate variable. The underlying idea of this [concept is to consider the predictive power of new variables defined as the results of statistical functions, called aggregation functions, computed on data corresponding ta time periods before an event ta predict. These variables are characterized by sets of parameters corresponding ta their specifications. We introduced hydro-meteorological aggregate variables allowing ta address the classification problem of hydrological events. We showed through a comparative study on two typical caribbean watersheds, using several common data driven modelling techniques that the AV2D approach is panicul.rly weil fitted ta the studied context. We also study the benefits offered by modulaI' approaches through the definition of the SM2D: Spatial Modular DataDriven approach, consisting in considering sub-processes partly defined by spatial criteria. We showed that the results obtained by the AV2D on these sub-processes allows to increase the performances particularly for short term prediction. Finally we proposed the modelization of a generic control tool for hydro-meteorological prediction systems, H2FCT: Hydro-meteorological Flood Forecasting Control 1'001

http://www.theses.fr/2013AGUY0673/document

Résumé : La forte variabilité de la vitesse du vent fait que l'énergie produite par un parc éolien n'est pas constante dans le temps. Le gestionnaire ne peut donc pas dimensionner son réseau électrique en prenant intégralement ce type de production en compte. L' une des solutions préconisées pour permettre le développement de l' éolien et son intégration avec une plus grande sureté aux réseaux, est de développer et d'améliorer les outils de prévisions. Le travail de thèse consiste à améliorer les performances d'un outil de prédiction basé sur les réseaux de neurones bayesiens, permettant la prédiction de la puissance à très court terme . Le prédicteur fonctionne notamment par J'ajustement de paramètres, certain se détermine « automatiquement » via le mécanisme des réseaux de neurones bayesiens d' autres, que nous nommerons paramètres temporels, sont à l' appréciation de l'utilisateur. Le travail mené consiste à établir un protocole pour la fixation de ces paramètres tout en améliorant les performances du prédicteur . Nous avons donc décidé de conditionner leurs valeurs en fonction de la variabilité des séquences de puissance précédent l'instant de prévision. Tout d'abord nous avons classifié des séquences de puissance en fonction de leurs coefficients de variation en appliquant la méthode des C-moyennes floues. Puis, chaque classe formée a été testée sur plusieurs valeurs de paramètres, les valeurs associées aux meilleures prédictions ont été retenues. Enfin, ces résultats couplés au formalisme des Chaines de Markov, par le biais de la matrice de transition , ont perm is d'obtenir des taux d'amélioration par rapport à la persistance allant de 7,73 à 23,22 % selon l'horizon de prédiction considéré
The high variability of the wind speed has for conse quences that the energy produced by a wind farm is not constant over time. Therefore, the manager can't size the electrical network by takin g into account this type of production. One solution advocated for the development of wind energy and its integrati on with greater security at network, is to develop and improve fore casting tools. The thesi s objective is to improve the performance of a predi ction tool based on Bayesian neural networks, allowing the predi ction of wind power for short timescales. The predictor works, in part icular by the adjustment of parameters, sorne is determined "automatically" through the mechan ism of neural networks Bayesian other , which we cali temporal parameters are at the discretion of the user. The work involves establishing a protocol for the determination of these parameters and improving the performance of the predictor. So, we decided to condition their values depending on the sequence variability of wind power previous the moment of the forecast. First we classified sequences of power according to their coefficients of variation using the method of fuzzy C-means. Then, each formed class was tested for several parameters values, the values associated with the best predictions were selected. Finally , these result s coupled with the formalism of Markov chains , through the transition matrix allowed to obtain rates of improvement over the persistence ranging from 7.73 to 23.22 % depending on the prediction horizon considered

http://www.theses.fr/2013AGUY0679/document

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : International audience
Challenging evaluation of tropical forest biodiversity requires the reporting of taxonomic diversity but also the systematic characterization of wood properties in order to discover new promising species for timber industry. Among wood properties, the dimensional stability is regarded as a major technological characteristic to validate whether a wood species is adapted to commercial uses. Cell structure and organization are known to influence the drying shrinkage making wood density and microfibrils angle markers of choice to predict wood dimensional stability. On the contrary the role of wood extractive content remains unclear. This work focuses on the fast-growing tropical species Bagassa guianensis and we report herein a correlation between heartwood drying shrinkage and extractive content. Chemical extractions and shrinkage experiments were performed on separate wood twin samples to better evaluate correctly how secondary metabolites influence the wood shrinkage behaviour. Extractive content were qualitatively and quantitatively analysed using HPLC and NMR spectroscopy. We found that B guianensis heartwood has a homogeneous low shrinkage along its radius that could not be explained only by its basic density. In fact the low drying shrinkage is correlated to the high extractive content and a corrected model to improve the prediction of wood dimensional stability is presented. Additionally NMR experiments conducted on sapwood and heartwood extracts demonstrate that secondary metabolites biosynthesis occurs in sapwood thus revealing B. guianensis as a Juglans-Type heartwood formation. This work demonstrates that B. guianensis, a fast-growing species associated with high durability and high dimensional stability, is a good candidate for lumber production and commercial purposes.
ISSN: 1932-6203

hal-01392725
https://hal.archives-ouvertes.fr/hal-01392725
https://hal.archives-ouvertes.fr/hal-01392725/document
https://hal.archives-ouvertes.fr/hal-01392725/file/2016_BossuBeaucheneEstevezDuplaisClair_PlosOne_WoodDimentionalStabilityAndSecondaryMetabolites.pdf
DOI : 10.1371/journal.pone.0150777.s005

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : International audience
The six-minute walk test is a well-established submaximal exercise reflecting the functional status and the clinical severity of sickle cell patients. The aim of the present cross-sectional study was to investigate the biological determinants of the six-minute walk test performance in children with sickle cell anemia. Hematological and hemorheological parameters, pulmonary function and the six-minute walk test performance were determined in 42 children with sickle cell anemia at steady state. The performance during the six-minute walk test was normalized for age, sex and height and expressed as percentage of the predicted six-minute walk distance. We showed that a high level of anemia, a low fetal hemoglobin expression and low red blood cell deformability were independent predictors of a low six-minute walk test performance. This study describes for the first time the impact of blood rheology in the six-minute walk test performance in children with sickle cell anemia.
ISSN: 1932-6203

inserm-00922110
http://www.hal.inserm.fr/inserm-00922110
http://www.hal.inserm.fr/inserm-00922110/document
http://www.hal.inserm.fr/inserm-00922110/file/2013_Waltz_-_Hematological_and_hemorheological_determinants_6_minutes.pdf
DOI : 10.1371/journal.pone.0077830

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Background: Ameloblastin (AMBN) is a phosphorylated, proline/glutamine-rich protein secreted during enamel formation. Previous studies have revealed that this enamel matrix protein was present early in vertebrate evolution and certainly plays important roles during enamel formation although its precise functions remain unclear. We performed evolutionary analyses of AMBN in order to (i) identify residues and motifs important for the protein function, (ii) predict mutations responsible for genetic diseases, and (iii) understand its molecular evolution in mammals. Results: In silico searches retrieved 56 complete sequences in public databases that were aligned and analyzed computationally. We showed that AMBN is globally evolving under moderate purifying selection in mammals and contains a strong phylogenetic signal. In addition, our analyses revealed codons evolving under significant positive selection. Evidence for positive selection acting on AMBN was observed in catarrhine primates and the aye-aye. We also found that (i) an additional translation initiation site was recruited in the ancestral placental AMBN, (ii) a short exon was duplicated several times in various species including catarrhine primates, and (iii) several polyadenylation sites are present. Conclusions: AMBN possesses many positions, which have been subjected to strong selective pressure for 200 million years. These positions correspond to several cleavage sites and hydroxylated, O-glycosylated, and phosphorylated residues. We predict that these conserved positions would be potentially responsible for enamel disorder if substituted. Some motifs that were previously identified as potentially important functionally were confirmed, and we found two, highly conserved, new motifs, the function of which should be tested in the near future. This study illustrates the power of evolutionary analyses for characterizing the functional constraints acting on proteins with yet uncharacterized structure.
ISSN: 1471-2148

Droits : http://creativecommons.org/licenses/by/
hal-01212816
http://hal.upmc.fr/hal-01212816
http://hal.upmc.fr/hal-01212816/document
http://hal.upmc.fr/hal-01212816/file/s12862-015-0431-0.pdf
DOI : 10.1186/s12862-015-0431-0

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
Malaria remains one of the most common vector-borne diseases in the world and the definition of novel control strategies can benefit from the modeling of transmission processes. However, data-driven models are often difficult to build, as data are very often incomplete, heterogeneous in nature and in quality, and/or biased. In this context, a knowledge-based approach is proposed to build a robust and general landscape-based hazard index for malaria transmission that is tailored to the Amazonian region. A partial knowledge-based model of the risk of malaria transmission in the Amazonian region, based on landscape features and extracted from a systematic literature review, was used. Spatialization of the model was obtained by generating land use and land cover maps of the cross-border area between French Guiana and Brazil, followed by computing and combining landscape metrics to build a set of normalized landscape-based hazard indices. An empirical selection of the best index was performed by comparing the indices in terms of adequacy with the knowledge-based model, intelligibility and correlation with P. falciparum incidence rates. The selected index is easy to interpret and successfully represents the current knowledge about the role played by landscape patterns in malaria transmission within the study area. It was significantly associated with P. falciparum incidence rates, using the Pearson and Spearman correlation coefficients (up to 0.79 and 0.75, respectively; p-value < 0.001), and the linear regression coefficient of determination (reaching 0.63; p-values < 0.001). This study establishes a spatial knowledge-driven, landscape-based hazard malaria index using remote sensing that can be easily produced on a regular basis and might be useful for malaria prediction, surveillance, and control. Keywords: remote sensing; land use and land cover; landscape metric; knowledge-based hazard modeling; malaria; cross-border area between French Guiana and Brazil
ISSN: 2072-4292

hal-01355255
https://hal.archives-ouvertes.fr/hal-01355255
https://hal.archives-ouvertes.fr/hal-01355255/document
https://hal.archives-ouvertes.fr/hal-01355255/file/010066865.pdf
IRD : fdi:010066865

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Endocrine disrupting chemicals and especially oestrogen receptor (ER) agonists have been extensively studied over the years due to their potential effects on sexual development and reproduction in vertebrates, notably fish. As ER agonists can exist as complex mixtures in the aquatic environment, evaluating the impact of combined exposure on oestrogenic effects has become increasingly important. Use of predictive models such as concentration addition (CA) and independent action (IA) has allowed assessment of combined estrogenic effects of complex multi-compound mixtures of ER agonists in various fish in vitro and in vivo experimental models. The present work makes use of a transgenic zebrafish strain, tg(cyp19a1b-GFP), which expresses the green fluorescent protein (GFP) under the control of the cyp19a1b (brain aromatase or aromatase B) gene to determine the oestrogenic potency of ER agonists alone or in mixtures. In these studies, tg(cyp19a1b-GFP) zebrafish embryos were exposed for four days (from one to five days post fertilization) to five different oestrogenic chemicals; 17α-ethinylestradiol (EE2), 17β-estradiol (E2), estrone (E1), bisphenol A (BPA) and 4-tert-octylphenol (OP), and three mixtures of up to four of these compounds. The mixture of BPA, OP and E2 was also tested with primary cultures of rainbow trout hepatocytes by analysing the ER-mediated induction of the oestrogenic biomarker vitellogenin in order to compare the performance of the two methods for assessing oestrogenic effects of complex mixtures. The three tested mixtures were predominantly acting in an additive manner on the expression of GFP. Additivity was indicated by the overlap of the 95% confidence interval of the concentration response curves for the observed data with the CA and IA prediction models, and model deviation ratios within a factor of two for a majority of the mixture concentrations. However, minor deviations determined as more than additive effects for the mixture of EE2, E1 and E2 and less than additive effects for the mixture of BPA, OP, EE2 and E1 were observed at the higher mixture concentrations tested. The successful prediction of additivity by CA and IA in tg(cyp19a1b-GFP) zebrafish embryos and deviations at high mixture concentrations seemed to correspond well to results obtained in the rainbow trout hepatocyte assay. The present results clearly show the usefulness of combining predictive modelling and use of in vitro bioassays for rapid screening of oestrogenic effects of complex mixtures and environmental samples.
ISSN: 0166-445X

hal-00877351
https://hal.archives-ouvertes.fr/hal-00877351
DOI : 10.1016/j.aquatox.2013.05.001
PUBMED : 23721851

Éditeur(s) : HAL CCSD
Résumé : 6 pages, 4 figures. In ICML 2013 Workshop on Machine Learning for Bioacoustics
International audience
The work presented in this paper is part of a global framework which long term goal is to design a wireless sensor network able to support the observation of a population of endangered birds. We present the first stage for which we have conducted a knowledge discovery approach on a sample of acoustical data. We use MFCC features extracted from bird songs and we exploit two knowledge discovery techniques. One that relies on clustering-based approaches, that highlights the homogeneity in the songs of the species. The other, based on predictive modeling, that demonstrates the good performances of various machine learning techniques for the identification process. The knowledge elicited provides promising results to consider a widespread study and to elicit guidelines for designing a first version of the automatic approach for data collection based on acoustic sensors.
ICML 2013 Workshop on Machine Learning for Bioacoustics
Atlanta, United States

hal-00840741
https://hal.archives-ouvertes.fr/hal-00840741
ARXIV : 1306.5349

Éditeur(s) : HAL CCSD Lavoisier
Résumé : National audience
Dans cette étude, nous présentons, une méthode permettant de découvrir, par le biais de variables agrégées et d’un algorithme génétique, des informations non connues a priori et utiles pour la prédiction de crues. Cette méthode se prête particulièrement bien aux données dans lesquelles des informations à propos d’un même élément sont dispersées à travers plusieurs lignes du jeu de données initial.
In this paper, we present an heuristic based approach for feature selection in the context of flood prediction. Features are complex variables that represent aggregate values. We apply a preprocessing method on data in order to elicit relevant information that could not be easily accessible initially because it is split through several lines of a dataset. A genetic algorithm is used in order to search for the features that may prove the best performances for flood prediction.
ISSN: 1633-1311

hal-01184510
https://hal.archives-ouvertes.fr/hal-01184510

Éditeur(s) : HAL CCSD Oxford University Press (OUP)
Résumé : International audience
STUDY QUESTION: Can protein biomarkers of the male genital tract be identified in human seminal plasma? SUMMARY ANSWER: We identified potential biomarkers for each of the organs participating in the secretions of the human seminal plasma. WHAT IS KNOWN ALREADY: The seminal plasma fulfills critical functions for fertility by providing spermatozoa with a protective milieu, promoting their final maturation and modulating the immune responsiveness of the female reproductive tract. It is also considered to be a promising source of biomarkers of male infertility and/or pathologies of the male genital tract. STUDY DESIGN, SIZE, DURATION: This study combines proteomic analyses of normal seminal plasma together with transcriptomic gene expression profiling of human healthy tissues. MATERIALS, SETTING, METHODS: Non-liquefied seminal plasma proteins from a healthy donor were prefractionated using two sequential Proteominer™ libraries. Eight subproteome fractions were collected, trypsin digested and subjected to three successive mass spectrometry analyses for peptide characterization. The list of identified proteins was compared with and merged with other available data sets of the human seminal plasma proteome. The expression of corresponding genes was then investigated using tissue transcriptome profiles to determine where, along the male reproductive tract, these proteins were produced. Finally, tissue specificity of a selected subset of biomarker candidates was validated on human tissues. MAIN RESULTS AND THE ROLE OF CHANCE: We first performed a proteomic analysis of the human seminal plasma and identified 699 proteins. By comparing our protein list with other previous proteomic data sets, we found that 2545 unique proteins have been described so far in the human seminal plasma. We then profiled their expression at the gene level and identified 83 testis, 42 epididymis, 7 seminal vesicle and 17 prostate candidate protein markers. For a subset of testis-specific candidates, i.e. TKTL1, LDHC and PGK2, we further validated their germ cell expression and demonstrated that such markers could distinguish between semen from fertile and infertile men. LIMITATIONS, REASONS FOR CAUTION: While some of the markers we identified are well-known tissue-specific products, further dedicated studies to validate the biomarker status of new candidates will be required. Additionally, whether or not the abundance of these proteins is indeed decreased in some specific pathological situations remains to be determined. WIDER IMPLICATIONS OF THE FINDINGS: Using an integrative genomics approach, we identified biomarker candidates for each of the organs participating in the seminal plasma production. In this study, we essentially focused on germ cell markers and their potential application for the diagnosis of male infertility. Other types of markers also deserve a focused attention given their potential predictive value for various reproductive disorders, notably for prostate cancers. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the Proteomics Core Facility at Biogenouest and was funded by Conseil Régional de Bretagne, IBiSA and Agence de la Biomédecine grants. The authors declare that there exists a competing financial interest in this work that is related to a patent application on the use of identified germ cell-specific proteins in an antibody-based assay (Fertichip™) to predict the successful testicular biopsy outcomes in human non-obstructive azoospermia.
ISSN: 0268-1161

inserm-00814146
http://www.hal.inserm.fr/inserm-00814146
DOI : 10.1093/humrep/des360
PUBMED : 23024119

Éditeur(s) : HAL CCSD Human Kinetics
Résumé : International audience
The authors investigated the anthropometric and physiological characteristics of young Guadeloupian competitive swimmers in relation to swimming performance and compared the abilities of these children with those of the young white swimmers reported in the literature. All 2004 competitive swimmers between 10 and 14 y old (126 children, 61 boys and 65 girls, 12.0 ± 1.3 y) from Guadeloupe underwent anthropometric measurements and physiological and performance testing. Six boys on the French national swimming team are referred to hereafter as the 2011 elite subgroup. Anthropometric parameters, a jump-and-reach test, glide, and estimated aerobic power (eVO2max) were assessed in terms of swimming-performance analysis through a 400-m test. This study demonstrated that the Guadeloupian swimmers had more body fat than most age-matched white swimmers but had very poor hydrostatic lift; they had higher peak jump height and they swam as well as their white counterparts. The variability in 400-m performance between subjects was best described by glide, age, and eVO2max. Compared with the group of boys with the same age, the 2011 elite subgroup was significantly better for arm span, peak jump height, glide, and 400-m and 15-m performances. Further research is needed to investigate motor organization and energy cost of swimming in Afro-Caribbean swimmers.
ISSN: 1555-0265

hal-01137258
https://hal.univ-antilles.fr/hal-01137258
https://hal.univ-antilles.fr/hal-01137258/document
https://hal.univ-antilles.fr/hal-01137258/file/Hue%20et%20al.%20-%202013%20-%20Anthropometric%20and%20physiological%20characteristics%20in%20young%20Afro-Caribbean%20swimmers%20A%20preliminary%20study.pdf

Éditeur(s) : HAL CCSD Elsevier
Résumé : Rh (Rhesus) is a major blood group system in man, which is clinically significant in transfusion medicine. Rh antigens are carried by an oligomer of two major erythroid specific polypeptides, the Rh (D and CcEe) proteins and the RhAG glycoprotein, that shared a common predicted structure with 12 transmembrane a-helices (M0 to M11). Non erythroid homologues of these proteins have been identified (RhBG and RhCG), notably in diverse organs specialized in ammonia production and excretion, such as kidney, liver and intestine. Phylogenetic studies and experimental evidence have shown that these proteins belong to the Amt/Mep/Rh protein superfamily of ammonium/methylammonium permease, but another view suggests that Rh proteins might function as CO2 gas channels. Until recently no information on the structure of these proteins were available. However, in the last two years, new insight has been gained into the structural features of Rh proteins (through the determination of the crystal structures of bacterial AmtB and archeaebacterial Amt-1. Here, models of the subunit and oligomeric architecture of human Rh proteins are proposed, based on a refined alignment with and crystal structure of the bacterial ammonia transporter AmtB, a member of the Amt/ Mep/Rh superfamily. This alignment was performed considering invariant structural features, which were revealed through Hydrophobic Cluster Analysis, and led to propose alternative predictions for the less conserved regions, particularly in the N-terminal sequences. The Rh models, on which an additional Rh-specific, N-terminal helix M0 was tentatively positioned, were further assessed through the consideration of biochemical and immunochemical data, as well as of stereochemical and topological constraints. These models highlighted some Rh specific features that have not yet been reported. Among these, are the prediction of some critical residues, which may play a role in the channel function, but also in the stability of the subunit structure and oligomeric assembly. These results provide a basis to further understand the structure/function relationships of Rh proteins, and the alterations occurring in variant phenotypes.
ISSN: 1246-7820

hal-00068842
https://hal.archives-ouvertes.fr/hal-00068842

Éditeur(s) : HAL CCSD American Geophysical Union
Résumé : International audience
We present a set of single-well injection withdrawal tracer tests in a paleoreef porous reservoir displaying important small-scale heterogeneity. An improved dual-packer probe was designed to perform dirac-like tracer injection and accurate downhole automatic measurements of the tracer concentration during the recovery phase. By flushing the tracer, at constant flow rate, for increasing time duration, we can probe distinctly different reservoir volumes and test the multiscale predictability of the (non-Fickian) dispersion models. First we describe the characteristics, from microscale to meter scale, of the reservoir rock. Second, the specificity of the tracer test setup and the results obtained using two different tracers and measurement methods (salinity-conductivity and fluorescent dye­optical measurement, respectively) are presented. All the tracer tests display strongly tailed breakthrough curves (BTC) consistent with diffusion in immobile regions. Conductivity results, measured over 3 orders of magnitude only, could have been easily interpreted by the conventional mobile-immobile (MIM) diffusive mass transfer model of asymptotic log-log slope of 2. However, the fluorescent dye sensor, which allows exploring much lower concentration values, shows that a change in the log-log slope occurs at larger time with an asymptotic value of 1.5, corresponding to the double-porosity model. These results suggest that the conventional, one-slope MIM transfer rate model is too simplistic to account for the real multiscale heterogeneity of the diffusion-dominant fraction of the reservoir.
ISSN: 0043-1397

insu-00373695
https://hal-insu.archives-ouvertes.fr/insu-00373695
https://hal-insu.archives-ouvertes.fr/insu-00373695/document
https://hal-insu.archives-ouvertes.fr/insu-00373695/file/Gouze_et_al-2008-Water_Resources_Research.pdf
DOI : 10.1029/2007WR006278