Association of PCK1 with Body Mass Index and Other Metabolic Features in Patients With Psychotropic Treatments.

Weight gain is a major health problem among psychiatric populations. It implicates several receptors and hormones involved in energy balance and metabolism. Phosphoenolpyruvate carboxykinase 1 is a rate-controlling enzyme involved in gluconeogenesis, glyceroneogenesis and cataplerosis and has been related to obesity and diabetes phenotypes in animals and humans. The aim of this study was to investigate the association of phosphoenolpyruvate carboxykinase 1 polymorphisms with metabolic traits in psychiatric patients treated with psychotropic drugs inducing weight gain and in general population samples. One polymorphism (rs11552145G > A) significantly associated with body mass index in the psychiatric discovery sample (n = 478) was replicated in 2 other psychiatric samples (n1 = 168, n2 = 188), with AA-genotype carriers having lower body mass index as compared to G-allele carriers. Stronger associations were found among women younger than 45 years carrying AA-genotype as compared to G-allele carriers (-2.25 kg/m, n = 151, P = 0.009) and in the discovery sample (-2.20 kg/m, n = 423, P = 0.0004). In the discovery sample for which metabolic parameters were available, AA-genotype showed lower waist circumference (-6.86 cm, P = 0.008) and triglycerides levels (-5.58 mg/100 mL, P < 0.002) when compared to G-allele carriers. Finally, waist-to-hip ratio was associated with rs6070157 (proxy of rs11552145, r = 0.99) in a population-based sample (N = 123,865, P = 0.022). Our results suggest an association of rs11552145G > A polymorphism with metabolic-related traits, especially in psychiatric populations and in women younger than 45 years.

Error models in hydrogeology applications

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

Oxidative potential of particles in different occupational environments: a pilot study

The oxidative potential (OP) of particulate matter has been proposed as a toxicologically relevant metric. This concept is already frequently used for hazard characterization of ambient particles but it is still seldom applied in the occupational field. The objective of this study was to assess the OP in two different types of workplaces and to investigate the relationship between the OP and the physicochemical characteristics of the collected particles. At a toll station, at the entrance of a tunnel ('Tunnel' site), and at three different mechanical yards ('Depot' sites), we assessed particle mass (PM4 and PM2.5 and size distribution), number and surface area, organic and elemental carbon, polycyclic aromatic hydrocarbon (PAH), and four quinones as well as iron and copper concentration. The OP was determined directly on filters without extraction by using the dithiothreitol assay (DTT assay-OP(DTT)). The averaged mass concentration of respirable particles (PM4) at the Tunnel site was about twice the one at the Depot sites (173±103 and 90±36 µg m(-3), respectively), whereas the OP(DTT) was practically identical for all the sites (10.6±7.2 pmol DTT min(-1) μg(-1) at the Tunnel site; 10.4±4.6 pmol DTT min(-1) μg(-1) at the Depot sites). The OP(DTT) of PM4 was mostly present on the smallest PM2.5 fraction (OP(DTT) PM2.5: 10.2±8.1 pmol DTT min(-1) μg(-1); OP(DTT) PM4: 10.5±5.8 pmol DTT min(-1) μg(-1) for all sites), suggesting the presence of redox inactive components in the PM2.5-4 fraction. Although the reactivity was similar at the Tunnel and Depot sites irrespective of the metric chosen (OP(DTT) µg(-1) or OP(DTT) m(-3)), the chemicals associated with OP(DTT) were different between the two types of workplaces. The organic carbon, quinones, and/or metal content (Fe, Cu) were strongly associated with the DTT reactivity at the Tunnel site whereas only Fe and PAH were associated (positively and negatively, respectively) with this reactivity at the Depot sites. These results demonstrate the feasibility of measuring of the OP(DTT) in occupational environments and suggest that the particulate OP(DTT) is integrative of different physicochemical properties. This parameter could be a potentially useful exposure proxy for investigating particle exposure-related oxidative stress and its consequences. Further research is needed mostly to demonstrate the association of OP(DTT) with relevant oxidative endpoints in humans exposed to particles.

Effect of Smoking on Blood Pressure and Resting Heart Rate: A Mendelian Randomization Meta-Analysis in the CARTA Consortium.

BACKGROUND: Smoking is an important cardiovascular disease risk factor, but the mechanisms linking smoking to blood pressure are poorly understood. METHODS AND RESULTS: Data on 141 317 participants (62 666 never, 40 669 former, 37 982 current smokers) from 23 population-based studies were included in observational and Mendelian randomization meta-analyses of the associations of smoking status and smoking heaviness with systolic and diastolic blood pressure, hypertension, and resting heart rate. For the Mendelian randomization analyses, a genetic variant rs16969968/rs1051730 was used as a proxy for smoking heaviness in current smokers. In observational analyses, current as compared with never smoking was associated with lower systolic blood pressure and diastolic blood pressure and lower hypertension risk, but with higher resting heart rate. In observational analyses among current smokers, 1 cigarette/day higher level of smoking heaviness was associated with higher (0.21 bpm; 95% confidence interval 0.19; 0.24) resting heart rate and slightly higher diastolic blood pressure (0.05 mm Hg; 95% confidence interval 0.02; 0.08) and systolic blood pressure (0.08 mm Hg; 95% confidence interval 0.03; 0.13). However, in Mendelian randomization analyses among current smokers, although each smoking increasing allele of rs16969968/rs1051730 was associated with higher resting heart rate (0.36 bpm/allele; 95% confidence interval 0.18; 0.54), there was no strong association with diastolic blood pressure, systolic blood pressure, or hypertension. This would suggest a 7 bpm higher heart rate in those who smoke 20 cigarettes/day. CONCLUSIONS: This Mendelian randomization meta-analysis supports a causal association of smoking heaviness with higher level of resting heart rate, but not with blood pressure. These findings suggest that part of the cardiovascular risk of smoking may operate through increasing resting heart rate.

The Influence of Chronic and Situational Social Status on Stereotype Susceptibility

We tested whether stereotypical situations would affect low-status group members' performance more strongly than high-status group members'. Experiment 1 and 2 tested this hypothesis using gender as a proxy of chronic social status and a gender-neutral task thathas been randomly presented to favor boys (men superiority condition), favor girls (women superiority condition), or show no gender preference (control condition). Both experiments found that women's (Experiment 1) and girls' performance (Experiment 2) suffered more from the evoked stereotypes than did men's and boys' ones. This result was replicated in Experiment 3, indicating that short men (low-status group) were more affected compared to tallmen (high-status group). Additionally, men were more affected compared to women when they perceived height as a threat. Hence, individuals are more or less vulnerable to identity threats as a function of the chronic social status at play; enjoying a high status provides protection and endorsing a low one weakens individual performance in stereotypical situations.

Dustiness and deagglomeration testing: interlaboratory comparison of systems for nanoparticle powders

Different types of aerosolization and deagglomeration testing systems exist for studying the properties of nanomaterial powders and their aerosols. However, results are dependent on the specific methods used. In order to have well-characterized aerosols, we require a better understanding of how system parameters and testing conditions influence the properties of the aerosols generated. In the present study, four experimental setups delivering different aerosolization energies were used to test the resultant aerosols of two distinct nanomaterials (hydrophobic and hydrophilic TiO2). The reproducibility of results within each system was good. However, the number concentrations and size distributions of the aerosols created varied across the four systems; for number concentrations, e.g., from 10(3) to 10(6) #/cm(3). Moreover, distinct differences were also observed between the two materials with different surface coatings. The article discusses how system characteristics and other pertinent conditions modify the test results. We propose using air velocity as a suitable proxy for estimating energy input levels in aerosolization systems. The information derived from this work will be especially useful for establishing standard operating procedures for testing nanopowders, as well as for estimating their release rates under different energy input conditions, which is relevant for occupational exposure.

CRTC2 polymorphism as a risk factor for the incidence of metabolic syndrome in patients with solid organ transplantation.

NlmCategory="UNASSIGNED">Metabolic syndrome after transplantation is a major concern following solid organ transplantation (SOT). The CREB-regulated transcription co-activator 2 (CRTC2) regulates glucose metabolism. The effect of CRTC2 polymorphisms on new-onset diabetes after transplantation (NODAT) was investigated in a discovery sample of SOT recipients (n1=197). Positive results were tested for replication in two samples from the Swiss Transplant Cohort Study (STCS, n2=1294 and n3=759). Obesity and other metabolic traits were also tested. Associations with metabolic traits in population-based samples (n4=46'186, n5=123'865, n6>100,000) were finally analyzed. In the discovery sample, CRTC2 rs8450-AA genotype was associated with NODAT, fasting blood glucose and body mass index (Pcorrected<0.05). CRTC2 rs8450-AA genotype was associated with NODAT in the second STCS replication sample (odd ratio (OR)=2.01, P=0.04). In the combined STCS replication samples, the effect of rs8450-AA genotype on NODAT was observed in patients having received SOT from a deceased donor and treated with tacrolimus (n=395, OR=2.08, P=0.02) and in non-kidney transplant recipients (OR=2.09, P=0.02). Moreover, rs8450-AA genotype was associated with overweight or obesity (n=1215, OR=1.56, P=0.02), new-onset hyperlipidemia (n=1007, OR=1.76, P=0.007), and lower high-density lipoprotein-cholesterol (n=1214, β=-0.08, P=0.001). In the population-based samples, a proxy of rs8450G>A was significantly associated with several metabolic abnormalities. CRTC2 rs8450G>A appears to have an important role in the high prevalence of metabolic traits observed in patients with SOT. A weak association with metabolic traits was also observed in the population-based samples.The Pharmacogenomics Journal advance online publication, 8 December 2015; doi:10.1038/tpj.2015.82.

Local effects drive heterozygosity-fitness correlations in an outcrossing long-lived tree.

Heterozygosity-fitness correlations (HFCs) have been used to understand the complex interactions between inbreeding, genetic diversity and evolution. Although frequently reported for decades, evidence for HFCs was often based on underpowered studies or inappropriate methods, and hence their underlying mechanisms are still under debate. Here, we used 6100 genome-wide single nucleotide polymorphisms (SNPs) to test for general and local effect HFCs in maritime pine (Pinus pinaster Ait.), an iconic Mediterranean forest tree. Survival was used as a fitness proxy, and HFCs were assessed at a four-site common garden under contrasting environmental conditions (total of 16 288 trees). We found no significant correlations between genome-wide heterozygosity and fitness at any location, despite variation in inbreeding explaining a substantial proportion of the total variance for survival. However, four SNPs (including two non-synonymous mutations) were involved in significant associations with survival, in particular in the common gardens with higher environmental stress, as shown by a novel heterozygosity-fitness association test at the species-wide level. Fitness effects of SNPs involved in significant HFCs were stable across maritime pine gene pools naturally growing in distinct environments. These results led us to dismiss the general effect hypothesis and suggested a significant role of heterozygosity in specific candidate genes for increasing fitness in maritime pine. Our study highlights the importance of considering the species evolutionary and demographic history and different spatial scales and testing environments when assessing and interpreting HFCs.

Farmer-Managed Natural Regeneration Enhances Rural Livelihoods in Dryland West Africa

Declining agricultural productivity, land clearance and climate change are compounding the vulnerability of already marginal rural populations in West Africa. 'Farmer-Managed Natural Regeneration' (FMNR) is an approach to arable land restoration and reforestation that seeks to reconcile sustained food production, conservation of soils and protection of biodiversity. It involves selecting and protecting the most vigorous stems regrowing from live stumps of felled trees, pruning off all other stems, and pollarding the chosen stems to grow into straight trunks. Despite widespread enthusiasm and application of FMNR by environmental management and development projects, to date, no research has provided a measure of the aggregate livelihood impact of community adoption of FMNR. This paper places FMNR in the context of other agroforestry initiatives, then seeks to quantify the value of livelihood outcomes of FMNR. We review published and unpublished evidence about the impacts of FMNR, and present a new case study that addresses gaps in the evidence base. The case study focuses on a FMNR project in the district of Talensi in the semi-arid Upper East Region in Ghana. The case study employs a Social Return on Investment (SROI) analysis, which identifies proxy financial values for non-economic as well as economic benefits. The results demonstrate income and agricultural benefits, but also show that asset creation, increased consumption of wild resources, health improvements and psycho-social benefits created more value in FMNR-adopting households during the period of the study than increases in income and agricultural yields.

Areas of high conservation value in Georgia: present and future threats by invasive alien plants

Georgia is known for its extraordinary rich biodiversity of plants, which may now be threatened due to the spread of invasive alien plants (IAP). We aimed to identify (i) the most prominent IAP out of 9 selected potentially invasive and harmful IAP IAP by predicting thetheir distribution of 9 selected IAP under current and future climate conditions in Georgia as well as in its 43 Protected Areas, as a proxy for areas of high conservation value and (ii) the Protected Areas most at risk due to these IAP. We used species distribution models based on 6 climate variables and then filtered the obtained distributions based on maps of soil and vegetation types, and on recorded occurrences, resulting into the predicted ecological distribution of the 9 IAP's at a resolution of 1km2. We foundOur habitat suitability analysis showed that Ambrosia artemisiifolia, (24% and 40%) Robinia pseudoacaia (14% and 19%) and Ailanthus altissima (9% and 11%) have the largest potential distribution are the most abundant (predicted % area covered)d) IAP, with Ailanthus altissima the potentially most increasing one over the next fifty years (from 9% to 13% and from 11% to 25%), for Georgia and the Protected Areas, respectively. Furthermore, our results show indicate two areas in Georgia that are under specifically high threat, i.e. the area around Tbilisi and an area in the western part of Georgia (Adjara), both at lower altitudes. Our procedure to identify areas of high conservation value most at risk by IAP has been applied for the first time. It will help national authorities in prioritizing their measures to protect Georgia's outstanding biodiversity from the negative impact of IAP.