Fracture network characterization using hydrological and geophysical data

Les décisions de gestion des eaux souterraines doivent souvent être justiffées par des modèles quantitatifs d'aquifères qui tiennent compte de l'hétérogénéité des propriétés hydrauliques. Les aquifères fracturés sont parmi les plus hétérogènes et très difficiles à étudier. Dans ceux-ci, les fractures connectées, d'ouverture millimètrique, peuvent agir comme conducteurs hydrauliques et donc créer des écoulements très localisés. Le manque général d'informations sur la distribution spatiale des fractures limite la possibilité de construire des modèles quantitatifs de flux et de transport. Les données qui conditionnent les modèles sont généralement spatialement limitées, bruitées et elles ne représentent que des mesures indirectes de propriétés physiques. Ces limitations aux données peuvent être en partie surmontées en combinant différents types de données, telles que les données hydrologiques et de radar à pénétration de sol plus commun ément appelé géoradar. L'utilisation du géoradar en forage est un outil prometteur pour identiffer les fractures individuelles jusqu'à quelques dizaines de mètres dans la formation. Dans cette thèse, je développe des approches pour combiner le géoradar avec les données hydrologiques affn d'améliorer la caractérisation des aquifères fracturés. Des investigations hydrologiques intensives ont déjà été réalisées à partir de trois forage adjacents dans un aquifère cristallin en Bretagne (France). Néanmoins, la dimension des fractures et la géométrie 3-D des fractures conductives restaient mal connue. Affn d'améliorer la caractérisation du réseau de fractures je propose dans un premier temps un traitement géoradar avancé qui permet l'imagerie des fractures individuellement. Les résultats montrent que les fractures perméables précédemment identiffées dans les forages peuvent être caractérisées géométriquement loin du forage et que les fractures qui ne croisent pas les forages peuvent aussi être identiffées. Les résultats d'une deuxième étude montrent que les données géoradar peuvent suivre le transport d'un traceur salin. Ainsi, les fractures qui font partie du réseau conductif et connecté qui dominent l'écoulement et le transport local sont identiffées. C'est la première fois que le transport d'un traceur salin a pu être imagé sur une dizaines de mètres dans des fractures individuelles. Une troisième étude conffrme ces résultats par des expériences répétées et des essais de traçage supplémentaires dans différentes parties du réseau local. En outre, la combinaison des données de surveillance hydrologique et géoradar fournit la preuve que les variations temporelles d'amplitude des signaux géoradar peuvent nous informer sur les changements relatifs de concentrations de traceurs dans la formation. Par conséquent, les données géoradar et hydrologiques sont complémentaires. Je propose ensuite une approche d'inversion stochastique pour générer des modèles 3-D de fractures discrètes qui sont conditionnés à toutes les données disponibles en respectant leurs incertitudes. La génération stochastique des modèles conditionnés par géoradar est capable de reproduire les connexions hydrauliques observées et leur contribution aux écoulements. L'ensemble des modèles conditionnés fournit des estimations quantitatives des dimensions et de l'organisation spatiale des fractures hydrauliquement importantes. Cette thèse montre clairement que l'imagerie géoradar est un outil utile pour caractériser les fractures. La combinaison de mesures géoradar avec des données hydrologiques permet de conditionner avec succès le réseau de fractures et de fournir des modèles quantitatifs. Les approches présentées peuvent être appliquées dans d'autres types de formations rocheuses fracturées où la roche est électriquement résistive.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Multilayer perceptron with local constraint as an emerging method in spatial data analysis

The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

Pore space analysis of beech wood: The vessel network

Water transport in wood is vital for the survival of trees. With synchrotron radiation X-ray tomographic microscopy (SRXTM), it has become possible to characterize and quantify the three-dimensional (3D) network formed by vessels that are responsible for longitudinal transport. In the present study, the spatial size dependence of vessels and the organization inside single growth rings in terms of vessel-induced porosity was studied by SRXTM. Network characteristics, such as connectivity, were deduced by digital image analysis from the processed tomographic data and related to known complex network topologies.

SYSGENET: a meeting report from a new European network for systems genetics.

The first scientific meeting of the newly established European SYSGENET network took place at the Helmholtz Centre for Infection Research (HZI) in Braunschweig, April 7-9, 2010. About 50 researchers working in the field of systems genetics using mouse genetic reference populations (GRP) participated in the meeting and exchanged their results, phenotyping approaches, and data analysis tools for studying systems genetics. In addition, the future of GRP resources and phenotyping in Europe was discussed.

Prevalence at Birth of Cleft Lip With or Without Cleft Palate: Data From the International Perinatal Database of Typical Oral Clefts (IPDTOC).

As part of a collaborative project on the epidemiology of craniofacial anomalies, funded by the National Institutes for Dental and Craniofacial Research and channeled through the Human Genetics Programme of the World Health Organization, the International Perinatal Database of Typical Orofacial Clefts (IPDTOC) was established in 2003. IPDTOC is collecting case-by-case information on cleft lip with or without cleft palate and on cleft palate alone from birth defects registries contributing to at least one of three collaborative organizations: European Surveillance Systems of Congenital Anomalies (EUROCAT) in Europe, National Birth Defects Prevention Network (NBDPN) in the United States, and International Clearinghouse for Birth Defects Surveillance and Research (ICBDSR) worldwide. Analysis of the collected information is performed centrally at the ICBDSR Centre in Rome, Italy, to maximize the comparability of results. The present paper, the first of a series, reports data on the prevalence of cleft lip with or without cleft palate from 54 registries in 30 countries over at least 1 complete year during the period 2000 to 2005. Thus, the denominator comprises more than 7.5 million births. A total of 7704 cases of cleft lip with or without cleft palate (7141 livebirths, 237 stillbirths, 301 terminations of pregnancy, and 25 with pregnancy outcome unknown) were available. The overall prevalence of cleft lip with or without cleft palate was 9.92 per 10,000. The prevalence of cleft lip was 3.28 per 10,000, and that of cleft lip and palate was 6.64 per 10,000. There were 5918 cases (76.8%) that were isolated, 1224 (15.9%) had malformations in other systems, and 562 (7.3%) occurred as part of recognized syndromes. Cases with greater dysmorphological severity of cleft lip with or without cleft palate were more likely to include malformations of other systems.

Inflammation-induced alteration of astrocyte mitochondrial dynamics requires autophagy for mitochondrial network maintenance.

Accumulating evidence suggests that changes in the metabolic signature of astrocytes underlie their response to neuroinflammation, but how proinflammatory stimuli induce these changes is poorly understood. By monitoring astrocytes following acute cortical injury, we identified a differential and region-specific remodeling of their mitochondrial network: while astrocytes within the penumbra of the lesion undergo mitochondrial elongation, those located in the core-the area invaded by proinflammatory cells-experience transient mitochondrial fragmentation. In brain slices, proinflammatory stimuli reproduced localized changes in mitochondrial dynamics, favoring fission over fusion. This effect was triggered by Drp1 phosphorylation and ultimately resulted in reduced respiratory capacity. Furthermore, maintenance of the mitochondrial architecture critically depended on the induction of autophagy. Deletion of Atg7, required for autophagosome formation, prevented the reestablishment of tubular mitochondria, leading to marked reactive oxygen species accumulation and cell death. Thus, our data reveal autophagy to be essential for regenerating astrocyte mitochondrial networks during inflammation.

Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model.

MOTIVATION: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes. RESULTS: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as 'stepping stones' for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or 'trigger' is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cruciferous vegetables and cancer risk in a network of case-control studies.

Background Cruciferous vegetables have been suggested to protect against various cancers, though the issue is open to discussion. To further understand their role, we analyzed data from a network of case-control studies conducted in Italy and Switzerland. Patients and methods The studies included a total of 1468 cancers of the oral cavity/pharynx, 505 of the esophagus, 230 of the stomach, 2390 of the colorectum, 185 of the liver, 326 of the pancreas, 852 of the larynx, 3034 of the breast, 367 of the endometrium, 1031 of the ovary, 1294 of the prostate, 767 of the kidney, and 11 492 controls. All cancers were incident, histologically confirmed; controls were subjects admitted to the same network of hospitals as cases for a wide spectrum of acute nonneoplastic conditions. Results The multivariate odds ratio (OR) for consumption of cruciferous vegetables at least once a week as compared with no/occasional consumption was significantly reduced for cancer of the oral cavity/pharynx (OR = 0.83), esophagus (OR = 0.72), colorectum (OR = 0.83), breast (OR = 0.83), and kidney (OR = 0.68). The OR was below unity, but not significant, for stomach (OR = 0.90), liver (OR = 0.72), pancreatic (OR = 0.90), laryngeal (OR = 0.84), endometrial (OR = 0.93), ovarian (OR = 0.91), and prostate (OR = 0.87) cancer. Conclusion This large series of studies provides additional evidence of a favorable effect of cruciferous vegetables on several common cancers.

Paper 3: EUROCAT data quality indicators for population-based registries of congenital anomalies.

The European Surveillance of Congenital Anomalies (EUROCAT) network of population-based congenital anomaly registries is an important source of epidemiologic information on congenital anomalies in Europe covering live births, fetal deaths from 20 weeks gestation, and terminations of pregnancy for fetal anomaly. EUROCAT's policy is to strive for high-quality data, while ensuring consistency and transparency across all member registries. A set of 30 data quality indicators (DQIs) was developed to assess five key elements of data quality: completeness of case ascertainment, accuracy of diagnosis, completeness of information on EUROCAT variables, timeliness of data transmission, and availability of population denominator information. This article describes each of the individual DQIs and presents the output for each registry as well as the EUROCAT (unweighted) average, for 29 full member registries for 2004-2008. This information is also available on the EUROCAT website for previous years. The EUROCAT DQIs allow registries to evaluate their performance in relation to other registries and allows appropriate interpretations to be made of the data collected. The DQIs provide direction for improving data collection and ascertainment, and they allow annual assessment for monitoring continuous improvement. The DQI are constantly reviewed and refined to best document registry procedures and processes regarding data collection, to ensure appropriateness of DQI, and to ensure transparency so that the data collected can make a substantial and useful contribution to epidemiologic research on congenital anomalies.

Gene expression databases and data mining.

The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study. The majority of the initial technical challenges of conducting experiments are being resolved, only to be replaced with new informatics hurdles, including statistical analysis, data visualization, interpretation, and storage. Two systems of databases, one containing expression data and one containing annotation data are quickly becoming essential knowledge repositories of the research community. This present paper surveys several databases, which are considered "pillars" of research and important nodes in the network. This paper focuses on a generalized workflow scheme typical for microarray experiments using two examples related to cancer research. The workflow is used to reference appropriate databases and tools for each step in the process of array experimentation. Additionally, benefits and drawbacks of current array databases are addressed, and suggestions are made for their improvement.

Quantifying network properties in multi-electrode recordings: spatiotemporal characterization and inter-trial variation of evoked gamma oscillations in mouse somatosensory cortex in vitro.

Linking the structural connectivity of brain circuits to their cooperative dynamics and emergent functions is a central aim of neuroscience research. Graph theory has recently been applied to study the structure-function relationship of networks, where dynamical similarity of different nodes has been turned into a "static" functional connection. However, the capability of the brain to adapt, learn and process external stimuli requires a constant dynamical functional rewiring between circuitries and cell assemblies. Hence, we must capture the changes of network functional connectivity over time. Multi-electrode array data present a unique challenge within this framework. We study the dynamics of gamma oscillations in acute slices of the somatosensory cortex from juvenile mice recorded by planar multi-electrode arrays. Bursts of gamma oscillatory activity lasting a few hundred milliseconds could be initiated only by brief trains of electrical stimulations applied at the deepest cortical layers and simultaneously delivered at multiple locations. Local field potentials were used to study the spatio-temporal properties and the instantaneous synchronization profile of the gamma oscillatory activity, combined with current source density (CSD) analysis. Pair-wise differences in the oscillation phase were used to determine the presence of instantaneous synchronization between the different sites of the circuitry during the oscillatory period. Despite variation in the duration of the oscillatory response over successive trials, they showed a constant average power, suggesting that the rate of expenditure of energy during the gamma bursts is consistent across repeated stimulations. Within each gamma burst, the functional connectivity map reflected the columnar organization of the neocortex. Over successive trials, an apparently random rearrangement of the functional connectivity was observed, with a more stable columnar than horizontal organization. This work reveals new features of evoked gamma oscillations in developing cortex.

GLUT2 surface expression and intracellular transport via the constitutive pathway in pancreatic beta cells and insulinoma: evidence for a block in trans-Golgi network exit by brefeldin A.

The biosynthesis, intracellular transport, and surface expression of the beta cell glucose transporter GLUT2 was investigated in isolated islets and insulinoma cells. Using a trypsin sensitivity assay to measure cell surface expression, we determined that: (a) greater than 95% of GLUT2 was expressed on the plasma membrane; (b) GLUT2 did not recycle in intracellular vesicles; and (c) after trypsin treatment, reexpression of the intact transporter occurred with a t1/2 of approximately 7 h. Kinetics of intracellular transport of GLUT2 was investigated in pulse-labeling experiments combined with glycosidase treatment and the trypsin sensitivity assay. We determined that transport from the endoplasmic reticulum to the trans-Golgi network (TGN) occurred with a t1/2 of 15 min and that transport from the TGN to the plasma membrane required a similar half-time. When added at the start of a pulse-labeling experiment, brefeldin A prevented exit of GLUT2 from the endoplasmic reticulum. When the transporter was first accumulated in the TGN during a 15-min period of chase, but not following a low temperature (22 degrees C) incubation, addition of brefeldin A (BFA) prevented subsequent surface expression of the transporter. This indicated that brefeldin A prevented GLUT2 exit from the TGN by acting at a site proximal to the 22 degrees C block. Together, these data demonstrate that GLUT2 surface expression in beta cells is via the constitutive pathway, that transport can be blocked by BFA at two distinct steps and that once on the surface, GLUT2 does not recycle in intracellular vesicles.

Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.

Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.

Conditioning of stochastic 3-D fracture networks to hydrological and geophysical data

The geometry and connectivity of fractures exert a strong influence on the flow and transport properties of fracture networks. We present a novel approach to stochastically generate three-dimensional discrete networks of connected fractures that are conditioned to hydrological and geophysical data. A hierarchical rejection sampling algorithm is used to draw realizations from the posterior probability density function at different conditioning levels. The method is applied to a well-studied granitic formation using data acquired within two boreholes located 6 m apart. The prior models include 27 fractures with their geometry (position and orientation) bounded by information derived from single-hole ground-penetrating radar (GPR) data acquired during saline tracer tests and optical televiewer logs. Eleven cross-hole hydraulic connections between fractures in neighboring boreholes and the order in which the tracer arrives at different fractures are used for conditioning. Furthermore, the networks are conditioned to the observed relative hydraulic importance of the different hydraulic connections by numerically simulating the flow response. Among the conditioning data considered, constraints on the relative flow contributions were the most effective in determining the variability among the network realizations. Nevertheless, we find that the posterior model space is strongly determined by the imposed prior bounds. Strong prior bounds were derived from GPR measurements and helped to make the approach computationally feasible. We analyze a set of 230 posterior realizations that reproduce all data given their uncertainties assuming the same uniform transmissivity in all fractures. The posterior models provide valuable statistics on length scales and density of connected fractures, as well as their connectivity. In an additional analysis, effective transmissivity estimates of the posterior realizations indicate a strong influence of the DFN structure, in that it induces large variations of equivalent transmissivities between realizations. The transmissivity estimates agree well with previous estimates at the site based on pumping, flowmeter and temperature data.