Control of landslide retrogression by discontinuities: Evidence by the integration of airbone- and ground-based geophysical information

The objective of this work is to present a multitechnique approach to define the geometry, the kinematics, and the failure mechanism of a retrogressive large landslide (upper part of the La Valette landslide, South French Alps) by the combination of airborne and terrestrial laser scanning data and ground-based seismic tomography data. The advantage of combining different methods is to constrain the geometrical and failure mechanism models by integrating different sources of information. Because of an important point density at the ground surface (4. 1 points m?2), a small laser footprint (0.09 m) and an accurate three-dimensional positioning (0.07 m), airborne laser scanning data are adapted as a source of information to analyze morphological structures at the surface. Seismic tomography surveys (P-wave and S-wave velocities) may highlight the presence of low-seismic-velocity zones that characterize the presence of dense fracture networks at the subsurface. The surface displacements measured from the terrestrial laser scanning data over a period of 2 years (May 2008?May 2010) allow one to quantify the landslide activity at the direct vicinity of the identified discontinuities. An important subsidence of the crown area with an average subsidence rate of 3.07 m?year?1 is determined. The displacement directions indicate that the retrogression is controlled structurally by the preexisting discontinuities. A conceptual structural model is proposed to explain the failure mechanism and the retrogressive evolution of the main scarp. Uphill, the crown area is affected by planar sliding included in a deeper wedge failure system constrained by two preexisting fractures. Downhill, the landslide body acts as a buttress for the upper part. Consequently, the progression of the landslide body downhill allows the development of dip-slope failures, and coherent blocks start sliding along planar discontinuities. The volume of the failed mass in the crown area is estimated at 500,000 m3 with the sloping local base level method.

On problems of calculating energy expenditure and substrate utilization from respiratory exchange data.

Indirect calorimetry based on respiratory exchange measurement has been successfully used from the beginning of the century to obtain an estimate of heat production (energy expenditure) in human subjects and animals. The errors inherent to this classical technique can stem from various sources: 1) model of calculation and assumptions, 2) calorimetric factors used, 3) technical factors and 4) human factors. The physiological and biochemical factors influencing the interpretation of calorimetric data include a change in the size of the bicarbonate and urea pools and the accumulation or loss (via breath, urine or sweat) of intermediary metabolites (gluconeogenesis, ketogenesis). More recently, respiratory gas exchange data have been used to estimate substrate utilization rates in various physiological and metabolic situations (fasting, post-prandial state, etc.). It should be recalled that indirect calorimetry provides an index of overall substrate disappearance rates. This is incorrectly assumed to be equivalent to substrate "oxidation" rates. Unfortunately, there is no adequate golden standard to validate whole body substrate "oxidation" rates, and this contrasts to the "validation" of heat production by indirect calorimetry, through use of direct calorimetry under strict thermal equilibrium conditions. Tracer techniques using stable (or radioactive) isotopes, represent an independent way of assessing substrate utilization rates. When carbohydrate metabolism is measured with both techniques, indirect calorimetry generally provides consistent glucose "oxidation" rates as compared to isotopic tracers, but only when certain metabolic processes (such as gluconeogenesis and lipogenesis) are minimal or / and when the respiratory quotients are not at the extreme of the physiological range. However, it is believed that the tracer techniques underestimate true glucose "oxidation" rates due to the failure to account for glycogenolysis in the tissue storing glucose, since this escapes the systemic circulation. A major advantage of isotopic techniques is that they are able to estimate (given certain assumptions) various metabolic processes (such as gluconeogenesis) in a noninvasive way. Furthermore when, in addition to the 3 macronutrients, a fourth substrate is administered (such as ethanol), isotopic quantification of substrate "oxidation" allows one to eliminate the inherent assumptions made by indirect calorimetry. In conclusion, isotopic tracers techniques and indirect calorimetry should be considered as complementary techniques, in particular since the tracer techniques require the measurement of carbon dioxide production obtained by indirect calorimetry. However, it should be kept in mind that the assessment of substrate oxidation by indirect calorimetry may involve large errors in particular over a short period of time. By indirect calorimetry, energy expenditure (heat production) is calculated with substantially less error than substrate oxidation rates.

Whole body protein metabolism and resting energy expenditure in pregnant Gambian women.

Whole body protein metabolism and resting energy expenditure (REE) were measured at 11, 23, and 33 wk of pregnancy in nine pregnant (not malnourished) Gambian women and in eight matched nonpregnant nonlactating (NPNL) matched controls. Rates of whole body nitrogen flux, protein synthesis, and protein breakdown were determined in the fed state from the level of isotope enrichment of urinary urea and ammonia during a period of 9 h after a single oral dose of [15N]glycine. At regular intervals, REE was measured by indirect calorimetry (hood system). Based on the arithmetic end-product average of values obtained with urea and ammonia, a significant increase in whole body protein synthesis was observed during the second trimester (5.8 +/- 0.4 g.kg-1.day-1) relative to values obtained both for the NPNL controls (4.5 +/- 0.3 g.kg-1.day-1) and those during the first trimester (4.7 +/- 0.3 g.kg-1.day-1). There was a significant rise in REE during the third trimester both in the preprandial and postprandial states. No correlation was found between REE after meal ingestion and the rate of whole body protein synthesis.

Influence of inflammation on total energy expenditure in hemodialysis patients.

Background and Objectives: Studies show that inflammation can contribute to an increase in resting energy expenditure in patients with chronic kidney disease; however, findings about total energy expenditure (TEE) have not been reported. The aim of this study was to evaluate the effects of inflammation on TEE and physical activity energy expenditure in hemodialysis (HD) patients.Design: This was a cross-sectional study.Setting: This study was conducted from Hopital Edouard Herriot, Lyon, France.Patients: This study included 24 HD patients and 18 healthy subjects.Main Outcome Measure: TEE and step counts were measured over a 7-day period by the SenseWear Pro2 Armband in 24 HD patients (15 patients with C-reactive protein,5 mg/L, aged 67.0 +/- 6 14.7 years, and 9 with C-reactive protein >5 mg/L, aged 69.0 +/- 6 18.0 years) and compared with 18 healthy subjects (62.3 +/- 6 15.3 years).Results: Mean estimated TEE measured with SenseWear Pro2 Armband was significantly lower (25.5 +/- 4.1 kcal/kg/day) in patients with inflammation when compared with those without inflammation (32.0 +/- 6.7 kcal/kg/day) and with healthy subjects (31.8 +/- 6 7.0 kcal/kg/day) (P = .012). There was a difference in the physical activity (step counts) between patient groups (P < .05). Healthy subjects and patients without inflammation walked more (8,107 +/- 5,419 and 6,016 +/- 3,752 steps/day, respectively) as compared with patients with inflammation (2,801 +/- 2,754 steps/day, P = .001).Conclusion: Our findings suggest that patients with inflammation have a lower TEE when compared with healthy subjects and patients without inflammation. TEE is influenced by physical activity because patients with inflammation appear to be less active. (C) 2011 by the National Kidney Foundation, Inc. All rights reserved.

Uncovering a role for micro-RNAs in sleep homeostasis and energy metabolism

Micro-RNAs (miRNAs) are key, post-transcriptional regulators of gene expression and have been implicated in almost every cellular process investigated thus far. However, their role in sleep, in particular the homeostatic aspect of sleep control, has received little attention. We here assessed the effects of sleep deprivation on the brain miRNA transcriptome in the mouse. Sleep deprivation affected miRNA expression in a brain-region specific manner. The forebrain expression of the miRNA miR-709 was affected the most and in situ analyses confirmed its robust increase throughout the brain, especially in the cerebral cortex and the hippocampus. The hippocampus was a major target of the sleep deprivation affecting 37 miRNAs compared to 52 in the whole forebrain. Moreover, independent from the sleep deprivation condition, miRNA expression was highly region-specific with 45% of all expressed miRNAs showing higher expression in hippocampus and 55% in cortex. Next we demonstrated that down-regulation of miRNAs in Com/c2o-expressing neurons of adult mice, through a conditional and inducible Dicer knockout mice model (cKO), results in an altered homeostatic response after sleep deprivation eight weeks following the tamoxifen-induced recombination. Dicer cKO mice showed a larger increase in the electro-encephalographic (EEG) marker of sleep pressure, EEG delta power, and a reduced Rapid Eye Movement sleep rebound, compared to controls, highlighting a functional role of miRNAs in sleep homeostasis. Beside a sleep phenotype, Dicer cKO mice developed an unexpected, severe obesity phenotype associated with hyperphagia and altered metabolism. Even more surprisingly, after reaching maximum body weight 5 weeks after tamoxifen injection, obese cKO mice spontaneously started losing weight as rapidly as it was gained. Brain transcriptome analyses in obese mice identified several obesity-related pathways (e.g. leptin, somatostatin, and nemo-like kinase signaling), as well as genes involved in feeding and appetite (e.g. Pmch, Neurotensin). A gene cluster with anti-correlated expression in the cerebral cortex of post-obese compared to obese mice was enriched for synaptic plasticity pathways. While other studies have identified a role for miRNAs in obesity, we here present a unique model that allows for the study of processes involved in reversing obesity. Moreover, our study identified the cortex as a brain area important for body weight homeostasis. Together, these observations strongly suggest a role for miRNAs in the maintenance of homeostatic processes in the mouse, and support the hypothesis of a tight relationship between sleep and metabolism at a molecular - Les micro-ARNS (miARNs) sont des régulateurs post-transcriptionnels de l'expression des gènes, impliqués dans la quasi-totalité des processus cellulaires. Cependant, leur rôle dans la régulation du sommeil, et en particulier dans le maintien de l'homéostasie du sommeil, n'a reçu que très peu d'attention jusqu'à présent. Dans cette étude, nous avons étudié les conséquences d'une privation de sommeil sur l'expression cérébrale des miARNs chez la souris, et observé des changements dans l'expression de nombreux miARNs. Dans le cerveau antérieur, miR-709 est le miARN le plus affecté par la perte de sommeil, en particulier dans le cortex cérébral et l'hippocampe. L'hippocampe est la région la plus touchée avec 37 miARNs changés comparés à 52 dans le cerveau entier. Par ailleurs, indépendamment de la privation de sommeil, certains miARNs sont spécifiquement enrichis dans certaines aires cérébrales, 45% des miARNs étant surexprimés dans l'hippocampe contre 55% dans le cortex. Dans une seconde étude, nous avons observé que la délétion de DICER, enzyme essentielle à la biosynthèse des miARNs, et la perte subséquente des miARNs dans les neurones exprimant la protéine CAMK2a altère la réponse homéostatique à une privation de sommeil, 8 semaines après l'induction de la recombinaison génétique par le tamoxifen. Les souris sans Dicer (cKO) ont une plus large augmentation de l'EEG delta power, le principal marqueur électro-encéphalographique du besoin de sommeil, comparée aux contrôles, ainsi qu'un rebond en sommeil paradoxal plus petit. De façon surprenante, les souris Dicer cKO développent une obésité rapide, sévère et transitoire, associée à de l'hyperphagie et une altération de leur métabolisme énergétique. Après avoir atteint un pic maximal d'obésité, les souris cKO entrent spontanément dans une période de perte de poids rapide. L'analyse du transcriptome cérébral des souris obèses nous a permis d'identifier des voies associées à l'obésité (leptine, somatostatine et nemo-like kinase), et à la prise alimentaire (Pmch, Neurotensin), tandis que celui des souris post-obèses a révélé un groupe de gènes liés à la plasticité synaptique. Au-delà des nombreux modèles d'obésité existant chez la souris, notre étude présente un modèle unique permettant d'étudier les mécanismes sous-jacent la perte de poids. De plus, nous avons mis en évidence un rôle important du cortex cérébral dans le maintien de la balance énergétique. En conclusion, toutes ces observations soutiennent l'idée que les miARNs sont des régulateurs cruciaux dans le maintien des processus homéostatiques et confortent l'hypothèse d'une étroite relation moléculaire entre le sommeil et le métabolisme.

Increased 24-hour energy expenditure in cigarette smokers.

We studied the effect of smoking on energy expenditure in eight healthy cigarette smokers who spent 24 hours in a metabolic chamber on two occasions, once without smoking and once while smoking 24 cigarettes per day. Diet and physical exercise (30 minutes of treadmill walking) were standardized on both occasions. Physical activity in the chamber was measured by use of a radar system. Smoking caused an increase in total 24-hour energy expenditure (from a mean value [+/- SEM] of 2230 +/- 115 to 2445 +/- 120 kcal per 24 hours; P less than 0.001), although no changes were observed in physical activity or mean basal metabolic rate (1545 +/- 80 vs. 1570 +/- 70 kcal per 24 hours). During the smoking period, the mean diurnal urinary excretion of norepinephrine (+/- SEM) increased from 1.25 +/- 0.14 to 1.82 +/- 0.28 micrograms per hour (P less than 0.025), and mean nocturnal excretion increased from 0.73 +/- 0.07 to 0.91 +/- 0.08 micrograms per hour (P less than 0.001). These short-term observations demonstrate that cigarette smoking increases 24-hour energy expenditure by approximately 10 percent, and that this effect may be mediated in part by the sympathetic nervous system. The findings also indicate that energy expenditure can be expected to decrease when people stop smoking, thereby favoring the gain in body weight that often accompanies the cessation of smoking.

Application of a roughness-length representation to parameterize energy loss in 3-D numerical simulations of large rivers

Recent technological advances in remote sensing have enabled investigation of the morphodynamics and hydrodynamics of large rivers. However, measuring topography and flow in these very large rivers is time consuming and thus often constrains the spatial resolution and reach-length scales that can be monitored. Similar constraints exist for computational fluid dynamics (CFD) studies of large rivers, requiring maximization of mesh-or grid-cell dimensions and implying a reduction in the representation of bedform-roughness elements that are of the order of a model grid cell or less, even if they are represented in available topographic data. These ``subgrid'' elements must be parameterized, and this paper applies and considers the impact of roughness-length treatments that include the effect of bed roughness due to ``unmeasured'' topography. CFD predictions were found to be sensitive to the roughness-length specification. Model optimization was based on acoustic Doppler current profiler measurements and estimates of the water surface slope for a variety of roughness lengths. This proved difficult as the metrics used to assess optimal model performance diverged due to the effects of large bedforms that are not well parameterized in roughness-length treatments. However, the general spatial flow patterns are effectively predicted by the model. Changes in roughness length were shown to have a major impact upon flow routing at the channel scale. The results also indicate an absence of secondary flow circulation cells in the reached studied, and suggest simpler two-dimensional models may have great utility in the investigation of flow within large rivers. Citation: Sandbach, S. D. et al. (2012), Application of a roughness-length representation to parameterize energy loss in 3-D numerical simulations of large rivers, Water Resour. Res., 48, W12501, doi: 10.1029/2011WR011284.

Métabolisme énergétique des personnes âgées [Energy metabolism in the elderly].

The energy metabolism in elderly subjects is discussed on the basis of previous analyses of the influence of age on the three components of energy expenditure in man: basal metabolic rate, thermogenesis and physical activity. All three components are diminished in elderly people. We conclude that the modifications of body composition, in particular the age-related loss of lean body mass, result in decreased basal metabolic rate and probably also a blunted diet-induced thermogenesis. Moreover we emphasize that the decrease in physical activity observed in elderly people is the most likely causal factor.

Comparison between computational alanine scanning and per-residue binding free energy decomposition for protein-protein association using MM-GBSA: application to the TCR-p-MHC complex.

Recognition by the T-cell receptor (TCR) of immunogenic peptides (p) presented by Class I major histocompatibility complexes (MHC) is the key event in the immune response against virus-infected cells or tumor cells. A study of the 2C TCR/SIYR/H-2K(b) system using a computational alanine scanning and a much faster binding free energy decomposition based on the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method is presented. The results show that the TCR-p-MHC binding free energy decomposition using this approach and including entropic terms provides a detailed and reliable description of the interactions between the molecules at an atomistic level. Comparison of the decomposition results with experimentally determined activity differences for alanine mutants yields a correlation of 0.67 when the entropy is neglected and 0.72 when the entropy is taken into account. Similarly, comparison of experimental activities with variations in binding free energies determined by computational alanine scanning yields correlations of 0.72 and 0.74 when the entropy is neglected or taken into account, respectively. Some key interactions for the TCR-p-MHC binding are analyzed and some possible side chains replacements are proposed in the context of TCR protein engineering. In addition, a comparison of the two theoretical approaches for estimating the role of each side chain in the complexation is given, and a new ad hoc approach to decompose the vibrational entropy term into atomic contributions, the linear decomposition of the vibrational entropy (LDVE), is introduced. The latter allows the rapid calculation of the entropic contribution of interesting side chains to the binding. This new method is based on the idea that the most important contributions to the vibrational entropy of a molecule originate from residues that contribute most to the vibrational amplitude of the normal modes. The LDVE approach is shown to provide results very similar to those of the exact but highly computationally demanding method.

Quantitative integration of geophysical and hydraulic data : from the local towards the regional scale range

Des progrès significatifs ont été réalisés dans le domaine de l'intégration quantitative des données géophysique et hydrologique l'échelle locale. Cependant, l'extension à de plus grandes échelles des approches correspondantes constitue encore un défi majeur. Il est néanmoins extrêmement important de relever ce défi pour développer des modèles fiables de flux des eaux souterraines et de transport de contaminant. Pour résoudre ce problème, j'ai développé une technique d'intégration des données hydrogéophysiques basée sur une procédure bayésienne de simulation séquentielle en deux étapes. Cette procédure vise des problèmes à plus grande échelle. L'objectif est de simuler la distribution d'un paramètre hydraulique cible à partir, d'une part, de mesures d'un paramètre géophysique pertinent qui couvrent l'espace de manière exhaustive, mais avec une faible résolution (spatiale) et, d'autre part, de mesures locales de très haute résolution des mêmes paramètres géophysique et hydraulique. Pour cela, mon algorithme lie dans un premier temps les données géophysiques de faible et de haute résolution à travers une procédure de réduction déchelle. Les données géophysiques régionales réduites sont ensuite reliées au champ du paramètre hydraulique à haute résolution. J'illustre d'abord l'application de cette nouvelle approche dintégration des données à une base de données synthétiques réaliste. Celle-ci est constituée de mesures de conductivité hydraulique et électrique de haute résolution réalisées dans les mêmes forages ainsi que destimations des conductivités électriques obtenues à partir de mesures de tomographic de résistivité électrique (ERT) sur l'ensemble de l'espace. Ces dernières mesures ont une faible résolution spatiale. La viabilité globale de cette méthode est testée en effectuant les simulations de flux et de transport au travers du modèle original du champ de conductivité hydraulique ainsi que du modèle simulé. Les simulations sont alors comparées. Les résultats obtenus indiquent que la procédure dintégration des données proposée permet d'obtenir des estimations de la conductivité en adéquation avec la structure à grande échelle ainsi que des predictions fiables des caractéristiques de transports sur des distances de moyenne à grande échelle. Les résultats correspondant au scénario de terrain indiquent que l'approche d'intégration des données nouvellement mise au point est capable d'appréhender correctement les hétérogénéitées à petite échelle aussi bien que les tendances à gande échelle du champ hydraulique prévalent. Les résultats montrent également une flexibilté remarquable et une robustesse de cette nouvelle approche dintégration des données. De ce fait, elle est susceptible d'être appliquée à un large éventail de données géophysiques et hydrologiques, à toutes les gammes déchelles. Dans la deuxième partie de ma thèse, j'évalue en détail la viabilité du réechantillonnage geostatique séquentiel comme mécanisme de proposition pour les méthodes Markov Chain Monte Carlo (MCMC) appliquées à des probmes inverses géophysiques et hydrologiques de grande dimension . L'objectif est de permettre une quantification plus précise et plus réaliste des incertitudes associées aux modèles obtenus. En considérant une série dexemples de tomographic radar puits à puits, j'étudie deux classes de stratégies de rééchantillonnage spatial en considérant leur habilité à générer efficacement et précisément des réalisations de la distribution postérieure bayésienne. Les résultats obtenus montrent que, malgré sa popularité, le réechantillonnage séquentiel est plutôt inefficace à générer des échantillons postérieurs indépendants pour des études de cas synthétiques réalistes, notamment pour le cas assez communs et importants où il existe de fortes corrélations spatiales entre le modèle et les paramètres. Pour résoudre ce problème, j'ai développé un nouvelle approche de perturbation basée sur une déformation progressive. Cette approche est flexible en ce qui concerne le nombre de paramètres du modèle et lintensité de la perturbation. Par rapport au rééchantillonage séquentiel, cette nouvelle approche s'avère être très efficace pour diminuer le nombre requis d'itérations pour générer des échantillons indépendants à partir de la distribution postérieure bayésienne. - Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending corresponding approaches beyond the local scale still represents a major challenge, yet is critically important for the development of reliable groundwater flow and contaminant transport models. To address this issue, I have developed a hydrogeophysical data integration technique based on a two-step Bayesian sequential simulation procedure that is specifically targeted towards larger-scale problems. The objective is to simulate the distribution of a target hydraulic parameter based on spatially exhaustive, but poorly resolved, measurements of a pertinent geophysical parameter and locally highly resolved, but spatially sparse, measurements of the considered geophysical and hydraulic parameters. To this end, my algorithm links the low- and high-resolution geophysical data via a downscaling procedure before relating the downscaled regional-scale geophysical data to the high-resolution hydraulic parameter field. I first illustrate the application of this novel data integration approach to a realistic synthetic database consisting of collocated high-resolution borehole measurements of the hydraulic and electrical conductivities and spatially exhaustive, low-resolution electrical conductivity estimates obtained from electrical resistivity tomography (ERT). The overall viability of this method is tested and verified by performing and comparing flow and transport simulations through the original and simulated hydraulic conductivity fields. The corresponding results indicate that the proposed data integration procedure does indeed allow for obtaining faithful estimates of the larger-scale hydraulic conductivity structure and reliable predictions of the transport characteristics over medium- to regional-scale distances. The approach is then applied to a corresponding field scenario consisting of collocated high- resolution measurements of the electrical conductivity, as measured using a cone penetrometer testing (CPT) system, and the hydraulic conductivity, as estimated from electromagnetic flowmeter and slug test measurements, in combination with spatially exhaustive low-resolution electrical conductivity estimates obtained from surface-based electrical resistivity tomography (ERT). The corresponding results indicate that the newly developed data integration approach is indeed capable of adequately capturing both the small-scale heterogeneity as well as the larger-scale trend of the prevailing hydraulic conductivity field. The results also indicate that this novel data integration approach is remarkably flexible and robust and hence can be expected to be applicable to a wide range of geophysical and hydrological data at all scale ranges. In the second part of my thesis, I evaluate in detail the viability of sequential geostatistical resampling as a proposal mechanism for Markov Chain Monte Carlo (MCMC) methods applied to high-dimensional geophysical and hydrological inverse problems in order to allow for a more accurate and realistic quantification of the uncertainty associated with the thus inferred models. Focusing on a series of pertinent crosshole georadar tomographic examples, I investigated two classes of geostatistical resampling strategies with regard to their ability to efficiently and accurately generate independent realizations from the Bayesian posterior distribution. The corresponding results indicate that, despite its popularity, sequential resampling is rather inefficient at drawing independent posterior samples for realistic synthetic case studies, notably for the practically common and important scenario of pronounced spatial correlation between model parameters. To address this issue, I have developed a new gradual-deformation-based perturbation approach, which is flexible with regard to the number of model parameters as well as the perturbation strength. Compared to sequential resampling, this newly proposed approach was proven to be highly effective in decreasing the number of iterations required for drawing independent samples from the Bayesian posterior distribution.