Fat oxidation in nonobese and obese adolescents: effect of body composition and pubertal development.

OBJECTIVES: To measure postabsorptive fat oxidation (F(ox)) and to assess its association with body composition (lean body mass [LBM] and body fat mass [BFM]) and pubertal development. DESIGN: We studied 235 control (male/female ratio = 116/119; age [mean +/- SD]: 13.1 +/- 1.7 years; weight: 45.3 +/- 10.5 kg; LBM: 34.3 +/- 7.1 kg; BFM: 11.0 +/- 4.5 kg) and 159 obese (male/female ratio = 93/66; age: 12.9 +/- 2.1 years; weight: 76.2 +/- 19.1 kg; LBM: 47.4 +/- 10.9 kg; BFM: 28.8 +/- 9.2 kg) adolescents. Postabsorptive F(ox) was calculated from oxygen consumption, carbon dioxide production, and urinary nitrogen as measured by indirect calorimetry and Kjeldahl's method, respectively. Body composition was determined by anthropometry. RESULTS: Postabsorptive F(ox) (absolute value and percentage of resting metabolic rate) was significantly (p < 0.001) higher in the obese adolescents (76.7 +/- 26.3 gm/24 hours, 42.3% +/- 18.7%) than in the control subjects (40.0 +/- 26.3 gm/24 hours, 28.7% +/- 17.0%), even if adjusted for LBM. F(ox) corrected for BFM was similar in control and in obese children, but was significantly lower in girls compared with boys (control male subjects: 62.1 +/- 29.1 gm/24 hours, control female subjects: 51.6 +/- 28.4 gm/24 hours, obese male subjects: 57.3 +/- 29 gm/24 hour, obese female subjects: 45.0 +/- 28.4 gm/24 hours). BFM and LBM showed a significant positive correlation with F(ox). By stepwise regression analysis the most important determinant of F(ox) was BFM in obese and LBM in control children. There was a significant rise in F(ox) during puberty; however, it was mainly explained by changes in body composition. CONCLUSIONS: Obese adolescents have higher F(ox) rates than their normal-weight counterparts. Both LBM and fat mass are important determinants of F(ox).

Initial Results on the Composition of Fingerprints and its Evolution as a Function of Time by GC/MS Analysis

Determining the time since deposition of fingermarks may prove necessary to assess their relevance to criminal investigations. The crucial factor is the initial composition of fingermarks, because it represents the starting point of any aging model. This study mainly aimed to characterize the initial composition of fingerprints, which show a high variability between donors (inter-variability), but also to investigate the variations among fingerprints from the same donor (intra-variability). Solutions to reduce this initial variability using squalene and cholesterol as target compounds are proposed and should be further investigated. The influence of substrates was also evaluated, and the initial composition was observed to be larger on porous surface than nonporous surfaces. Preliminary aging of fingerprints over 30 days was finally studied on a porous and a nonporous substrate to evaluate the potential for dating of fingermarks. Squalene was observed to decrease in a faster rate on a nonporous substrate.

Changes in understorey biomass and species composition after water level drawdown on pine mires in southern Finland

Tiivistelmä: Vedenpinnan alenemisen vaikutus sararämeen pintakasvillisuuden biomassaan ja lajistoon

Modification de la composition des cigarettes durant te XXe siècle: rôle de l'industrie du tabac et effet sur la dépendance tabagique [Chemicals added to cigarettes and their effects on tobacco dependence]

This paper summarizes data on the factors involved in addiction and dependence to cigarettes. Nicotine has been intensively studied by the tobacco industry, for instance for its addictive effect at the lowest possible rates. The addition of diammonium phosphate and urea produces an alcalinization of the pH of cigarette smoke, and promotes the absorption and the trans-membrane passage of nicotine. The taste, the smell of smoke, and the visual aspect of the pack of cigarettes are also sensory components that promote addiction. Finally, menthol, sugar, cocoa and liquorice added to cigarettes also play a role in dependence and addiction to cigarettes by, for instance, making an anesthetic effect on the airways.

Composition and cross-sectional area of muscle fibre types in relation to daily gain and lean and fat content of carcass in Landrace and Yorkshire pigs

Selostus: Lihassolutyypin ja lihassolun poikkipinta-alan yhteys sian kasvuun ja ruhon koostumukseen maatiaisessa ja yorkshiressa

Lead acetate toxicity in vitro: Dependence on the cell composition of the cultures.

It is well known that exposure to low doses of lead causes long-lasting neurobehavioural deficits, but the cellular changes underlying these behavioural changes remain to be elucidated. A protective role of glial cells on neurons through lead sequestration by astrocytes has been proposed. The possible modulation of lead neurotoxicity by neuron-glia interactions was examined in three-dimensional cultures of foetal rat telencephalon. Mixed-brain cell cultures or cultures enriched in either neurons or glial cells were treated for 10 days with lead acetate (10(-6) m), a concentration below the limit of cytotoxicity. Intracellular lead content and cell type-specific enzyme activities were determined. It was found that in enriched cultures neurons stored more lead than glial cells, and each cell type alone stored more lead than in co-culture. Moreover, glial cells but not neurons were more affected by lead in enriched culture than in co-culture. These results show that neuron-glia interactions attenuate the cellular lead uptake and the glial susceptibility to lead, but they do not support the idea of a protective role of astrocytes.

Composition of weight gain during the neonatal period and longitudinal growth follow-up in premature babies.

Changes in the rate of growth and adiposity index (Quetelet index), calculated as weight/(length)2, kg/m2, were monitored from birth to 3 years in 19 premature babies (post-conceptional age 31.2 +/- 2 weeks) who were subjected during rapid growth (16 +/- 4 g/kg.day) to initial metabolic balance studies in the first weeks of life. These studies showed that the rate of fat accretion in these infants (3.3 +/- 0.9 g/kg.day) was substantially greater than that observed in fetuses of the same gestational age (2 g/kg.day) but the adiposity index was lower (9.6 +/- 1 kg/m2) than intrauterine values (11 kg/m2). Since at 6 months of age (corrected for gestational age at birth) the adiposity index was close to normality (103% of standard), the greater rate of fat accretion in early life contributed to progressively restore total body fat in premature babies. It is concluded that despite substantial fat deposition during the first weeks of life, the future evolution of these premature babies is favourable as judged from the normalization of adiposity index within the first 2 years of life.

Time-lapse and probabilistic inversion strategies for plane-wave electromagnetic data.

L'utilisation efficace des systèmes géothermaux, la séquestration du CO2 pour limiter le changement climatique et la prévention de l'intrusion d'eau salée dans les aquifères costaux ne sont que quelques exemples qui démontrent notre besoin en technologies nouvelles pour suivre l'évolution des processus souterrains à partir de la surface. Un défi majeur est d'assurer la caractérisation et l'optimisation des performances de ces technologies à différentes échelles spatiales et temporelles. Les méthodes électromagnétiques (EM) d'ondes planes sont sensibles à la conductivité électrique du sous-sol et, par conséquent, à la conductivité électrique des fluides saturant la roche, à la présence de fractures connectées, à la température et aux matériaux géologiques. Ces méthodes sont régies par des équations valides sur de larges gammes de fréquences, permettant détudier de manières analogues des processus allant de quelques mètres sous la surface jusqu'à plusieurs kilomètres de profondeur. Néanmoins, ces méthodes sont soumises à une perte de résolution avec la profondeur à cause des propriétés diffusives du champ électromagnétique. Pour cette raison, l'estimation des modèles du sous-sol par ces méthodes doit prendre en compte des informations a priori afin de contraindre les modèles autant que possible et de permettre la quantification des incertitudes de ces modèles de façon appropriée. Dans la présente thèse, je développe des approches permettant la caractérisation statique et dynamique du sous-sol à l'aide d'ondes EM planes. Dans une première partie, je présente une approche déterministe permettant de réaliser des inversions répétées dans le temps (time-lapse) de données d'ondes EM planes en deux dimensions. Cette stratégie est basée sur l'incorporation dans l'algorithme d'informations a priori en fonction des changements du modèle de conductivité électrique attendus. Ceci est réalisé en intégrant une régularisation stochastique et des contraintes flexibles par rapport à la gamme des changements attendus en utilisant les multiplicateurs de Lagrange. J'utilise des normes différentes de la norme l2 pour contraindre la structure du modèle et obtenir des transitions abruptes entre les régions du model qui subissent des changements dans le temps et celles qui n'en subissent pas. Aussi, j'incorpore une stratégie afin d'éliminer les erreurs systématiques de données time-lapse. Ce travail a mis en évidence l'amélioration de la caractérisation des changements temporels par rapport aux approches classiques qui réalisent des inversions indépendantes à chaque pas de temps et comparent les modèles. Dans la seconde partie de cette thèse, j'adopte un formalisme bayésien et je teste la possibilité de quantifier les incertitudes sur les paramètres du modèle dans l'inversion d'ondes EM planes. Pour ce faire, je présente une stratégie d'inversion probabiliste basée sur des pixels à deux dimensions pour des inversions de données d'ondes EM planes et de tomographies de résistivité électrique (ERT) séparées et jointes. Je compare les incertitudes des paramètres du modèle en considérant différents types d'information a priori sur la structure du modèle et différentes fonctions de vraisemblance pour décrire les erreurs sur les données. Les résultats indiquent que la régularisation du modèle est nécessaire lorsqu'on a à faire à un large nombre de paramètres car cela permet d'accélérer la convergence des chaînes et d'obtenir des modèles plus réalistes. Cependent, ces contraintes mènent à des incertitudes d'estimations plus faibles, ce qui implique des distributions a posteriori qui ne contiennent pas le vrai modèledans les régions ou` la méthode présente une sensibilité limitée. Cette situation peut être améliorée en combinant des méthodes d'ondes EM planes avec d'autres méthodes complémentaires telles que l'ERT. De plus, je montre que le poids de régularisation des paramètres et l'écart-type des erreurs sur les données peuvent être retrouvés par une inversion probabiliste. Finalement, j'évalue la possibilité de caractériser une distribution tridimensionnelle d'un panache de traceur salin injecté dans le sous-sol en réalisant une inversion probabiliste time-lapse tridimensionnelle d'ondes EM planes. Etant donné que les inversions probabilistes sont très coûteuses en temps de calcul lorsque l'espace des paramètres présente une grande dimension, je propose une stratégie de réduction du modèle ou` les coefficients de décomposition des moments de Legendre du panache de traceur injecté ainsi que sa position sont estimés. Pour ce faire, un modèle de résistivité de base est nécessaire. Il peut être obtenu avant l'expérience time-lapse. Un test synthétique montre que la méthodologie marche bien quand le modèle de résistivité de base est caractérisé correctement. Cette méthodologie est aussi appliquée à un test de trac¸age par injection d'une solution saline et d'acides réalisé dans un système géothermal en Australie, puis comparée à une inversion time-lapse tridimensionnelle réalisée selon une approche déterministe. L'inversion probabiliste permet de mieux contraindre le panache du traceur salin gr^ace à la grande quantité d'informations a priori incluse dans l'algorithme. Néanmoins, les changements de conductivités nécessaires pour expliquer les changements observés dans les données sont plus grands que ce qu'expliquent notre connaissance actuelle des phénomenès physiques. Ce problème peut être lié à la qualité limitée du modèle de résistivité de base utilisé, indiquant ainsi que des efforts plus grands devront être fournis dans le futur pour obtenir des modèles de base de bonne qualité avant de réaliser des expériences dynamiques. Les études décrites dans cette thèse montrent que les méthodes d'ondes EM planes sont très utiles pour caractériser et suivre les variations temporelles du sous-sol sur de larges échelles. Les présentes approches améliorent l'évaluation des modèles obtenus, autant en termes d'incorporation d'informations a priori, qu'en termes de quantification d'incertitudes a posteriori. De plus, les stratégies développées peuvent être appliquées à d'autres méthodes géophysiques, et offrent une grande flexibilité pour l'incorporation d'informations additionnelles lorsqu'elles sont disponibles. -- The efficient use of geothermal systems, the sequestration of CO2 to mitigate climate change, and the prevention of seawater intrusion in coastal aquifers are only some examples that demonstrate the need for novel technologies to monitor subsurface processes from the surface. A main challenge is to assure optimal performance of such technologies at different temporal and spatial scales. Plane-wave electromagnetic (EM) methods are sensitive to subsurface electrical conductivity and consequently to fluid conductivity, fracture connectivity, temperature, and rock mineralogy. These methods have governing equations that are the same over a large range of frequencies, thus allowing to study in an analogous manner processes on scales ranging from few meters close to the surface down to several hundreds of kilometers depth. Unfortunately, they suffer from a significant resolution loss with depth due to the diffusive nature of the electromagnetic fields. Therefore, estimations of subsurface models that use these methods should incorporate a priori information to better constrain the models, and provide appropriate measures of model uncertainty. During my thesis, I have developed approaches to improve the static and dynamic characterization of the subsurface with plane-wave EM methods. In the first part of this thesis, I present a two-dimensional deterministic approach to perform time-lapse inversion of plane-wave EM data. The strategy is based on the incorporation of prior information into the inversion algorithm regarding the expected temporal changes in electrical conductivity. This is done by incorporating a flexible stochastic regularization and constraints regarding the expected ranges of the changes by using Lagrange multipliers. I use non-l2 norms to penalize the model update in order to obtain sharp transitions between regions that experience temporal changes and regions that do not. I also incorporate a time-lapse differencing strategy to remove systematic errors in the time-lapse inversion. This work presents improvements in the characterization of temporal changes with respect to the classical approach of performing separate inversions and computing differences between the models. In the second part of this thesis, I adopt a Bayesian framework and use Markov chain Monte Carlo (MCMC) simulations to quantify model parameter uncertainty in plane-wave EM inversion. For this purpose, I present a two-dimensional pixel-based probabilistic inversion strategy for separate and joint inversions of plane-wave EM and electrical resistivity tomography (ERT) data. I compare the uncertainties of the model parameters when considering different types of prior information on the model structure and different likelihood functions to describe the data errors. The results indicate that model regularization is necessary when dealing with a large number of model parameters because it helps to accelerate the convergence of the chains and leads to more realistic models. These constraints also lead to smaller uncertainty estimates, which imply posterior distributions that do not include the true underlying model in regions where the method has limited sensitivity. This situation can be improved by combining planewave EM methods with complimentary geophysical methods such as ERT. In addition, I show that an appropriate regularization weight and the standard deviation of the data errors can be retrieved by the MCMC inversion. Finally, I evaluate the possibility of characterizing the three-dimensional distribution of an injected water plume by performing three-dimensional time-lapse MCMC inversion of planewave EM data. Since MCMC inversion involves a significant computational burden in high parameter dimensions, I propose a model reduction strategy where the coefficients of a Legendre moment decomposition of the injected water plume and its location are estimated. For this purpose, a base resistivity model is needed which is obtained prior to the time-lapse experiment. A synthetic test shows that the methodology works well when the base resistivity model is correctly characterized. The methodology is also applied to an injection experiment performed in a geothermal system in Australia, and compared to a three-dimensional time-lapse inversion performed within a deterministic framework. The MCMC inversion better constrains the water plumes due to the larger amount of prior information that is included in the algorithm. The conductivity changes needed to explain the time-lapse data are much larger than what is physically possible based on present day understandings. This issue may be related to the base resistivity model used, therefore indicating that more efforts should be given to obtain high-quality base models prior to dynamic experiments. The studies described herein give clear evidence that plane-wave EM methods are useful to characterize and monitor the subsurface at a wide range of scales. The presented approaches contribute to an improved appraisal of the obtained models, both in terms of the incorporation of prior information in the algorithms and the posterior uncertainty quantification. In addition, the developed strategies can be applied to other geophysical methods, and offer great flexibility to incorporate additional information when available.

Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables

Questions Soil properties have been widely shown to influence plant growth and distribution. However, the degree to which edaphic variables can improve models based on topo-climatic variables is still unclear. In this study, we tested the roles of seven edaphic variables, namely (1) pH; (2) the content of nitrogen and of (3) phosphorus; (4) silt; (5) sand; (6) clay and (7) carbon-to-nitrogen ratio, as predictors of species distribution models in an edaphically heterogeneous landscape. We also tested how the respective influence of these variables in the models is linked to different ecological and functional species characteristics. Location The Western Alps, Switzerland. Methods With four different modelling techniques, we built models for 115 plant species using topo-climatic variables alone and then topo-climatic variables plus each of the seven edaphic variables, one at a time. We evaluated the contribution of each edaphic variable by assessing the change in predictive power of the model. In a second step, we evaluated the importance of the two edaphic variables that yielded the largest increase in predictive power in one final set of models for each species. Third, we explored the change in predictive power and the importance of variables across plant functional groups. Finally, we assessed the influence of the edaphic predictors on the prediction of community composition by stacking the models for all species and comparing the predicted communities with the observed community. Results Among the set of edaphic variables studied, pH and nitrogen content showed the highest contributions to improvement of the predictive power of the models, as well as the predictions of community composition. When considering all topo-climatic and edaphic variables together, pH was the second most important variable after degree-days. The changes in model results caused by edaphic predictors were dependent on species characteristics. The predictions for the species that have a low specific leaf area, and acidophilic preferences, tolerating low soil pH and high humus content, showed the largest improvement by the addition of pH and nitrogen in the model. Conclusions pH was an important predictor variable for explaining species distribution and community composition of the mountain plants considered in our study. pH allowed more precise predictions for acidophilic species. This variable should not be neglected in the construction of species distribution models in areas with contrasting edaphic conditions.