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.

Development of spatial integration depends on top-down and interhemispheric connections that can be perturbed in migraine: a DCM analysis.

In humans, spatial integration develops slowly, continuing through childhood into adolescence. On the assumption that this protracted course depends on the formation of networks with slowly developing top-down connections, we compared effective connectivity in the visual cortex between 13 children (age 7-13) and 14 adults (age 21-42) using a passive perceptual task. The subjects were scanned while viewing bilateral gratings, which either obeyed Gestalt grouping rules [colinear gratings (CG)] or violated them [non-colinear gratings (NG)]. The regions of interest for dynamic causal modeling were determined from activations in functional MRI contrasts stimuli > background and CG > NG. They were symmetrically located in V1 and V3v areas of both hemispheres. We studied a common model, which contained reciprocal intrinsic and modulatory connections between these regions. An analysis of effective connectivity showed that top-down modulatory effects generated at an extrastriate level and interhemispheric modulatory effects between primary visual areas (all inhibitory) are significantly weaker in children than in adults, suggesting that the formation of feedback and interhemispheric effective connections continues into adolescence. These results are consistent with a model in which spatial integration at an extrastriate level results in top-down messages to the primary visual areas, where they are supplemented by lateral (interhemispheric) messages, making perceptual encoding more efficient and less redundant. Abnormal formation of top-down inhibitory connections can lead to the reduction of habituation observed in migraine patients.

Angiotensinergic innervation of the kidney: Localization and relationship with catecholaminergic postganglionic and sensory nerve fibers.

We describe an angiotensin (Ang) II-containing innervation of the kidney. Cryosections of rat, pig and human kidneys were investigated for the presence of Ang II-containing nerve fibers using a mouse monoclonal antibody against Ang II (4B3). Co-staining was performed with antibodies against synaptophysin, tyrosine 3-hydroxylase, and dopamine beta-hydroxylase to detect catecholaminergic efferent fibers and against calcitonin gene-related peptide to detect sensory fibers. Tagged secondary antibodies and confocal light or laser scanning microscopy were used for immunofluorescence detection. Ang II-containing nerve fibers were densely present in the renal pelvis, the subepithelial layer of the urothelium, the arterial nervous plexus, and the peritubular interstitium of the cortex and outer medulla. They were infrequent in central veins and the renal capsule and absent within glomeruli and the renal papilla. Ang II-positive fibers represented phenotypic subgroups of catecholaminergic postganglionic or sensory fibers with different morphology and intrarenal distribution compared to their Ang II-negative counterparts. The Ang II-positive postganglionic fibers were thicker, produced typically fusiform varicosities and preferentially innervated the outer medulla and periglomerular arterioles. Ang II-negative sensory fibers were highly varicose, prevailing in the pelvis and scarce in the renal periphery compared to the rarely varicose Ang II-positive fibers. Neurons within renal microganglia displayed angiotensinergic, cate-cholaminergic, or combined phenotypes. Our results suggest that autonomic fibers may be an independent source of intrarenal Ang II acting as a neuropeptide co-transmitter or neuromodulator. The angiotensinergic renal innervation may play a distinct role in the neuronal control of renal sodium reabsorption, vasomotion and renin secretion.

Multiple-scale hydrological and geophysical data integration through non-linear Bayesian sequential simulation

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the scale of a field site represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed downscaling procedure based on a non-linear Bayesian sequential simulation approach. The main objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity logged at collocated wells and surface resistivity measurements, which are available throughout the studied site. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariatekernel density function. Then a stochastic integration of low-resolution, large-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities is applied. The overall viability of this downscaling approach is tested and validated by comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure allows obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models

The MIGCLIM R package is a function library for the open source R software that enables the implementation of species-specific dispersal constraints into projections of species distribution models under environmental change and/or landscape fragmentation scenarios. The model is based on a cellular automaton and the basic modeling unit is a cell that is inhabited or not. Model parameters include dispersal distance and kernel, long distance dispersal, barriers to dispersal, propagule production potential and habitat invasibility. The MIGCLIM R package has been designed to be highly flexible in the parameter values it accepts, and to offer good compatibility with existing species distribution modeling software. Possible applications include the projection of future species distributions under environmental change conditions and modeling the spread of invasive species.

Triiodothyronine exerts a trophic action on rat sensory neuron survival and neurite outgrowth through different pathways.

Apart from several growth factors which play a crucial role in the survival and development of the central and peripheral nervous systems, thyroid hormones can affect different processes involved in the differentiation and maturation of neurons. The present study was initiated to determine whether triiodothyronine (T3) affects the survival and neurite outgrowth of primary sensory neurons in vitro. Dorsal root ganglia (DRG) from 19-day-old embryos or newborn rats were plated in explant or dissociated cell cultures. The effect of T3 on neuron survival was tested, either in mixed DRG cell cultures, where neurons grow with non-neuronal cells, or in neuron-enriched cultures where non-neuronal cells were eliminated at the outset. T3, in physiological concentrations, promoted the growth of neurons in mixed DRG cell cultures as well as in neuron-enriched cultures without added nerve growth factor (NGF). Since neuron survival in neuron-enriched cultures cannot be promoted by endogenous neurotrophic factors synthesized by non-neuronal cells, the increased number of surviving neurons was due to a direct trophic action of T3. Another trophic effect was revealed in this study: T3 sustained the neurite outgrowth of sensory neurons in DRG explants. The stimulatory effect of T3 on nerve fibre outgrowth was considerably reduced when non-neuronal cell proliferation was inhibited by the antimitotic agent cytosine arabinoside, and was completely suppressed when the great majority of non-neuronal cells were eliminated in neuron-enriched cultures. These results indicate that the stimulatory effect of T3 on neurite outgrowth is mediated through non-neuronal cells. It is conceivable that T3 up-regulates Schwann cell expression of a neurotrophic factor, which in turn stimulates axon growth of sensory neurons. Together, these results demonstrate that T3 promotes both survival and neurite outgrowth of primary sensory neurons in DRG cell cultures. The trophic actions of T3 on neuron survival and neurite outgrowth operate under two different pathways.

Integration and development in schizotypy research : an introduction to the special supplement

In its fifth decade of existence, the construct of schizotypy is recapturing the early scientific interest it attracted when Paul E. Meehl (1920-2003), who coined the term, pioneered the field of schizotypy research. The International Lemanic Workshop on Schizotypy, hosted at the University of Geneva in December 2013, recently offered an opportunity to address some of the fundamental questions in contemporary schizotypy research and situate the construct in the greater scheme of future scientific projects on schizophrenia and psychological health research. What kind of knowledge has schizotypy research provided in furthering our understanding of schizophrenia? What types of questions can schizotypy research tackle, and which are the conceptual and methodological frameworks to address them? How will schizotypy research contribute to future scientific endeavors? The International Lemanic Workshop brought together leading experts in the field around the tasks of articulating the essential findings in schizotypy research, as well as providing some key insights and guidance to face scientific challenges of the future. The current supplement contains 8 position articles, 4 research articles, and 1 invited commentary that outline the state of the art in schizotypy research today

Mechanisms governing lentivirus integration site selection.

An essential step of the life cycle of retroviruses is the stable insertion of a copy of their DNA genome into the host cell genome, and lentiviruses are no exception. This integration step, catalyzed by the viral-encoded integrase, ensures long-term expression of the viral genes, thus allowing a productive viral replication and rendering retroviral vectors also attractive for the field of gene therapy. At the same time, this ability to integrate into the host genome raises safety concerns regarding the use of retroviral-based gene therapy vectors, due to the genomic locations of integration sites. The availability of the human genome sequence made possible the analysis of the integration site preferences, which revealed to be nonrandom and retrovirus-specific, i.e. all lentiviruses studied so far favor integration in active transcription units, while other retroviruses have a different integration site distribution. Several mechanisms have been proposed that may influence integration targeting, which include (i) chromatin accessibility, (ii) cell cycle effects, and (iii) tethering proteins. Recent data provide evidence that integration site selection can occur via a tethering mechanism, through the recruitment of the lentiviral integrase by the cellular LEDGF/p75 protein, both proteins being the two major players in lentiviral integration targeting.

Regional-scale integration of multi-scale hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying ?true? hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distances.

Interhemispheric coupling between the posterior sylvian regions impacts successful auditory temporal order judgment

Introduction: Accurate registration of the relative timing between the occurrence of sensory events on a sub-second time scale is crucial for both sensory-motor and cognitive functions (Mauk and Buonomano, 2004; Habib, 2000). Support for this assumption comes notably from evidence that temporal processing impairments are implicated in a range of neurological and psychiatric conditions (e.g. Buhusi & Meck, 2005). For instance, deficits in fast auditory temporal integration have been regularly put forward as resulting in phonologic discrimination impairments at the basis of speech comprehension deficits characterizing e.g. dyslexia (Habib, 2000). At least two aspects of the brain mechanisms of temporal order judgment remain unknown. First, it is unknown when during the course of stimulus processing a temporal ,,stamp‟ is established to guide TOJ perception. Second, the extent of interplay between the cerebral hemispheres in engendering accurate TOJ performance is unresolved Methods: We investigated the spatiotemporal brain dynamics of auditory temporal order judgment (aTOJ) using electrical neuroimaging analyses of auditory evoked potentials (AEPs) recorded while participants completed a near-threshold task requiring spatial discrimination of left-right and right-left sound sequences. Results: AEPs to sound pairs modulated topographically as a function of aTOJ accuracy over the 39-77ms post-stimulus period, indicating the engagement of distinct configurations of brain networks during early auditory processing stages. Source estimations revealed that accurate and inaccurate performance were linked to bilateral posterior sylvian regions activity (PSR). However, activity within left, but not right, PSR predicted behavioral performance suggesting that left PSR activity during early encoding phases of pairs of auditory spatial stimuli appears critical for the perception of their order of occurrence. Correlation analyses of source estimations further revealed that activity between left and right PSR was significantly correlated in the inaccurate but not accurate condition, indicating that aTOJ accuracy depends on the functional de-coupling between homotopic PSR areas. Conclusions: These results support a model of temporal order processing wherein behaviorally relevant temporal information - i.e. a temporal 'stamp'- is extracted within the early stages of cortical processes within left PSR but critically modulated by inputs from right PSR. We discuss our results with regard to current models of temporal of temporal order processing, namely gating and latency mechanisms.

Calbindin-immunoreactive sensory neurons in dissociated dorsal root ganglion cell cultures of chick embryo: role of culture conditions.

Immunoreactivity to calbindin D-28k, a vitamin D-dependent calcium-binding protein, is expressed by neuronal subpopulations of dorsal root ganglia (DRG) in the chick embryo. To determine whether the expression of this phenotypic characteristic is maintained in vitro and controlled by environmental factors, dissociated DRG cell cultures were performed under various conditions. Subpopulations of DRG cells cultured at embryonic day 10 displayed calbindin-immunoreactive cell bodies and neurites in both neuron-enriched or mixed DRG cell cultures. The number of calbindin-immunoreactive ganglion cells increased up to 7-10 days of culture independently of the changes occurring in the whole neuronal population. The presence of non-neuronal cells, which promotes the maturation of the sensory neurons, tended to reduce the percentage of calbindin-immunoreactive cell bodies. Addition of horse serum enhanced both the number of calbindin-positive neurons and the intensity of the immunostaining, but does not prevent the decline of the subpopulation of calbindin-immunoreactive neurons during the second week of culture; on the contrary, the addition of muscular extract to cultures at 10 days maintained the number of calbindin-expressing neurons. While calbindin-immunoreactive cell bodies grown in culture were small- or medium-sized, no correlation was found between cell size and immunostaining density. At the ultrastructural level, the calbindin immunoreaction was distributed throughout the neuroplasm. These results indicate that the expression of calbindin by sensory neurons grown in vitro may be modulated by horse serum-contained factors or interaction with non-neuronal cells. As distinct from horse serum, muscular extract is able to maintain the expression of calbindin by a subpopulation of DRG cells.