The SwissLipids knowledgebase for lipid biology.

MOTIVATION: Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it. RESULTS: To facilitate the description and exploration of lipidomic data and its integration with prior biological knowledge, we have developed a knowledge resource for lipids and their biology-SwissLipids. SwissLipids provides curated knowledge of lipid structures and metabolism which is used to generate an in silico library of feasible lipid structures. These are arranged in a hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. SwissLipids provides a reference namespace for lipidomic data publication, data exploration and hypothesis generation. The current version of SwissLipids includes over 244 000 known and theoretically possible lipid structures, over 800 proteins, and curated links to published knowledge from over 620 peer-reviewed publications. We are continually updating the SwissLipids hierarchy with new lipid categories and new expert curated knowledge. AVAILABILITY: SwissLipids is freely available at http://www.swisslipids.org/. CONTACT: alan.bridge@isb-sib.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploration of the contribution of isotope ratio mass spectrometry to the investigation of explosives: A study of black powders and ammonium nitrate fertilisers

The increasing number of bomb attacks involving improvised explosive devices, as well as the nature of the explosives, give rise to concern among safety and law enforcement agencies. The substances used in explosive charges are often everyday products diverted from their primary licit applications. Thus, reducing or limiting their accessibility for prevention purposes is difficult. Ammonium nitrate, employed in agriculture as a fertiliser, is used worldwide in small and large homemade bombs. Black powder, dedicated to hunting and shooting sports, is used illegally as a filling in pipe bombs causing extensive damage. If the main developments of instrumental techniques in explosive analysis have been constantly pushing the limits of detection, their actual contribution to the investigation of explosives in terms of source discrimination is limited. Forensic science has seen the emergence of a new technology, isotope ratio mass spectrometry (IRMS), that shows promising results. Its very first application in forensic science dates back to 1979. Liu et al. analysed cannabis plants coming from different countries [Liu et al. 1979]. This preliminary study highlighted its potential to discriminate specimens coming from different sources. Thirty years later, the keen interest in this new technology has given rise to a flourishing number of publications in forensic science. The countless applications of IRMS to a wide range of materials and substances attest to its success and suggest that the technique is ready to be used in forensic science. However, many studies are characterised by a lack of methodology and fundamental data. They have been undertaken in a top-down approach, applying this technique in an exploratory manner on a restricted sampling. This manner of procedure often does not allow the researcher to answer a number of questions, such as: do the specimens come from the same source, what do we mean by source or what is the inherent variability of a substance? The production of positive results has prevailed at the expense of forensic fundamentals. This research focused on the evaluation of the contribution of the information provided by isotopic analysis to the investigation of explosives. More specifically, this evaluation was based on a sampling of black powders and ammonium nitrate fertilisers coming from known sources. Not only has the methodology developed in this work enabled us to highlight crucial elements inherent to the methods themselves, but also to evaluate both the longitudinal and transversal variabilities of the information. First, the study of the variability of the profile over time was undertaken. Secondly, the variability of black powders and ammonium nitrate fertilisers within the same source and between different sources was evaluated. The contribution of this information to the investigation of explosives was then evaluated and discussed. --------------------------------------------------------------------------------------------------- Le nombre croissant d'attentats à la bombe impliquant des engins explosifs artisanaux, ainsi que la nature des charges explosives, constituent une préoccupation majeure pour les autorités d'application de la loi et les organismes de sécurité. Les substances utilisées dans les charges explosives sont souvent des produits du quotidien, détournés de leurs applications licites. Par conséquent, réduire ou limiter l'accessibilité de ces produits dans un but de prévention est difficile. Le nitrate d'ammonium, employé dans l'agriculture comme engrais, est utilisé dans des petits et grands engins explosifs artisanaux. La poudre noire, initialement dédiée à la chasse et au tir sportif, est fréquemment utilisée comme charge explosive dans les pipe bombs, qui causent des dommages importants. Si les développements des techniques d'analyse des explosifs n'ont cessé de repousser les limites de détection, leur contribution réelle à l'investigation des explosifs est limitée en termes de discrimination de sources. Une nouvelle technologie qui donne des résultats prometteurs a fait son apparition en science forensique: la spectrométrie de masse à rapport isotopique (IRMS). Sa première application en science forensique remonte à 1979. Liu et al. ont analysé des plants de cannabis provenant de différents pays [Liu et al. 1979]. Cette étude préliminaire, basée sur quelques analyses, a mis en évidence le potentiel de l'IRMS à discriminer des spécimens provenant de sources différentes. Trente ans plus tard, l'intérêt marqué pour cette nouvelle technologie en science forensique se traduit par un nombre florissant de publications. Les innombrables applications de l'IRMS à une large gamme de matériaux et de substances attestent de son succès et suggèrent que la technique est prête à être utilisée en science forensique. Cependant, de nombreuses études sont caractérisées par un manque de méthodologie et de données fondamentales. Elles ont été menées sans définir les hypothèses de recherche et en appliquant cette technique de façon exploratoire sur un échantillonnage restreint. Cette manière de procéder ne permet souvent pas au chercheur de répondre à un certain nombre de questions, tels que: est-ce que deux spécimens proviennent de la même source, qu'entend-on par source ou encore quelle est l'intravariabilité d'une substance? La production de résultats positifs a prévalu au détriment des fondamentaux de science forensique. Cette recherche s'est attachée à évaluer la contribution réelle de l'information isotopique dans les investigations en matière d'explosifs. Plus particulièrement, cette évaluation s'est basée sur un échantillonnage constitué de poudres noires et d'engrais à base de nitrate d'ammonium provenant de sources connues. La méthodologie développée dans ce travail a permis non seulement de mettre en évidence des éléments cruciaux relatifs à la méthode d'analyse elle-même, mais également d'évaluer la variabilité de l'information isotopique d'un point de vue longitudinal et transversal. Dans un premier temps, l'évolution du profil en fonction du temps a été étudiée. Dans un second temps, la variabilité du profil des poudres noires et des engrais à base de nitrate d'ammonium au sein d'une même source et entre différentes sources a été évaluée. La contribution de cette information dans le cadre des investigations d'explosifs a ensuite été discutée et évaluée.

Exploration of alpine orographic precipitation patterns with radar image processing and clustering techniques

A methodology of exploratory data analysis investigating the phenomenon of orographic precipitation enhancement is proposed. The precipitation observations obtained from three Swiss Doppler weather radars are analysed for the major precipitation event of August 2005 in the Alps. Image processing techniques are used to detect significant precipitation cells/pixels from radar images while filtering out spurious effects due to ground clutter. The contribution of topography to precipitation patterns is described by an extensive set of topographical descriptors computed from the digital elevation model at multiple spatial scales. Additionally, the motion vector field is derived from subsequent radar images and integrated into a set of topographic features to highlight the slopes exposed to main flows. Following the exploratory data analysis with a recent algorithm of spectral clustering, it is shown that orographic precipitation cells are generated under specific flow and topographic conditions. Repeatability of precipitation patterns in particular spatial locations is found to be linked to specific local terrain shapes, e.g. at the top of hills and on the upwind side of the mountains. This methodology and our empirical findings for the Alpine region provide a basis for building computational data-driven models of orographic enhancement and triggering of precipitation. Copyright (C) 2011 Royal Meteorological Society .

Monitoring of illicit pill distribution networks using an image collection exploration framework

This paper proposes a novel approach for the analysis of illicit tablets based on their visual characteristics. In particular, the paper concentrates on the problem of ecstasy pill seizure profiling and monitoring. The presented method extracts the visual information from pill images and builds a representation of it, i.e. it builds a pill profile based on the pill visual appearance. Different visual features are used to build different image similarity measures, which are the basis for a pill monitoring strategy based on both discriminative and clustering models. The discriminative model permits to infer whether two pills come from the same seizure, while the clustering models groups of pills that share similar visual characteristics. The resulting clustering structure allows to perform a visual identification of the relationships between different seizures. The proposed approach was evaluated using a data set of 621 Ecstasy pill pictures. The results demonstrate that this is a feasible and cost effective method for performing pill profiling and monitoring.

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.

Joint inversion of crosshole GPR and seismic traveltime data

Joint inversion of crosshole ground-penetrating radar and seismic data can improve model resolution and fidelity of the resultant individual models. Model coupling obtained by minimizing or penalizing some measure of structural dissimilarity between models appears to be the most versatile approach because only weak assumptions about petrophysical relationships are required. Nevertheless, experimental results and petrophysical arguments suggest that when porosity variations are weak in saturated unconsolidated environments, then radar wave speed is approximately linearly related to seismic wave speed. Under such circumstances, model coupling also can be achieved by incorporating cross-covariances in the model regularization. In two case studies, structural similarity is imposed by penalizing models for which the model cross-gradients are nonzero. A first case study demonstrates improvements in model resolution by comparing the resulting models with borehole information, whereas a second case study uses point-spread functions. Although radar seismic wavespeed crossplots are very similar for the two case studies, the models plot in different portions of the graph, suggesting variances in porosity. Both examples display a close, quasilinear relationship between radar seismic wave speed in unconsolidated environments that is described rather well by the corresponding lower Hashin-Shtrikman (HS) bounds. Combining crossplots of the joint inversion models with HS bounds can constrain porosity and pore structure better than individual inversion results can.

A non-linear Bayesian approach for upscaling local-scale hydraulic conductivity measurements based on local- and regional-scale geophysical data

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed an upscaling procedure based on a Bayesian sequential simulation approach. This method is then applied to the stochastic integration of low-resolution, regional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this upscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

UniPathway: a resource for the exploration and annotation of metabolic pathways.

UniPathway (http://www.unipathway.org) is a fully manually curated resource for the representation and annotation of metabolic pathways. UniPathway provides explicit representations of enzyme-catalyzed and spontaneous chemical reactions, as well as a hierarchical representation of metabolic pathways. This hierarchy uses linear subpathways as the basic building block for the assembly of larger and more complex pathways, including species-specific pathway variants. All of the pathway data in UniPathway has been extensively cross-linked to existing pathway resources such as KEGG and MetaCyc, as well as sequence resources such as the UniProt KnowledgeBase (UniProtKB), for which UniPathway provides a controlled vocabulary for pathway annotation. We introduce here the basic concepts underlying the UniPathway resource, with the aim of allowing users to fully exploit the information provided by UniPathway.