Epidemiology of multiple congenital anomalies in Europe: a EUROCAT population-based registry study.

BACKGROUND: This study describes the prevalence, associated anomalies, and demographic characteristics of cases of multiple congenital anomalies (MCA) in 19 population-based European registries (EUROCAT) covering 959,446 births in 2004 and 2010. METHODS: EUROCAT implemented a computer algorithm for classification of congenital anomaly cases followed by manual review of potential MCA cases by geneticists. MCA cases are defined as cases with two or more major anomalies of different organ systems, excluding sequences, chromosomal and monogenic syndromes. RESULTS: The combination of an epidemiological and clinical approach for classification of cases has improved the quality and accuracy of the MCA data. Total prevalence of MCA cases was 15.8 per 10,000 births. Fetal deaths and termination of pregnancy were significantly more frequent in MCA cases compared with isolated cases (p < 0.001) and MCA cases were more frequently prenatally diagnosed (p < 0.001). Live born infants with MCA were more often born preterm (p < 0.01) and with birth weight < 2500 grams (p < 0.01). Respiratory and ear, face, and neck anomalies were the most likely to occur with other anomalies (34% and 32%) and congenital heart defects and limb anomalies were the least likely to occur with other anomalies (13%) (p < 0.01). However, due to their high prevalence, congenital heart defects were present in half of all MCA cases. Among males with MCA, the frequency of genital anomalies was significantly greater than the frequency of genital anomalies among females with MCA (p < 0.001). CONCLUSION: Although rare, MCA cases are an important public health issue, because of their severity. The EUROCAT database of MCA cases will allow future investigation on the epidemiology of these conditions and related clinical and diagnostic problems.

Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps.

Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures.

Land plants and DNA barcodes: short-term and long-term goals.

Land plants have had the reputation of being problematic for DNA barcoding for two general reasons: (i) the standard DNA regions used in algae, animals and fungi have exceedingly low levels of variability and (ii) the typically used land plant plastid phylogenetic markers (e.g. rbcL, trnL-F, etc.) appear to have too little variation. However, no one has assessed how well current phylogenetic resources might work in the context of identification (versus phylogeny reconstruction). In this paper, we make such an assessment, particularly with two of the markers commonly sequenced in land plant phylogenetic studies, plastid rbcL and internal transcribed spacers of the large subunits of nuclear ribosomal DNA (ITS), and find that both of these DNA regions perform well even though the data currently available in GenBank/EBI were not produced to be used as barcodes and BLAST searches are not an ideal tool for this purpose. These results bode well for the use of even more variable regions of plastid DNA (such as, for example, psbA-trnH) as barcodes, once they have been widely sequenced. In the short term, efforts to bring land plant barcoding up to the standards being used now in other organisms should make swift progress. There are two categories of DNA barcode users, scientists in fields other than taxonomy and taxonomists. For the former, the use of mitochondrial and plastid DNA, the two most easily assessed genomes, is at least in the short term a useful tool that permits them to get on with their studies, which depend on knowing roughly which species or species groups they are dealing with, but these same DNA regions have important drawbacks for use in taxonomic studies (i.e. studies designed to elucidate species limits). For these purposes, DNA markers from uniparentally (usually maternally) inherited genomes can only provide half of the story required to improve taxonomic standards being used in DNA barcoding. In the long term, we will need to develop more sophisticated barcoding tools, which would be multiple, low-copy nuclear markers with sufficient genetic variability and PCR-reliability; these would permit the detection of hybrids and permit researchers to identify the 'genetic gaps' that are useful in assessing species limits.

Enhancing the vertical resolution of surface georadar data

There are far-reaching conceptual similarities between bi-static surface georadar and post-stack, "zero-offset" seismic reflection data, which is expressed in largely identical processing flows. One important difference is, however, that standard deconvolution algorithms routinely used to enhance the vertical resolution of seismic data are notoriously problematic or even detrimental to the overall signal quality when applied to surface georadar data. We have explored various options for alleviating this problem and have tested them on a geologically well-constrained surface georadar dataset. Standard stochastic and direct deterministic deconvolution approaches proved to be largely unsatisfactory. While least-squares-type deterministic deconvolution showed some promise, the inherent uncertainties involved in estimating the source wavelet introduced some artificial "ringiness". In contrast, we found spectral balancing approaches to be effective, practical and robust means for enhancing the vertical resolution of surface georadar data, particularly, but not exclusively, in the uppermost part of the georadar section, which is notoriously plagued by the interference of the direct air- and groundwaves. For the data considered in this study, it can be argued that band-limited spectral blueing may provide somewhat better results than standard band-limited spectral whitening, particularly in the uppermost part of the section affected by the interference of the air- and groundwaves. Interestingly, this finding is consistent with the fact that the amplitude spectrum resulting from least-squares-type deterministic deconvolution is characterized by a systematic enhancement of higher frequencies at the expense of lower frequencies and hence is blue rather than white. It is also consistent with increasing evidence that spectral "blueness" is a seemingly universal, albeit enigmatic, property of the distribution of reflection coefficients in the Earth. Our results therefore indicate that spectral balancing techniques in general and spectral blueing in particular represent simple, yet effective means of enhancing the vertical resolution of surface georadar data and, in many cases, could turn out to be a preferable alternative to standard deconvolution approaches.

Fracture imaging within a granitic rock aquifer using multiple-offset single-hole and cross-hole GPR reflection data

The sparsely spaced highly permeable fractures of the granitic rock aquifer at Stang-er-Brune (Brittany, France) form a well-connected fracture network of high permeability but unknown geometry. Previous work based on optical and acoustic logging together with single-hole and cross-hole flowmeter data acquired in 3 neighbouring boreholes (70-100 m deep) has identified the most important permeable fractures crossing the boreholes and their hydraulic connections. To constrain possible flow paths by estimating the geometries of known and previously unknown fractures, we have acquired, processed and interpreted multifold, single- and cross-hole GPR data using 100 and 250 MHz antennas. The GPR data processing scheme consisting of timezero corrections, scaling, bandpass filtering and F-X deconvolution, eigenvector filtering, muting, pre-stack Kirchhoff depth migration and stacking was used to differentiate fluid-filled fracture reflections from source generated noise. The final stacked and pre-stack depth-migrated GPR sections provide high-resolution images of individual fractures (dipping 30-90°) in the surroundings (2-20 m for the 100 MHz antennas; 2-12 m for the 250 MHz antennas) of each borehole in a 2D plane projection that are of superior quality to those obtained from single-offset sections. Most fractures previously identified from hydraulic testing can be correlated to reflections in the single-hole data. Several previously unknown major near vertical fractures have also been identified away from the boreholes.

Probabilistic base calling of Solexa sequencing data.

BACKGROUND: Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology. RESULTS: We propose a novel base calling algorithm using model-based clustering and probability theory to identify ambiguous bases and code them with IUPAC symbols. We also select optimal sub-tags using a score based on information content to remove uncertain bases towards the ends of the reads. CONCLUSION: We show that the method improves genome coverage and number of usable tags as compared with Solexa's data processing pipeline by an average of 15%. An R package is provided which allows fast and accurate base calling of Solexa's fluorescence intensity files and the production of informative diagnostic plots.

A neuronal gamma oscillatory signature during morphological unification in the left occipitotemporal junction.

Morphology is the aspect of language concerned with the internal structure of words. In the past decades, a large body of masked priming (behavioral and neuroimaging) data has suggested that the visual word recognition system automatically decomposes any morphologically complex word into a stem and its constituent morphemes. Yet the reliance of morphology on other reading processes (e.g., orthography and semantics), as well as its underlying neuronal mechanisms are yet to be determined. In the current magnetoencephalography study, we addressed morphology from the perspective of the unification framework, that is, by applying the Hold/Release paradigm, morphological unification was simulated via the assembly of internal morphemic units into a whole word. Trials representing real words were divided into words with a transparent (true) or a nontransparent (pseudo) morphological relationship. Morphological unification of truly suffixed words was faster and more accurate and additionally enhanced induced oscillations in the narrow gamma band (60-85 Hz, 260-440 ms) in the left posterior occipitotemporal junction. This neural signature could not be explained by a mere automatic lexical processing (i.e., stem perception), but more likely it related to a semantic access step during the morphological unification process. By demonstrating the validity of unification at the morphological level, this study contributes to the vast empirical evidence on unification across other language processes. Furthermore, we point out that morphological unification relies on the retrieval of lexical semantic associations via induced gamma band oscillations in a cerebral hub region for visual word form processing.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

La gravimétrie expéditive appliquée à la recherche d'aquifères en zone aride. Cas de la nappe alluviale du Teloua (Agadez, Niger)

This paper illustrates the practicality and efficiency of gravimetry for aquifer prospecting in arid zones. Known for the long and tedious data-processing it requires, this method becomes expeditious when simplified as presented here. Its use is then fully justified in a survey of this kind. During the study of the Teloua alluvial aquifer (Agadez, Niger), several ancient channels were clearly and rapidly located. Comparison of the results obtained here with those from previous studies demonstrates anew that for comprehensive prospecting, several complementary geophysical methods should always be employed.

Sensitivity of genome-wide-association signals to phenotyping strategy: the PROP-TAS2R38 taste association as a benchmark.

Natural genetic variation can have a pronounced influence on human taste perception, which in turn may influence food preference and dietary choice. Genome-wide association studies represent a powerful tool to understand this influence. To help optimize the design of future genome-wide-association studies on human taste perception we have used the well-known TAS2R38-PROP association as a tool to determine the relative power and efficiency of different phenotyping and data-analysis strategies. The results show that the choice of both data collection and data processing schemes can have a very substantial impact on the power to detect genotypic variation that affects chemosensory perception. Based on these results we provide practical guidelines for the design of future GWAS studies on chemosensory phenotypes. Moreover, in addition to the TAS2R38 gene past studies have implicated a number of other genetic loci to affect taste sensitivity to PROP and the related bitter compound PTC. None of these other locations showed genome-wide significant associations in our study. To facilitate further, target-gene driven, studies on PROP taste perception we provide the genome-wide list of p-values for all SNPs genotyped in the current study.

Optimizing a remotely sensed proxy for plankton biomass in Lake Kivu

Many regions of the world, including inland lakes, present with suboptimal conditions for the remotely sensed retrieval of optical signals, thus challenging the limits of available satellite data-processing tools, such as atmospheric correction models (ACM) and water constituent-retrieval (WCR) algorithms. Working in such regions, however, can improve our understanding of remote-sensing tools and their applicabil- ity in new contexts, in addition to potentially offering useful information about aquatic ecology. Here, we assess and compare 32 combinations of two ACMs, two WCRs, and three binary categories of data quality standards to optimize a remotely sensed proxy of plankton biomass in Lake Kivu. Each parameter set is compared against the available ground-truth match-ups using Spearman's right-tailed ρ. Focusing on the best sets from each ACM-WCR combination, their performances are discussed with regard to data distribution, sample size, spatial completeness, and seasonality. The results of this study may be of interest both for ecological studies on Lake Kivu and for epidemio- logical studies of disease, such as cholera, the dynamics of which has been associated with plankton biomass in other regions of the world.

Experimental and computational approaches to quantitative proteomics: status quo and outlook.

Proteomics has come a long way from the initial qualitative analysis of proteins present in a given sample at a given time ("cataloguing") to large-scale characterization of proteomes, their interactions and dynamic behavior. Originally enabled by breakthroughs in protein separation and visualization (by two-dimensional gels) and protein identification (by mass spectrometry), the discipline now encompasses a large body of protein and peptide separation, labeling, detection and sequencing tools supported by computational data processing. The decisive mass spectrometric developments and most recent instrumentation news are briefly mentioned accompanied by a short review of gel and chromatographic techniques for protein/peptide separation, depletion and enrichment. Special emphasis is placed on quantification techniques: gel-based, and label-free techniques are briefly discussed whereas stable-isotope coding and internal peptide standards are extensively reviewed. Another special chapter is dedicated to software and computing tools for proteomic data processing and validation. A short assessment of the status quo and recommendations for future developments round up this journey through quantitative proteomics.

Method to assess and optimise dependability of complex macro-systems: application to a railway signalling system

Achieving a high degree of dependability in complex macro-systems is challenging. Because of the large number of components and numerous independent teams involved, an overview of the global system performance is usually lacking to support both design and operation adequately. A functional failure mode, effects and criticality analysis (FMECA) approach is proposed to address the dependability optimisation of large and complex systems. The basic inductive model FMECA has been enriched to include considerations such as operational procedures, alarm systems. environmental and human factors, as well as operation in degraded mode. Its implementation on a commercial software tool allows an active linking between the functional layers of the system and facilitates data processing and retrieval, which enables to contribute actively to the system optimisation. The proposed methodology has been applied to optimise dependability in a railway signalling system. Signalling systems are typical example of large complex systems made of multiple hierarchical layers. The proposed approach appears appropriate to assess the global risk- and availability-level of the system as well as to identify its vulnerabilities. This enriched-FMECA approach enables to overcome some of the limitations and pitfalls previously reported with classical FMECA approaches.

Adaptive tracking of EEG oscillations.

Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.