ALBERT

Beschreibung: Coal seam fires are a worldwide disaster of both ecological and economic importance. Their remote detection from the ground surface or using airborne techniques is required for developing efficient strategies to extinguish them. We investigate here the use of time-domain-induced polarization to localize coal seam fires. For laboratory experiments, we first introduce a modified time-domain-induced polarization methodology to quickly acquire and invert the secondary voltage distribution mapped after the shutdown of the primary current. A set of sandbox experiments is conducted in which coal is embedded into humidified sand. Raw coal alone generates significant induced polarization anomalies, above those shown by the sand. Even higher induced polarization anomalies are detected in presence of a coal seam fire. We postulate that the higher chargeability is due to the pyrolysis, which may enhance electronic polarization or the polarization associated with the cation exchange capacity (CEC) of the material. The position of the coal seam fire is well recovered inside the tank by inverting the secondary voltages in term of a source current density distribution. We also collected field data over a recognized coal seam fire in Colorado, USA. A chargeability anomaly (~800 mV V –1 ) and a resistivity anomaly (~1 Ohm m) are observed at the position of the coal seam fire. We propose a normalized burning front index (a scaled normalized chargeability) to image and localize, without ambiguity, the position of the coal seam fire in the subsurface. The 3-D reconstructed target is located below a negative self-potential anomaly (similarly to what is observed in laboratory experiments) and a temperature anomaly recorded at a depth of 30 cm.

Beschreibung: RNA molecules are attractive therapeutic targets because non-coding RNA molecules have increasingly been found to play key regulatory roles in the cell. Comparing and classifying RNA 3D structures yields unique insights into RNA evolution and function. With the rapid increase in the number of atomic-resolution RNA structures, it is crucial to have effective tools to classify RNA structures and to investigate them for structural similarities at different resolutions. We previously developed the algorithm CLICK to superimpose a pair of protein 3D structures by clique matching and 3D least squares fitting. In this study, we extend and optimize the CLICK algorithm to superimpose pairs of RNA 3D structures and RNA–protein complexes, independent of the associated topologies. Benchmarking Rclick on four different datasets showed that it is either comparable to or better than other structural alignment methods in terms of the extent of structural overlaps. Rclick also recognizes conformational changes between RNA structures and produces complementary alignments to maximize the extent of detectable similarity. Applying Rclick to study Ribonuclease III protein correctly aligned the RNA binding sites of RNAse III with its substrate. Rclick can be further extended to identify ligand-binding pockets in RNA. A web server is developed at http://mspc.bii.a-star.edu.sg/minhn/rclick.html .

Beschreibung: Joint inversion that simultaneously inverts multiple geophysical data sets to recover a common Earth model is increasingly being applied to exploration problems. Petrophysical data can serve as an effective constraint to link different physical property models in such inversions. There are two challenges, among others, associated with the petrophysical approach to joint inversion. One is related to the multimodality of petrophysical data because there often exist more than one relationship between different physical properties in a region of study. The other challenge arises from the fact that petrophysical relationships have different characteristics and can exhibit point, linear, quadratic, or exponential forms in a crossplot. The fuzzy c-means (FCM) clustering technique is effective in tackling the first challenge and has been applied successfully. We focus on the second challenge in this paper and develop a joint inversion method based on variations of the FCM clustering technique. To account for the specific shapes of petrophysical relationships, we introduce several different fuzzy clustering algorithms that are capable of handling different shapes of petrophysical relationships. We present two synthetic and one field data examples and demonstrate that, by choosing appropriate distance measures for the clustering component in the joint inversion algorithm, the proposed joint inversion method provides an effective means of handling common petrophysical situations we encounter in practice. The jointly inverted models have both enhanced structural similarity and increased petrophysical correlation, and better represent the subsurface in the spatial domain and the parameter domain of physical properties.

Beschreibung: In general, the complex electrical resistivity in the subsurface is anisotropic. Despite this, algorithms for the tomographic inversion of complex resistivity data commonly assume isotropy, mainly due to the lack of anisotropic modelling and inversion schemes, potentially leading to artefacts in the inversion results in the presence of anisotropy. The development of an effective anisotropic complex resistivity inversion algorithm which utilizes the gradient information of some cost function benefits from understanding the characteristics of the problem's sensitivities, that is, the partial derivative of the impedance forward response with respect to the complex conductivities in the different spatial directions, as well as with respect to the different ratios of complex conductivities, that is, the different anisotropy ratios. We here derive expressions for these sensitivities and, based on a 2.5-D finite-element modelling algorithm, we compute and discuss sensitivity distributions as well as measurement response curves of typical surface and cross-borehole measurement configurations for 2-D subsurface anisotropic complex resistivity distributions. Depending on the electrode layout and measurement configuration, the sensitivity with respect to the conductivity in a particular direction shows a unique pattern, while for other directions sensitivity patterns are qualitatively similar. These sensitivity characteristics translate into important equivalences between impedance responses of local anisotropic and isotropic anomalies, for both magnitude and phase. Accordingly, with collinear surface arrays only the complex conductivity in the direction of the electrode layout can be unambiguously resolved, and with cross-borehole arrays only the conductivity in the vertical direction, provided an in-hole current injection is used. Nevertheless, anisotropy ratios involving these resolvable conductivity components are likewise detectable. The distinct shape of the measurement response curves, reflecting the distinct spatial patterns of the corresponding sensitivity distributions, suggest that optimized measurement configurations can be inferred for specific exploration questions involving electrical anisotropy and given electrode layouts. The gained insight into the characteristics of the sensitivity distributions of complex resistivity measurements in case of subsurface anisotropy should guide the implementation of effective anisotropic complex resistivity inversion schemes and lead to a routine use of such schemes in any resistivity and induced polarization surveys whenever subsurface electrical anisotropy could be encountered.

Beschreibung: The streaming potential phenomenon is an electrokinetic effect that occurs in porous media. It is characterized by an electrokinetic (EK) coefficient. The aim of this paper is to simulate the EK coefficient in unsaturated conditions using the Lattice Boltzmann method in a 2-D capillary channel. The multiphase flow is simulated with the model of Shan & Chen. The Poisson–Boltzmann equation is solved by implementing the model of Chai & Shi. The streaming potential response shows a non-monotonous behaviour due to the combination of the increase of charge density and decrease of flow velocity with decreasing water saturation. Using a potential of –20 mV at the air–water interface, an enhancement of a factor 5–30 of the EK coefficient, compared to the saturated state, can be observed due to the positive charge excess at this interface which is magnified by the fluid velocity away from the rock surface. This enhancement is correlated to the fractioning of the bubbles, and to the dynamic state of these bubbles, moving or entrapped in the crevices of the channel.

Beschreibung: In recent years, marine controlled source electromagnetics (CSEM) has found increasing use in hydrocarbon exploration due to its ability to detect thin resistive zones beneath the seafloor. It is the purpose of this paper to evaluate the physics of CSEM for an ocean whose electrical thickness is comparable to or much thinner than that of the overburden using the in-line configuration through examination of the elliptically polarized seafloor electric field, the time-averaged energy flow depicted by the real part of the complex Poynting vector, energy dissipation through Joule heating and the Fréchet derivatives of the seafloor field with respect to the subseafloor conductivity that is assumed to be isotropic. The deep water (ocean layer electrically much thicker than the overburden) seafloor EM response for a model containing a resistive reservoir layer has a greater amplitude and reduced phase as a function of offset compared to that for a half-space, or a stronger and faster response. For an ocean whose electrical thickness is comparable to or much smaller than that of the overburden, the electric field displays a greater amplitude and reduced phase at small offsets, shifting to a stronger amplitude and increased phase at intermediate offsets and a weaker amplitude and enhanced phase at long offsets, or a stronger and faster response that first changes to stronger and slower, and then transitions to weaker and slower. These transitions can be understood by visualizing the energy flow throughout the structure caused by the competing influences of the dipole source and guided energy flow in the reservoir layer, and the air interaction caused by coupling of the entire subseafloor resistivity structure with the sea surface. A stronger and faster response occurs when guided energy flow is dominant, while a weaker and slower response occurs when the air interaction is dominant. However, at intermediate offsets for some models, the air interaction can partially or fully reverse the direction of energy flux in the reservoir layer toward rather than away from the source, resulting in a stronger and slower response. The Fréchet derivatives are dominated by preferential sensitivity to the reservoir layer conductivity for all water depths except at high frequencies, but also display a shift with offset from the galvanic to the inductive mode in the underburden and overburden due to the interplay of guided energy flow and the air interaction. This means that the sensitivity to the horizontal conductivity is almost as strong as to the vertical component in the shallow parts of the subsurface, and in fact is stronger than the vertical sensitivity deeper down. However, the sensitivity to horizontal conductivity is still weak compared to the vertical component within thin resistive regions. The horizontal sensitivity is gradually decreased when the water becomes deep. These observations in part explain the success of shallow towed CSEM using only measurements of the in-line component of the electric field.

Beschreibung: In this work, we study seismoelectric conversions generated in the vadose zone, when this region is traversed by a pure SH wave. We assume that the soil is a 1-D partially saturated lossy porous medium and we use the van Genuchten's constitutive model to describe the water saturation profile. Correspondingly, we extend Pride's formulation to deal with partially saturated media. In order to evaluate the influence of different soil textures we perform a numerical analysis considering, among other relevant properties, the electrokinetic coupling, coseismic responses and interface responses (IRs). We propose new analytical transfer functions for the electric and magnetic field as a function of the water saturation, modifying those of Bordes et al. and Garambois & Dietrich, respectively. Further, we introduce two substantially different saturation-dependent functions into the electrokinetic (EK) coupling linking the poroelastic and the electromagnetic wave equations. The numerical results show that the electric field IRs markedly depend on the soil texture and the chosen EK coupling model, and are several orders of magnitude stronger than the electric field coseismic ones. We also found that the IRs of the water table for the silty and clayey soils are stronger than those for the sandy soils, assuming a non-monotonous saturation dependence of the EK coupling, which takes into account the charged air–water interface. These IRs have been interpreted as the result of the jump in the viscous electric current density at the water table. The amplitude of the IR is obtained using a plane SH wave, neglecting both the spherical spreading and the restriction of its origin to the first Fresnel zone, effects that could lower the predicted values. However, we made an estimation of the expected electric field IR amplitudes detectable in the field by means of the analytical transfer functions, accounting for spherical spreading of the SH seismic waves. This prediction yields a value of 15 μV m –1 , which is compatible with reported values.

Beschreibung: Surface nuclear magnetic resonance (NMR) is a unique geophysical method due to its direct sensitivity to water. A key limitation to overcome is the difficulty of making surface NMR measurements in environments with anthropogenic electromagnetic noise, particularly constant frequency sources such as powerlines. Here we present a method of removing harmonic noise by utilizing frequency domain symmetry of surface NMR signals to reconstruct portions of the spectrum corrupted by frequency-domain noise peaks. This method supplements the existing NMR processing workflow and is applicable after despiking, coherent noise cancellation, and stacking. The symmetry based correction is simple, grounded in mathematical theory describing NMR signals, does not introduce errors into the data set, and requires no prior knowledge about the harmonics. Modelling and field examples show that symmetry based noise removal reduces the effects of harmonics. In one modelling example, symmetry based noise removal improved signal-to-noise ratio in the data by 10 per cent. This improvement had noticeable effects on inversion parameters including water content and the decay constant T 2 *. Within water content profiles, aquifer boundaries and water content are more accurate after harmonics are removed. Fewer spurious water content spikes appear within aquifers, which is especially useful for resolving multilayered structures. Within T 2 * profiles, estimates are more accurate after harmonics are removed, especially in the lower half of profiles.

Beschreibung: A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.

Beschreibung: Motivation: Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data. There is a great potential for misclassifying non-rhythmic as rhythmic genes and vice versa. This has been a problem of considerable interest in recent years. In this article we develop a constrained inference based methodology called Order Restricted Inference for Oscillatory Systems (ORIOS) to detect rhythmic signals. Instead of using mathematical functions (e.g. sinusoidal) to describe shape of rhythmic signals, ORIOS uses mathematical inequalities. Consequently, it is robust and not limited by the biologist's choice of the mathematical model. We studied the performance of ORIOS using simulated as well as real data obtained from mouse liver, pituitary gland and data from NIH3T3, U2OS cell lines. Our results suggest that, for a broad collection of patterns of gene expression, ORIOS has substantially higher power to detect true rhythmic genes in comparison to some popular methods, while also declaring substantially fewer non-rhythmic genes as rhythmic. Availability and Implementation: A user friendly code implemented in R language can be downloaded from http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/peddada/index.cfm . Contact: peddada@niehs.nih.gov