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.

Melt/peridotite interaction in the Southern Lanzo peridotite: Field, textural and geochemical evidence

This paper presents field, petrographic-structural and geochemical data on spinet and plagioclase peridotites from the southern domain of the Lanzo ophiolitic peridotite massif (Western Alps). Spinet lherzolites, harzburgites and dunites crop out at Mt. Arpone and Mt. Musine. Field evidence indicates that pristine porphyroclastic spinet lherzolites are transformed to coarse granular spinet harzburgites, which are in turn overprinted by plagioclase peridotites, while strongly depleted spinet harzburgite and dunite bands and bodies replace the plagioclase peridotites. On the northern flank of Mt. Arpone, deformed, porphyroclastic (lithospheric) lherzolites, with diffuse pyroxenite banding, represent the oldest spinel-facies rocks. They show microstructures of a composite subsolidus evolution, suggesting provenance from deeper (asthenospheric) mantle levels and accretion to the lithosphere. These protoliths are locally transformed to coarse granular (reactive) spinet harzburgites and dunites, which show textures reminiscent of melt/rock reaction and geochemical characteristics suggesting that they are products of peridotite interaction with reactively percolating melts. Geochemical data and modelling suggest that <1-5% fractional melting of spinel-facies DMM produced the injected melts. Plagioclase peridotites are hybrid rocks resulting from pre-existing spinet peridotites and variable enrichment of plagioclase and micro-gabbroic material by percolating melts. The impregnating melts attained silica-saturation, as testified by widespread orthopyroxene replacement of olivine, during open system migration in the lithosphere. At Mt. Musine, coarse granular spinet harzburgite and dunite bodies replace the plagioclase peridotites. Most of these replacive, refractory peridotites have interstitial magmatic clinopyroxene with trace element compositions in equilibrium with MORB, while some Cpx have REE-depleted patterns suggesting transient geochemical features of the migrating MORB-type melts, acquired by interaction with the ambient plagioclase peridotite. These replacive spinet harzburgite and dunite bodies are interpreted as channels exploited for focused and reactive migration of silica-undersaturated melts with aggregate MORB compositions. Such melts were unrelated to the silica-saturated melts that refertilized the pre-existing plagioclase peridotites. Finally, MORB melt migration occurred along open fractures, now recorded as gabbroic dikes. Our data document the complexity of rock-types and mantle processes in the South Lanzo peridotite massif and describe a composite tectonic and magmatic scenario that is not consistent with the ``asthenospheric scenario'' proposed by previous authors. We envisage a ``transitional scenario'' in which extending subcontinental lithospheric mantle was strongly modified (both depleted and refertilized) by early melts with MORB-affinity formed by decompression partial melting of the upwelling asthenosphere, during pre-oceanic rifting and lithospheric thinning in the Ligurian Tethys realm. (C) 2006 Elsevier B.V. All rights reserved.

Toxicokinetic modeling of folpet fungicide and its ring-biomarkers of exposure in humans.

A human in vivo toxicokinetic model was built to allow a better understanding of the toxicokinetics of folpet fungicide and its key ring biomarkers of exposure: phthalimide (PI), phthalamic acid (PAA) and phthalic acid (PA). Both PI and the sum of ring metabolites, expressed as PA equivalents (PAeq), may be used as biomarkers of exposure. The conceptual representation of the model was based on the analysis of the time course of these biomarkers in volunteers orally and dermally exposed to folpet. In the model, compartments were also used to represent the body burden of folpet and experimentally relevant PI, PAA and PA ring metabolites in blood and in key tissues as well as in excreta, hence urinary and feces. The time evolution of these biomarkers in each compartment of the model was then mathematically described by a system of coupled differential equations. The mathematical parameters of the model were then determined from best fits to the time courses of PI and PAeq in blood and urine of five volunteers administered orally 1 mg kg(-1) and dermally 10 mg kg(-1) of folpet. In the case of oral administration, the mean elimination half-life of PI from blood (through feces, urine or metabolism) was found to be 39.9 h as compared with 28.0 h for PAeq. In the case of a dermal application, mean elimination half-life of PI and PAeq was estimated to be 34.3 and 29.3 h, respectively. The average final fractions of administered dose recovered in urine as PI over the 0-96 h period were 0.030 and 0.002%, for oral and dermal exposure, respectively. Corresponding values for PAeq were 24.5 and 1.83%, respectively. Finally, the average clearance rate of PI from blood calculated from the oral and dermal data was 0.09 ± 0.03 and 0.13 ± 0.05 ml h(-1) while the volume of distribution was 4.30 ± 1.12 and 6.05 ± 2.22 l, respectively. It was not possible to obtain the corresponding values from PAeq data owing to the lack of blood time course data.

What matters for predicting spatial distributions of trees: techniques, data, or species' characteristics?

Data characteristics and species traits are expected to influence the accuracy with which species' distributions can be modeled and predicted. We compare 10 modeling techniques in terms of predictive power and sensitivity to location error, change in map resolution, and sample size, and assess whether some species traits can explain variation in model performance. We focused on 30 native tree species in Switzerland and used presence-only data to model current distribution, which we evaluated against independent presence-absence data. While there are important differences between the predictive performance of modeling methods, the variance in model performance is greater among species than among techniques. Within the range of data perturbations in this study, some extrinsic parameters of data affect model performance more than others: location error and sample size reduced performance of many techniques, whereas grain had little effect on most techniques. No technique can rescue species that are difficult to predict. The predictive power of species-distribution models can partly be predicted from a series of species characteristics and traits based on growth rate, elevational distribution range, and maximum elevation. Slow-growing species or species with narrow and specialized niches tend to be better modeled. The Swiss presence-only tree data produce models that are reliable enough to be useful in planning and management applications.

Public health impact of statin prescribing strategies based on JUPITER Modeling study from a population-based study.

OBJECTIVE: To evaluate the public health impact of statin prescribing strategies based on the Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin Study (JUPITER). METHODS: We studied 2268 adults aged 35-75 without cardiovascular disease in a population-based study in Switzerland in 2003-2006. We assessed the eligibility for statins according to the Adult Treatment Panel III (ATPIII) guidelines, and by adding "strict" (hs-CRP≥2.0mg/L and LDL-cholesterol <3.4mmol/L), and "extended" (hs-CRP≥2.0mg/L alone) JUPITER-like criteria. We estimated the proportion of CHD deaths potentially prevented over 10years in the Swiss population. RESULTS: Fifteen % were already taking statins, 42% were eligible by ATPIII guidelines, 53% by adding "strict", and 62% by adding "extended" criteria, with a total of 19% newly eligible. The number needed to treat with statins to avoid one CHD death over 10years was 38 for ATPIII, 84 for "strict" and 92 for "extended" JUPITER-like criteria. ATPIII would prevent 17% of CHD deaths, compared with 20% for ATPIII+"strict" and 23% for ATPIII + "extended" criteria (+6%). CONCLUSION: Implementing JUPITER-like strategies would make statin prescribing for primary prevention more common and less efficient than it is with current guidelines.

The Caribbean-North America-Cocos Triple Junction and the dynamics of the Polochic-Motagua fault systems: Pull-up and zipper models

The Polochic-Motagua fault systems (PMFS) are part of the sinistral transform boundary between the North American and Caribbean plates. To the west, these systems interact with the subduction zone of the Cocos plate, forming a subduction-subduction-transform triple junction. The North American plate moves westward relative to the Caribbean plate. This movement does not affect the geometry of the subducted Cocos plate, which implies that deformation is accommodated entirely in the two overriding plates. Structural data, fault kinematic analysis, and geomorphic observations provide new elements that help to understand the late Cenozoic evolution of this triple junction. In the Miocene, extension and shortening occurred south and north of the Motagua fault, respectively. This strain regime migrated northward to the Polochic fault after the late Miocene. This shift is interpreted as a ``pull-up'' of North American blocks into the Caribbean realm. To the west, the PMFS interact with a trench-parallel fault zone that links the Tonala fault to the Jalpatagua fault. These faults bound a fore-arc sliver that is shared by the two overriding plates. We propose that the dextral Jalpatagua fault merges with the sinistral PMFS, leaving behind a suturing structure, the Tonala fault. This tectonic ``zipper'' allows the migration of the triple junction. As a result, the fore-arc sliver comes into contact with the North American plate and helps to maintain a linear subduction zone along the trailing edge of the Caribbean plate. All these processes currently make the triple junction increasingly diffuse as it propagates eastward and inland within both overriding plates.

A spatially explicit life cycle inventory of the global textile chain

Life cycle analyses (LCA) approaches require adaptation to reflect the increasing delocalization of production to emerging countries. This work addresses this challenge by establishing a country-level, spatially explicit life cycle inventory (LCI). This study comprises three separate dimensions. The first dimension is spatial: processes and emissions are allocated to the country in which they take place and modeled to take into account local factors. Emerging economies China and India are the location of production, the consumption occurs in Germany, an Organisation for Economic Cooperation and Development country. The second dimension is the product level: we consider two distinct textile garments, a cotton T-shirt and a polyester jacket, in order to highlight potential differences in the production and use phases. The third dimension is the inventory composition: we track CO2, SO2, NO (x), and particulates, four major atmospheric pollutants, as well as energy use. This third dimension enriches the analysis of the spatial differentiation (first dimension) and distinct products (second dimension). We describe the textile production and use processes and define a functional unit for a garment. We then model important processes using a hierarchy of preferential data sources. We place special emphasis on the modeling of the principal local energy processes: electricity and transport in emerging countries. The spatially explicit inventory is disaggregated by country of location of the emissions and analyzed according to the dimensions of the study: location, product, and pollutant. The inventory shows striking differences between the two products considered as well as between the different pollutants considered. For the T-shirt, over 70% of the energy use and CO2 emissions occur in the consuming country, whereas for the jacket, more than 70% occur in the producing country. This reversal of proportions is due to differences in the use phase of the garments. For SO2, in contrast, over two thirds of the emissions occur in the country of production for both T-shirt and jacket. The difference in emission patterns between CO2 and SO2 is due to local electricity processes, justifying our emphasis on local energy infrastructure. The complexity of considering differences in location, product, and pollutant is rewarded by a much richer understanding of a global production-consumption chain. The inclusion of two different products in the LCI highlights the importance of the definition of a product's functional unit in the analysis and implications of results. Several use-phase scenarios demonstrate the importance of consumer behavior over equipment efficiency. The spatial emission patterns of the different pollutants allow us to understand the role of various energy infrastructure elements. The emission patterns furthermore inform the debate on the Environmental Kuznets Curve, which applies only to pollutants which can be easily filtered and does not take into account the effects of production displacement. We also discuss the appropriateness and limitations of applying the LCA methodology in a global context, especially in developing countries. Our spatial LCI method yields important insights in the quantity and pattern of emissions due to different product life cycle stages, dependent on the local technology, emphasizing the importance of consumer behavior. From a life cycle perspective, consumer education promoting air-drying and cool washing is more important than efficient appliances. Spatial LCI with country-specific data is a promising method, necessary for the challenges of globalized production-consumption chains. We recommend inventory reporting of final energy forms, such as electricity, and modular LCA databases, which would allow the easy modification of underlying energy infrastructure.

High precision U/Pb zircon dating of the Chalten Plutonic Complex (Cerro Fitz Roy, Patagonia) and its relationship to arc migration in the southernmost Andes

We report new high-precision U/Pb ages and geochemical data from the Chalten Plutonic Complex to better understand the link between magmatism and tectonics in Southern Patagonia. This small intrusion located in the back-arc region east of the Patagonian Batholith provides important insights on the role of arc migration and subduction erosion. The Chalten Plutonic Complex consists of a suite of calc-alkaline gabbroic to granitic rocks, which were emplaced over 530 kyr between 16.90 +/- 0.05 Ma and 16.37 +/- 0.02 Ma. A synthesis of age and geochemical data from other intrusions in Patagonia reveals (a) striking similarities between the Chalten Plutonic Complex and the Neogene intrusions of the batholith and differences to other back-arc intrusions such as Torres del Paine (b) a distinct E-W trend of calc-alkaline magmatic activity between 20 and 17 Ma. We propose that this trend reflects the eastward migration of the magmatic arc, and the consistent age pattern between the subduction segments north and south of the Chile triple junction suggests a causal relation with a period of fast subduction of the Farallon-Nazca plate during the Early Miocene. Previously proposed flat slab models are not consistent with the present location and morphology of the Southern Patagonian Batholith. We advocate, alternatively, that migration of the magmatic arc is caused by subduction erosion due to the increasing subduction velocities during the Early Miocene.

Stable isotope (C, O, S) systematics of the mercury mineralization at Idrija, Slovenia: constraints on fluid source and alteration processes

The world-class Idrija mercury deposit (western Slovenia) is hosted by highly deformed Permocarboniferous to Middle Triassic sedimentary rocks within a complex tectonic structure at the transition between the External Dinarides and the Southern Alps. Concordant and discordant mineralization formed concomitant with Middle Triassic bimodal volcanism in an aborted rift. A multiple isotopic (C, O, S) investigation of host rocks and ore minerals was performed to put constraints on the source and composition of the fluid, and the hydrothermal alteration. The distributions of the delta(13)C and delta(18)O values of host and gangue carbonates are indicative of a fracture-controlled hydrothermal system, with locally high fluid-rock ratios. Quantitative modeling of the delta(13)C and delta(18)O covariation for host carbonates during temperature dependent fluid-rock interaction, and concomitant precipitation of void-filling dolomites points to a slightly acidic hydrothermal fluid (delta(13)Capproximate to-4parts per thousand and delta(18)Oapproximate to+10parts per thousand), which most likely evolved during isotopic exchange with carbonates under low fluid/rock ratios. The delta(34)S values of hydrothermal and sedimentary sulfur minerals were used to re-evaluate the previously proposed magmatic and evaporitic sulfur sources for the mineralization, and to assess the importance of other possible sulfur sources such as the contemporaneous seawater sulfate, sedimentary pyrite, and organic sulfur compounds. The delta(34)S values of the sulfides show a large variation at deposit down to hand-specimen scale. They range for cinnabar and pyrite from -19.1 to +22.8parts per thousand, and from -22.4 to +59.6parts per thousand, respectively, suggesting mixing of sulfur from different sources. The peak of delta(34)S values of cinnabar and pyrite close to 0parts per thousand is compatible with ore sulfur derived dominantly from a magmatic fluid and/or from hydrothermal leaching of basement rocks. The similar stratigraphic trends of the delta(34)S values of both cinnabar and pyrite suggest a minor contribution of sedimentary sulfur (pyrite and organic sulfur) to the ore formation. Some of the positive delta(34)S values are probably derived from thermochemical reduction of evaporitic and contemporaneous seawater sulfates.

CCL21 is sufficient to mediate DC migration, maturation and function in the absence of CCL19.

Mice deficient in CCR7 signals show severe defects in lymphoid tissue architecture and immune response. These defects are due to impaired attraction of CCR7+ DC and CCR7+ T cells into the T zones of secondary lymphoid organs and altered DC maturation. It is currently unclear which CCR7 ligand mediates these processes in vivo as CCL19 and CCL21 show an overlapping expression pattern and blocking experiments have given contradictory results. In this study, we addressed this question using CCL19-deficient mice expressing various levels of CCL21. Complete deficiency of CCL19 and CCL21 but not CCL19 alone was found to be associated with abnormal frequencies and localization of DC in naïve LN. Similarly, CCL19 was not required for DC migration from the skin, full DC maturation and efficient T-cell priming. Our findings suggest that CCL21 is the critical CCR7 ligand regulating DC homeostasis and function in vivo with CCL19 being redundant for these processes.

Combining Internet Monitoring Processes, Packaging and Isotopic Analyses to Determine The Market Structure: Example of Gamma Butyrolactone

The Internet is becoming more and more popular among drug users. The use of websites and forums to obtain illicit drugs and relevant information about the means of consumption is a growing phenomenon mainly for new synthetic drugs. Gamma Butyrolactone (GBL), a chemical precursor of Gamma Hydroxy Butyric acid (GHB), is used as a "club drug" and also in drug facilitated sexual assaults. Its market takes place mainly on the Internet through online websites but the structure of the market remains unknown. This research aims to combine digital, physical and chemical information to help understand the distribution routes and the structure of the GBL market. Based on an Internet monitoring process, thirty-nine websites selling GBL, mainly in the Netherlands, were detected between January 2010 and December 2011. Seventeen websites were categorized into six groups based on digital traces (e.g. IP addresses and contact information). In parallel, twenty-five bulk GBL specimens were purchased from sixteen websites for packaging comparisons and carbon isotopic measurements. Packaging information showed a high correlation with digital data confirming the links previously established whereas chemical information revealed undetected links and provided complementary information. Indeed, while digital and packaging data give relevant information about the retailers, the supply routes and the distribution close to the consumer, the carbon isotopic data provides upstream information about the production level and in particular the synthesis pathways and the chemical precursors. A three-level structured market has been thereby identified with a production level mainly located in China and in Germany, an online distribution level mainly hosted in the Netherlands and the customers who order on the Internet.

A functional microsatellite of the macrophage migration inhibitory factor gene associated with meningococcal disease.

Macrophage migration inhibitory factor (MIF) is an abundantly expressed proinflammatory cytokine playing a critical role in innate immunity and sepsis and other inflammatory diseases. We examined whether functional MIF gene polymorphisms (-794 CATT(5-8) microsatellite and -173 G/C SNP) were associated with the occurrence and outcome of meningococcal disease in children. The CATT(5) allele was associated with the probability of death predicted by the Pediatric Index of Mortality 2 (P=0.001), which increased in correlation with the CATT(5) copy number (P=0.04). The CATT(5) allele, but not the -173 G/C alleles, was also associated with the actual mortality from meningoccal sepsis [OR 2.72 (1.2-6.4), P=0.02]. A family-based association test (i.e., transmission disequilibrium test) performed in 240 trios with 1 afflicted offspring indicated that CATT(5) was a protective allele (P=0.02) for the occurrence of meningococcal disease. At baseline and after stimulation with Neisseria meningitidis in THP-1 monocytic cells or in a whole-blood assay, CATT(5) was found to be a low-expression MIF allele (P=0.005 and P=0.04 for transcriptional activity; P=0.09 and P=0.09 for MIF production). Taken together, these data suggest that polymorphisms of the MIF gene affecting MIF expression are associated with the occurrence, severity, and outcome of meningococcal disease in children.

A case for hornblende dominated fractionation of arc magmas: the Chelan Complex (Washington Cascades)

Amphibole fractionation in the deep roots of subduction-related magmatic arcs is a fundamental process for the generation of the continental crust. Field relations and geochemical data of exposed lower crustal igneous rocks can be used to better constrain these processes. The Chelan Complex in the western U. S. forms the lowest level of a 40-km thick exposed crustal section of the North Cascades and is composed of olivine websterite, pyroxenite, hornblendite, and dominantly by hornblende gabbro and tonalite. Magmatic breccias, comb layers and intrusive contacts suggest that the Chelan Complex was build by igneous processes. Phase equilibria, textural observations and mineral chemistry yield emplacement pressures of similar to 1.0 GPa followed by isobaric cooling to 700 degrees C. The widespread occurrence of idiomorphic hornblende and interstitial plagioclase together with the lack of Eu anomalies in bulk rock compositions indicate that the differentiation is largely dominated by amphibole. Major and trace element modeling constrained by field observations and bulk chemistry demonstrate that peraluminous tonalite could be derived by removing successively 3% of olivine websterite, 12% of pyroxene hornblendite, 33% of pyroxene hornblendite, 19% of gabbros, 15% of diorite and 2% tonalite. Peraluminous tonalite with high Sr/Y that are worldwide associated with active margin settings can be derived from a parental basaltic melt by crystal fractionation at high pressure provided that amphibole dominates the fractionation process. Crustal assimilation during fractionation is thus not required to generate peraluminous tonalite.

Basin-internal derivation of hydrocarbons in the Witwatersrand Basin, South Africa: evidence from bulk and molecular δ13C data

Gold in the quartz-pebble conglomerates of the late Archean Witwatersrand Basin, South Africa, is often intimately associated with carbonaceous matter of organic/biogenic origin which occurs in the form of stratiform carbon seams and paragenetically late bitumen nodules. Both carbon forms are believed to be formed by solidification of migrating hydrocarbons. This paper presents bulk and molecular chemical and stable carbon isotope data for the carbonaceous matter, all of which are used to provide a clue to the source of the hydrocarbons. These data are compared with those from intra-basinal shales and overlying dolostone of the Transvaal Supergroup. The delta C-13 values of the extracts from the Witwatersrand carbonaceous material show small differences (up to 2.4 parts per thousand) compared to the associated insoluble organic matter. This suggests that the auriferous rocks were stained by mobile hydrocarbons produced by thermal and oxidative alteration of indigenous bitumens, a contribution from hydrocarbons derived from intra-basinal Witwatersrand shales cannot be excluded. Individual aliphatic hydrocarbons of the various carbonaceous materials were subjected to compound specific isotope analysis using on-line gas chromatography/combustion/stable isotope ratio mass spectrometry (GC/C/IRMS). The limited variability of the molecular parameters and uniform delta C-13 values of individual n-alkanes (-31.1 +/- 1.7 parts per thousand) and isoprenoids (-30.7 +/- 1.1 parts per thousand) in the Witwatersrand samples exclude the mixing of oils from different sources. Carbonaceous matter in the dolostones shows distinctly different bulk and molecular isotope characteristics and thus cannot have been the source of the hydrocarbons in the Witwatersrand deposits. All the various forms of Witwatersrand carbon appear indigenous to the Witwatersrand Basin, and the differences between them are explained by variable, in general probably short (centimeter- to meter-scale) hydrocarbon migration during diagenesis and subsequent hydrothermal infiltration. (C) 2001 Elsevier Science B.V. All rights reserved.