Estimation of inelastic seismic material properties of a surgical sea-bed from multi-component marine seismic data

To date, state-of-the-art seismic material parameter estimates from multi-component sea-bed seismic data are based on the assumption that the sea-bed consists of a fully elastic half-space. In reality, however, the shallow sea-bed generally consists of soft, unconsolidated sediments that are characterized by strong to very strong seismic attenuation. To explore the potential implications, we apply a state-of-the-art elastic decomposition algorithm to synthetic data for a range of canonical sea-bed models consisting of a viscoelastic half-space of varying attenuation. We find that in the presence of strong seismic attenuation, as quantified by Q-values of 10 or less, significant errors arise in the conventional elastic estimation of seismic properties. Tests on synthetic data indicate that these errors can be largely avoided by accounting for the inherent attenuation of the seafloor when estimating the seismic parameters. This can be achieved by replacing the real-valued expressions for the elastic moduli in the governing equations in the parameter estimation by their complex-valued viscoelastic equivalents. The practical application of our parameter procedure yields realistic estimates of the elastic seismic material properties of the shallow sea-bed, while the corresponding Q-estimates seem to be biased towards too low values, particularly for S-waves. Given that the estimation of inelastic material parameters is notoriously difficult, particularly in the immediate vicinity of the sea-bed, this is expected to be of interest and importance for civil and ocean engineering purposes.

Intracellular nanomanipulation by a photonic-force microscope with real-time acquisition of a 3D stiffness matrix.

A traditional photonic-force microscope (PFM) results in huge sets of data, which requires tedious numerical analysis. In this paper, we propose instead an analog signal processor to attain real-time capabilities while retaining the richness of the traditional PFM data. Our system is devoted to intracellular measurements and is fully interactive through the use of a haptic joystick. Using our specialized analog hardware along with a dedicated algorithm, we can extract the full 3D stiffness matrix of the optical trap in real time, including the off-diagonal cross-terms. Our system is also capable of simultaneously recording data for subsequent offline analysis. This allows us to check that a good correlation exists between the classical analysis of stiffness and our real-time measurements. We monitor the PFM beads using an optical microscope. The force-feedback mechanism of the haptic joystick helps us in interactively guiding the bead inside living cells and collecting information from its (possibly anisotropic) environment. The instantaneous stiffness measurements are also displayed in real time on a graphical user interface. The whole system has been built and is operational; here we present early results that confirm the consistency of the real-time measurements with offline computations.

Detection of four Plasmodium species in blood from humans by 18S rRNA gene subunit-based and specific real-time PCR assays

Résumé : Un nombre croissant de cas de malaria chez les voyageurs et migrants a été rapporté. Bien que l'analyse microscopique des frottis sanguins reste traditionnellement l'outil diagnostic de référence, sa fiabilité dépend considérablement de l'expertise de l'examinateur, pouvant elle-même faire défaut sous nos latitudes. Une PCR multiplex en temps réel a donc été développée en vue d'une standardisation du diagnostic. Un ensemble d'amorces génériques ciblant une région hautement conservée du gène d'ARN ribosomial 18S du genre Plasmodium a tout d'abord été conçu, dont le polymorphisme du produit d'amplification semblait suffisant pour créer quatre sondes spécifiques à l'espèce P. falciparum, P. malariae, P. vivax et P. ovale. Ces sondes utilisées en PCR en temps réel se sont révélées capables de détecter une seule copie de plasmide de P. falciparum, P. malariae, P. vivax et P. ovale spécifiquement. La même sensibilité a été obtenue avec une sonde de screening pouvant détecter les quatre espèces. Quatre-vingt-dix-sept échantillons de sang ont ensuite été testés, dont on a comparé la microscopie et la PCR en temps réel pour 66 (60 patients) d'entre eux. Ces deux méthodes ont montré une concordance globale de 86% pour la détection de plasmodia. Les résultats discordants ont été réévalués grâce à des données cliniques, une deuxième expertise microscopique et moléculaire (laboratoire de Genève et de l'Institut Suisse Tropical de Bâle), ainsi qu'à l'aide du séquençage. Cette nouvelle analyse s'est prononcé en faveur de la méthode moléculaire pour tous les neuf résultats discordants. Sur les 31 résultats positifs par les deux méthodes, la même réévaluation a pu donner raison 8 fois sur 9 à la PCR en temps réel sur le plan de l'identification de l'espèce plasmodiale. Les 31 autres échantillons ont été analysés pour le suivi de sept patients sous traitement antimalarique. Il a été observé une baisse rapide du nombre de parasites mesurée par la PCR en temps réel chez six des sept patients, baisse correspondant à la parasitémie déterminée microscopiquement. Ceci suggère ainsi le rôle potentiel de la PCR en temps réel dans le suivi thérapeutique des patients traités par antipaludéens. Abstract : There have been reports of increasing numbers of cases of malaria among migrants and travelers. Although microscopic examination of blood smears remains the "gold standard" in diagnosis, this method suffers from insufficient sensitivity and requires considerable expertise. To improve diagnosis, a multiplex real-time PCR was developed. One set of generic primers targeting a highly conserved region of the 18S rRNA gene of the genus Plasmodium was designed; the primer set was polymorphic enough internally to design four species-specific probes for P. falciparum, P. vivax, P. malarie, and P. ovale. Real-time PCR with species-specific probes detected one plasmid copy of P. falciparum, P. vivax, P. malariae, and P. ovale specifically. The same sensitivity was achieved for all species with real-time PCR with the 18S screening probe. Ninety-seven blood samples were investigated. For 66 of them (60 patients), microscopy and real-time PCR results were compared and had a crude agreement of 86% for the detection of plasmodia. Discordant results were reevaluated with clinical, molecular, and sequencing data to resolve them. All nine discordances between 18S screening PCR and microscopy were resolved in favor of the molecular method, as were eight of nine discordances at the species level for the species-specific PCR among the 31 samples positive by both methods. The other 31 blood samples were tested to monitor the antimalaria treatment in seven patients. The number of parasites measured by real-time PCR fell rapidly for six out of seven patients in parallel to parasitemia determined microscopically. This suggests a role of quantitative PCR for the monitoring of patients receiving antimalaria therapy.

Source wavelet estimation for waveform inversion of crosshole georadar data

AbstractFor a wide range of environmental, hydrological, and engineering applications there is a fast growing need for high-resolution imaging. In this context, waveform tomographic imaging of crosshole georadar data is a powerful method able to provide images of pertinent electrical properties in near-surface environments with unprecedented spatial resolution. In contrast, conventional ray-based tomographic methods, which consider only a very limited part of the recorded signal (first-arrival traveltimes and maximum first-cycle amplitudes), suffer from inherent limitations in resolution and may prove to be inadequate in complex environments. For a typical crosshole georadar survey the potential improvement in resolution when using waveform-based approaches instead of ray-based approaches is in the range of one order-of- magnitude. Moreover, the spatial resolution of waveform-based inversions is comparable to that of common logging methods. While in exploration seismology waveform tomographic imaging has become well established over the past two decades, it is comparably still underdeveloped in the georadar domain despite corresponding needs. Recently, different groups have presented finite-difference time-domain waveform inversion schemes for crosshole georadar data, which are adaptations and extensions of Tarantola's seminal nonlinear generalized least-squares approach developed for the seismic case. First applications of these new crosshole georadar waveform inversion schemes on synthetic and field data have shown promising results. However, there is little known about the limits and performance of such schemes in complex environments. To this end, the general motivation of my thesis is the evaluation of the robustness and limitations of waveform inversion algorithms for crosshole georadar data in order to apply such schemes to a wide range of real world problems.One crucial issue to making applicable and effective any waveform scheme to real-world crosshole georadar problems is the accurate estimation of the source wavelet, which is unknown in reality. Waveform inversion schemes for crosshole georadar data require forward simulations of the wavefield in order to iteratively solve the inverse problem. Therefore, accurate knowledge of the source wavelet is critically important for successful application of such schemes. Relatively small differences in the estimated source wavelet shape can lead to large differences in the resulting tomograms. In the first part of my thesis, I explore the viability and robustness of a relatively simple iterative deconvolution technique that incorporates the estimation of the source wavelet into the waveform inversion procedure rather than adding additional model parameters into the inversion problem. Extensive tests indicate that this source wavelet estimation technique is simple yet effective, and is able to provide remarkably accurate and robust estimates of the source wavelet in the presence of strong heterogeneity in both the dielectric permittivity and electrical conductivity as well as significant ambient noise in the recorded data. Furthermore, our tests also indicate that the approach is insensitive to the phase characteristics of the starting wavelet, which is not the case when directly incorporating the wavelet estimation into the inverse problem.Another critical issue with crosshole georadar waveform inversion schemes which clearly needs to be investigated is the consequence of the common assumption of frequency- independent electromagnetic constitutive parameters. This is crucial since in reality, these parameters are known to be frequency-dependent and complex and thus recorded georadar data may show significant dispersive behaviour. In particular, in the presence of water, there is a wide body of evidence showing that the dielectric permittivity can be significantly frequency dependent over the GPR frequency range, due to a variety of relaxation processes. The second part of my thesis is therefore dedicated to the evaluation of the reconstruction limits of a non-dispersive crosshole georadar waveform inversion scheme in the presence of varying degrees of dielectric dispersion. I show that the inversion algorithm, combined with the iterative deconvolution-based source wavelet estimation procedure that is partially able to account for the frequency-dependent effects through an "effective" wavelet, performs remarkably well in weakly to moderately dispersive environments and has the ability to provide adequate tomographic reconstructions.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Reverse transcription quantitative real-time polymerase chain reaction reference genes in the spared nerve injury model of neuropathic pain: validation and literature search.

BACKGROUND: The reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a widely used, highly sensitive laboratory technique to rapidly and easily detect, identify and quantify gene expression. Reliable RT-qPCR data necessitates accurate normalization with validated control genes (reference genes) whose expression is constant in all studied conditions. This stability has to be demonstrated.We performed a literature search for studies using quantitative or semi-quantitative PCR in the rat spared nerve injury (SNI) model of neuropathic pain to verify whether any reference genes had previously been validated. We then analyzed the stability over time of 7 commonly used reference genes in the nervous system - specifically in the spinal cord dorsal horn and the dorsal root ganglion (DRG). These were: Actin beta (Actb), Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ribosomal proteins 18S (18S), L13a (RPL13a) and L29 (RPL29), hypoxanthine phosphoribosyltransferase 1 (HPRT1) and hydroxymethylbilane synthase (HMBS). We compared the candidate genes and established a stability ranking using the geNorm algorithm. Finally, we assessed the number of reference genes necessary for accurate normalization in this neuropathic pain model. RESULTS: We found GAPDH, HMBS, Actb, HPRT1 and 18S cited as reference genes in literature on studies using the SNI model. Only HPRT1 and 18S had been once previously demonstrated as stable in RT-qPCR arrays. All the genes tested in this study, using the geNorm algorithm, presented gene stability values (M-value) acceptable enough for them to qualify as potential reference genes in both DRG and spinal cord. Using the coefficient of variation, 18S failed the 50% cut-off with a value of 61% in the DRG. The two most stable genes in the dorsal horn were RPL29 and RPL13a; in the DRG they were HPRT1 and Actb. Using a 0.15 cut-off for pairwise variations we found that any pair of stable reference gene was sufficient for the normalization process. CONCLUSIONS: In the rat SNI model, we validated and ranked Actb, RPL29, RPL13a, HMBS, GAPDH, HPRT1 and 18S as good reference genes in the spinal cord. In the DRG, 18S did not fulfill stability criteria. The combination of any two stable reference genes was sufficient to provide an accurate normalization.

Long-term data from the swiss pulmonary hypertension registry.

BACKGROUND: Registries are important for real-life epidemiology on different pulmonary hypertension (PH) groups. OBJECTIVE: To provide long-term data of the Swiss PH registry of 1998-2012. METHODS: PH patients have been classified into 5 groups and registered upon written informed consent at 5 university and 8 associated hospitals since 1998. New York Heart Association (NYHA) class, 6-min walk distance, hemodynamics and therapy were registered at baseline. Patients were regularly followed, and therapy and events (death, transplantation, endarterectomy or loss to follow-up) registered. The data were stratified according to the time of diagnosis into prevalent before 2000 and incident during 2000-2004, 2005-2008 and 2009-2012. RESULTS: From 996 (53% female) PH patients, 549 had pulmonary arterial hypertension (PAH), 36 PH due to left heart disease, 127 due to lung disease, 249 to chronic thromboembolic PH (CTEPH) and 35 to miscellaneous PH. Age and BMI significantly increased over time, whereas hemodynamic severity decreased. Overall, event-free survival was 84, 72, 64 and 58% for the years 1-4 and similar for time periods since 2000, but better during the more recent periods for PAH and CTEPH. Of all PAH cases, 89% had target medical therapy and 43% combination therapy. Of CTEPH patients, 14 and 2% underwent pulmonary endarterectomy or transplantation, respectively; 87% were treated with PAH target therapy. CONCLUSION: Since 2000, the incident Swiss PH patients registered were older, hemodynamically better and mostly treated with PAH target therapies. Survival has been better for PAH and CTEPH diagnosed since 2008 compared with earlier diagnosis or other classifications.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Spatiotemporal analysis of forensic case data: a visualisation approach

Whether for investigative or intelligence aims, crime analysts often face up the necessity to analyse the spatiotemporal distribution of crimes or traces left by suspects. This article presents a visualisation methodology supporting recurrent practical analytical tasks such as the detection of crime series or the analysis of traces left by digital devices like mobile phone or GPS devices. The proposed approach has led to the development of a dedicated tool that has proven its effectiveness in real inquiries and intelligence practices. It supports a more fluent visual analysis of the collected data and may provide critical clues to support police operations as exemplified by the presented case studies.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Comparison of Strategies to Detect Epistasis from eQTL Data.

Genome-wide association studies have been instrumental in identifying genetic variants associated with complex traits such as human disease or gene expression phenotypes. It has been proposed that extending existing analysis methods by considering interactions between pairs of loci may uncover additional genetic effects. However, the large number of possible two-marker tests presents significant computational and statistical challenges. Although several strategies to detect epistasis effects have been proposed and tested for specific phenotypes, so far there has been no systematic attempt to compare their performance using real data. We made use of thousands of gene expression traits from linkage and eQTL studies, to compare the performance of different strategies. We found that using information from marginal associations between markers and phenotypes to detect epistatic effects yielded a lower false discovery rate (FDR) than a strategy solely using biological annotation in yeast, whereas results from human data were inconclusive. For future studies whose aim is to discover epistatic effects, we recommend incorporating information about marginal associations between SNPs and phenotypes instead of relying solely on biological annotation. Improved methods to discover epistatic effects will result in a more complete understanding of complex genetic effects.

Viscoelasticity in Achilles Tendonopathy: Quantitative Assessment by Using Real-time Shear-Wave Elastography.

Purpose To investigate the differences in viscoelastic properties between normal and pathologic Achilles tendons ( AT Achilles tendon s) by using real-time shear-wave elastography ( SWE shear-wave elastography ). Materials and Methods The institutional review board approved this study, and written informed consent was obtained from 25 symptomatic patients and 80 volunteers. One hundred eighty ultrasonographic (US) and SWE shear-wave elastography studies of AT Achilles tendon s without tendonopathy and 30 studies of the middle portion of the AT Achilles tendon in patients with tendonopathy were assessed prospectively. Each study included data sets acquired at B-mode US (tendon morphology and cross-sectional area) and SWE shear-wave elastography (axial and sagittal mean velocity and relative anisotropic coefficient) for two passively mobilized ankle positions. The presence of AT Achilles tendon tears at B-mode US and signal-void areas at SWE shear-wave elastography were noted. Results Significantly lower mean velocity was shown in tendons with tendonopathy than in normal tendons in the relaxed position at axial SWE shear-wave elastography (P < .001) and in the stretched position at sagittal (P < .001) and axial (P = .0026) SWE shear-wave elastography . Tendon softening was a sign of tendonopathy in relaxed AT Achilles tendon s when the mean velocity was less than or equal to 4.06 m · sec(-1) at axial SWE shear-wave elastography (sensitivity, 54.2%; 95% confidence interval [ CI confidence interval ]: 32.8, 74.4; specificity, 91.5%; 95% CI confidence interval : 86.3, 95.1) and less than or equal to 5.70 m · sec(-1) at sagittal SWE shear-wave elastography (sensitivity, 41.7%; 95% CI confidence interval : 22.1, 63.3; specificity, 81.8%; 95% CI confidence interval : 75.3, 87.2) and in stretched AT Achilles tendon s, when the mean velocity was less than or equal to 4.86 m · sec(-1) at axial SWE shear-wave elastography (sensitivity, 66.7%; 95% CI confidence interval : 44.7, 84.3; specificity, 75.6%; 95% CI confidence interval : 68.5, 81.7) and less than or equal to 14.58 m · sec(-1) at sagittal SWE shear-wave elastography (sensitivity, 58.3%; 95% CI confidence interval : 36.7, 77.9; specificity, 83.5%; 95% CI confidence interval : 77.2, 88.7). Anisotropic results were not significantly different between normal and pathologic AT Achilles tendon s. Six of six (100%) partial-thickness tears appeared as signal-void areas at SWE shear-wave elastography . Conclusion Whether the AT Achilles tendon was relaxed or stretched, SWE shear-wave elastography helped to confirm and quantify pathologic tendon softening in patients with tendonopathy in the midportion of the AT Achilles tendon and did not reveal modifications of viscoelastic anisotropy in the tendon. Tendon softening assessed by using SWE shear-wave elastography appeared to be highly specific, but sensitivity was relatively low. © RSNA, 2014.

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.