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.

Recombinant " Physcomitrella patens " as a sensitive tool to study heat sensing and heat-shock signal transduction in plants

SUMMARY When exposed to heat stress, plants display a particular set of cellular and molecular responses, such as chaperones expression, which are highly conserved in all organisms. In chapter 1, I studied the ability of heat shock genes to become transiently and abundantly induced under various temperature regimes. To this aim, I designed a highly sensitive heat-shock dependent conditional gene expression system in the moss Physcomitrella patens, using the soybean heatinducible promoter (hsp17.3B). Heat-induced expression of various reporter genes was over three orders of magnitude, in tight correlation with the intensity and duration of the heat treatments. By performing repeated heating/cooling cycles, a massive accumulation of recombinant proteins was obtained. Interestingly, the hsp17.3B promoter was also activated by specific organic chemicals. Thus, in chapter 2, I took advantage of the extreme sensitivity of this promoter to small temperature variations to further address the role of various natural and organic chemicals and develop a plant based-bioassay that can serve as an early warning indicator of toxicity by pollutants and heavy metals. A screen of several organic pollutants from textile and paper industry showed that chlorophenols as well as sulfonated anthraquinones elicited a heat shock like response at noninducing temperatures. Their effects were synergistically amplified by mild elevated temperatures. In contrast to standard methods of pollutant detection, this plant-based biosensor allowed to monitor early stress-responses, in correlation with long-term toxic effect, and to attribute effective toxicity thresholds for pollutants, in a context of varying environmental cues. In chapter 3, I deepened the study of the primary mechanism by which plants sense mild temperature variations and trigger a cellular signal leading to the heat shock response. In addition to the above described heat-inducible reporter line, I generated a P. patens transgenic line to measure, in vivo, variations of cytosolic calcium during heat treatment, and another line to monitor the role of protein unfolding in heat-shock sensing and signalling. The heat shock signalling pathway was found to be triggered by the plasma membrane, where temperature up shift specifically induced the transient opening of a putative high afimity calcium channel. The calcium influx triggered a signalling cascade leading to the activation of the heat shock genes, independently on the presence of misfolded proteins in the cytoplasm. These results strongly suggest that changes in the fluidity of the plasma membrane are the primary trigger of the heatshocksignalling pathway in plants. The present thesis contributes to the understanding of the basic mechanism by which plants perceive and respond to heat and chemical stresses. This may contribute to developing appropriate better strategies to enhance plant productivity under the increasingly stressful environment of global warming. RÉSUME Les plantes exposées à des températures élevées déclenchent rapidement des réponses cellulaires qui conduisent à l'induction de gènes codant pour les heat shock proteins (HSPs). En fonction de la durée d'exposition et de la vitesse à laquelle la température augmente, les HSPs sont fortement et transitoirement induites. Dans le premier chapitre, cette caractéristique aété utilisée pour développer un système inductible d'expression de gènes dans la mousse Physcomitrella patens. En utilisant plusieurs gènes rapporteurs, j'ai montré que le promoteur du gène hsp17.3B du Soja est activé d'une manière. homogène dans tous les tissus de la mousse proportionnellement à l'intensité du heat shock physiologique appliqué. Un très fort taux de protéines recombinantes peut ainsi être produit en réalisant plusieurs cycles induction/recovery. De plus, ce promoteur peut également être activé par des composés organiques, tels que les composés anti-inflammatoires, ce qui constitue une bonne alternative à l'induction par la chaleur. Les HSPs sont induites pour remédier aux dommages cellulaires qui surviennent. Étant donné que le promoteur hsp17.3B est très sensible à des petites augmentations de température ainsi qu'à des composés chimiques, j'ai utilisé les lignées développées dans le chapitre 1 pour identifier des polluants qui déclenchent une réaction de défense impliquant les HSPs. Après un criblage de plusieurs composés, les chlorophénols et les antraquinones sulfonés ont été identifiés comme étant activateurs du promoteur de stress. La détection de leurs effets a été réalisée seulement après quelques heures d'exposition et corrèle parfaitement avec les effets toxiques détectés après de longues périodes d'exposition. Les produits identifiés montrent aussi un effet synergique avec la température, ce qui fait du biosensor développé dans ce chapitre un bon outil pour révéler les effets réels des polluants dans un environnement où les stress chimiques sont combinés aux stress abiotiques. Le troisième chapitre est consacré à l'étude des mécanismes précoces qui permettent aux plantes de percevoir la chaleur et ainsi de déclencher une cascade de signalisation spécifique qui aboutit à l'induction des gènes HSPs. J'ai généré deux nouvelles lignées afin de mesurer en temps réel les changements de concentrations du calcium cytosolique ainsi que l'état de dénaturation des protéines au cours du heat shock. Quand la fluidité de la membrane augmente après élévation de la température, elle semble induire l'ouverture d'un canal qui permet de faire entrer le calcium dans les cellules. Ce dernier initie une cascade de signalisation qui finit par activer la transcription des gènes HSPs indépendamment de la dénaturation de protéines cytoplasmiques. Les résultats présentés dans ce chapitre montrent que la perception de la chaleur se fait essentiellement au niveau de la membrane plasmique qui joue un rôle majeur dans la régulation des gènes HSPs. L'élucidation des mécanismes par lesquels les plantes perçoivent les signaux environnementaux est d'une grande utilité pour le développement de nouvelles stratégies afin d'améliorer la productivité des plantes soumises à des conditions extrêmes. La présente thèse contribue à décortiquer la voie de signalisation impliquée dans la réponse à la chaleur.

Dynamic responses of oxygen uptake at the onset and end of moderate and heavy exercise in trained subjects.

Inconsistencies about dynamic asymmetry between the on- and off-transient responses in VO2 are found in the literature. Therefore the purpose of this study was to examine VO2 on- and off-transients during moderate- and heavy-intensity cycling exercise in trained subjects. Ten men underwent an initial incremental test for the estimation of ventilatory threshold (VT) and, on different days, two bouts of square-wave exercise at moderate (<VT) and heavy (>VT) intensities. VO2 kinetics in exercise and recovery were better described by a single exponential model (<VT), or by a double exponential with two time delays (>VT). For moderate exercise, we found a symmetry of VO2 kinetics between the on- and off-transients (i.e., fundamental component), consistent with a system manifesting linear control dynamics. For heavy exercise, a slow component superimposed on the fundamental phase was expressed in both the exercise and recovery, with similar parameter estimates. But the on-transient values of the time constant were appreciably faster than the associated off-transient, and independent of the work rate imposed (<VT and >VT). Our results do not support a dynamically linear system model of VO2 during cycling exercise in the heavy-intensity domain.

A DGT Technique for Plutonium Bioavailability Measurements.

The toxicity of heavy metals in natural waters is strongly dependent on the local chemical environment. Assessing the bioavailability of radionuclides predicts the toxic effects to aquatic biota. The technique of diffusive gradients in thin films (DGT) is largely exploited for bioavailability measurements of trace metals in waters. However, it has not been applied for plutonium speciation measurements yet. This study investigates the use of DGT technique for plutonium bioavailability measurements in chemically different environments. We used a diffusion cell to determine the diffusion coefficients (D) of plutonium in polyacrylamide (PAM) gel and found D in the range of 2.06-2.29 × 10(-6) cm(2) s(-1). It ranged between 1.10 and 2.03 × 10(-6) cm(2) s(-1) in the presence of fulvic acid and in natural waters with low DOM. In the presence of 20 ppm of humic acid of an organic-rich soil, plutonium diffusion was hindered by a factor of 5, with a diffusion coefficient of 0.50 × 10(-6) cm(2) s(-1). We also tested commercially available DGT devices with Chelex resin for plutonium bioavailability measurements in laboratory conditions and the diffusion coefficients agreed with those from the diffusion cell experiments. These findings show that the DGT methodology can be used to investigate the bioaccumulation of the labile plutonium fraction in aquatic biota.

Involvement of environmental mercury and lead in the etiology of neurodegenerative diseases.

The incidence of neurodegenerative disease like Parkinson's disease and Alzheimer's disease (AD) increases dramatically with age; only a small percentage is directly related to familial forms. The etiology of the most abundant, sporadic forms is complex and multifactorial, involving both genetic and environmental factors. Several environmental pollutants have been associated with neurodegenerative disorders. The present article focuses on results obtained in experimental neurotoxicology studies that indicate a potential pathogenic role of lead and mercury in the development of neurodegenerative diseases. Both heavy metals have been shown to interfere with a multitude of intracellular targets, thereby contributing to several pathogenic processes typical of neurodegenerative disorders, including mitochondrial dysfunction, oxidative stress, deregulation of protein turnover, and brain inflammation. Exposure to heavy metals early in development can precondition the brain for developing a neurodegenerative disease later in life. Alternatively, heavy metals can exert their adverse effects through acute neurotoxicity or through slow accumulation during prolonged periods of life. The pro-oxidant effects of heavy metals can exacerbate the age-related increase in oxidative stress that is related to the decline of the antioxidant defense systems. Brain inflammatory reactions also generate oxidative stress. Chronic inflammation can contribute to the formation of the senile plaques that are typical for AD. In accord with this view, nonsteroidal anti-inflammatory drugs and antioxidants suppress early pathogenic processes leading to Alzheimer's disease, thus decreasing the risk of developing the disease. The effects of lead and mercury were also tested in aggregating brain-cell cultures of fetal rat telencephalon, a three-dimensional brain-cell culture system. The continuous application for 10 to 50 days of non-cytotoxic concentrations of heavy metals resulted in their accumulation in brain cells and the occurrence of delayed toxic effects. When applied at non-toxic concentrations, methylmercury, the most common environmental form of mercury, becomes neurotoxic under pro-oxidant conditions. Furthermore, lead and mercury induce glial cell reactivity, a hallmark of brain inflammation. Both mercury and lead increase the expression of the amyloid precursor protein; mercury also stimulates the formation of insoluble beta-amyloid, which plays a crucial role in the pathogenesis of AD and causes oxidative stress and neurotoxicity in vitro. Taken together, a considerable body of evidence suggests that the heavy metals lead and mercury contribute to the etiology of neurodegenerative diseases and emphasizes the importance of taking preventive measures in this regard.

Molecular chaperones and associated cellular clearance mechanisms against toxic protein conformers in Parkinson's disease.

Parkinson's disease (PD) is a slowly progressive neurodegenerative disorder marked by the loss of dopaminergic neurons (in particular in the substantia nigra) causing severe impairment of movement coordination and locomotion, associated with the accumulation of aggregated α-synuclein (α-Syn) into proteinaceous inclusions named Lewy bodies. Various early forms of misfolded α-Syn oligomers are cytotoxic. Their formation is favored by mutations and external factors, such as heavy metals, pesticides, trauma-related oxidative stress and heat shock. Here, we discuss the role of several complementing cellular defense mechanisms that may counteract PD pathogenesis, especially in youth, and whose effectiveness decreases with age. Particular emphasis is given to the 'holdase' and 'unfoldase' molecular chaperones that provide cells with potent means to neutralize and scavenge toxic protein conformers. Because chaperones can specifically recognize misfolded proteins, they are key specificity factors for other cellular defenses, such as proteolysis by the proteasome and autophagy. The efficiency of the cellular defenses decreases in stressed or aging neurons, leading to neuroinflammation, apoptosis and tissue loss. Thus, drugs that can upregulate the molecular chaperones, the ubiquitin-proteasome system and autophagy in brain tissues are promising avenues for therapies against PD and other mutation-, stress- or age-dependent protein-misfolding diseases.

Assessment of diesel exhaust particulate exposure and surface characteristics in association with levels of oxidative stress biomarkers

Exposure to PM10 and PM2.5 (particulate matter with aerodynamic diameter smaller than 10 μm and 2.5 μm, respectively) is associated with a range of adverse health effects, including cancer, pulmonary and cardiovascular diseases. Surface characteristics (chemical reactivity, surface area) are considered of prime importance to understand the mechanisms which lead to harmful effects. A hypothetical mechanism to explain these adverse effects is the ability of components (organics, metal ions) adsorbed on these particles to generate Reactive Oxygen Species (ROS), and thereby to cause oxidative stress in biological systems (Donaldson et al., 2003). ROS can attack almost any cellular structure, like DNA or cellular membrane, leading to the formation of a wide variety of degradation products which can be used as a biomarker of oxidative stress. The aim of the present research project is to test whether there is a correlation between the exposure to Diesel Exhaust Particulate (DEP) and the oxidative stress status. For that purpose, a survey has been conducted in real occupational situations where workers were exposed to DEP (bus depots). Different exposure variables have been considered: - particulate number, size distribution and surface area (SMPS); - particulate mass - PM2.5 and PM4 (gravimetry); - elemental and organic carbon (coulometry); - total adsorbed heavy metals - iron, copper, manganese (atomic adsorption); - surface functional groups present on aerosols (Knudsen flow reactor). (Demirdjian et al., 2005). Several biomarkers of oxidative stress (8-hydroxy-2'-deoxyguanosine and several aldehydes) have been determined either in urine or serum of volunteers. Results obtained during the sampling campaign in several bus depots indicated that the occupational exposure to particulates in these places was rather low (40-50 μg/m3 for PM4). Size distributions indicated that particles are within the nanometric range. Surface characteristics of sampled particles varied strongly, depending on the bus depot. They were usually characterized by high carbonyl and low acidic sites content. Among the different biomarkers which have been analyzed within the framework of this study, mean levels of 8- hydroxy-2'-deoxyguanosine and several aldehydes (hexanal, heptanal, octanal, nonanal) increased during two consecutive days of exposure for non-smokers. In order to bring some insight into the relation between the particulate characteristics and the formation of ROS by-products, biomarkers levels will be discussed in relation with exposure variables.

Metal concentrations in soils around the copper smelter and surrounding industrial complex of Port Kembla, NSW, Australia

Anthropogenic emissions of metals from sources such as smelters are an international problem, but there is limited published information on emissions from Australian smelters. The objective of this study was to investigate the regional distribution of heavy metals in soils in the vicinity of the industrial complex of Port Kembla, NSW, Australia, which comprises a copper smelter, steelworks and associated industries. Soil samples (n=25) were collected at the depths of 0-5 and 5-20 cm, air dried and sieved to < 2 mm. Aqua regia extractable amounts of As, Cr, Cu, Ph and Zn were analysed by inductively coupled plasma mass spectrometry (lCP-MS) and inductively coupled plasma atomic emission spectrometry (ICP-AES). Outliers were identified from background levels by statistical methods. Mean background levels at a depth of 0-5 cm were estimated at 3.2 mg/kg As, 12 mg/kg Cr, 49 mg/kg Cu, 20 mg/kg Ph and 42 mg/kg Zn. Outliers for elevated As and Cu values were mainly present within 4 km from the Port Kembla industrial complex, but high Ph at two sites and high Zn concentrations were found at six sites up to 23 km from Port Kembla. Chromium concentrations were not anomalous close to the industrial complex. There was no significant difference of metal concentrations at depths of 0-5 and 5-20 cm, except for Ph and Zn. Copper and As concentrations in the soils are probably related to the concentrations in the parent rock. From this investigation, the extent of the contamination emanating from the Port Kembla industrial complex is limited to 1-13 km, but most likely <4 km, depending on the element; the contamination at the greater distance may not originate from the industrial complex. (C) 2003 Elsevier B.V. All rights reserved.

High-resolution ammonite and carbon isotope stratigraphy across the Triassic-Jurassic boundary at New York Canyon (Nevada)

The Triassic-Jurassic boundary is generally considered as one of the major extinctions in the history of Phanerozoic. The high-resolution ammonite correlations and carbon isotope marine record in the New York Canyon area allow to distinguish two negative carbon excursions across this boundary with different paleoenvironmental meanings. The Late Rhaetian negative excursion is related to the extinction and regressive phase. The Early Hettangian delta(13)C(org) negative excursion is associated with a major floristic turnover and major ammonite and radiolarian radiation. The end-Triassic extinction-Early Jurassic recovery is fully compatible with a volcanism-triggered crisis, probably related to the Central Atlantic Magmatic Province. The main environmental stress might have been generated by repeated release of SO(2) gas, heavy metals emissions, darkening, and subsequent cooling. This phase was followed by a major long-term CO(2) accumulation during the Early Hettangian with development of nutrient-rich marine waters favouring the recovery of productivity and deposition of black shales. (C) 2004 Elsevier B.V. All rights reserved.

Assessment of diesel exhaust particulate exposure and surface characteristics in association with levels of oxidative stress biomarkers

Exposure to PM10 and PM2.5 (particulate matter with aerodynamic diameter smaller than 10 μm and 2.5 μm, respectively) is associated with a range of adverse health effects, including cancer, pulmonary and cardiovascular diseases. Surface characteristics (chemical reactivity, surface area) are considered of prime importance to understand the mechanisms which lead to harmful effects. A hypothetical mechanism to explain these adverse effects is the ability of components (organics, metal ions) adsorbed on these particles to generate Reactive Oxygen Species (ROS), and thereby to cause oxidative stress in biological systems (Donaldson et al., 2003). ROS can attack almost any cellular structure, like DNA or cellular membrane, leading to the formation of a wide variety of degradation products which can be used as a biomarker of oxidative stress. The aim of the present research project is to test whether there is a correlation between the exposure to Diesel Exhaust Particulate (DEP) and the oxidative stress status. For that purpose, a survey has been conducted in real occupational situations where workers were exposed to DEP (bus depots). Different exposure variables have been considered: - particulate number, size distribution and surface area (SMPS); - particulate mass - PM2.5 and PM4 (gravimetry); - elemental and organic carbon (coulometry); - total adsorbed heavy metals - iron, copper, manganese (atomic adsorption); - surface functional groups present on aerosols (Knudsen flow reactor). Several biomarkers of oxidative stress (8-hydroxy-2'-deoxyguanosine and several aldehydes) have been determined either in urine or serum of volunteers. Results obtained during the sampling campaign in several bus depots indicated that the occupational exposure to particulates in these places was rather low (40-50 μg/m3 for PM4). Bimodal size distributions were generally observed (5 μm and <1 μm). Surface characteristics of PM4 varied strongly, depending on the bus depot. They were usually characterized by high carbonyl and low acidic sites content. Among the different biomarkers which have been analyzed within the framework of this study, mean urinary levels of 8-hydroxy-2'-deoxyguanosine increased significantly (p<0.05) during two consecutive days of exposure for non-smoker workers. On the other hand, no statistically significant differences were observed for serum levels of hexanal, nonanal and 4- hydroxy-nonenal (p>0.05). Biomarkers levels will be compared to exposure variables to gain a better understanding of the relation between the particulate characteristics and the formation of ROS by-products. This project is financed by the Swiss State Secretariat for Education and Research. It is conducted within the framework of the COST Action 633 "Particulate Matter - Properties Related to Health Effects".

Heavy metal ions are potent inhibitors of protein folding.

Environmental and occupational exposure to heavy metals such as cadmium, mercury and lead results in severe health hazards including prenatal and developmental defects. The deleterious effects of heavy metal ions have hitherto been attributed to their interactions with specific, particularly susceptible native proteins. Here, we report an as yet undescribed mode of heavy metal toxicity. Cd2+, Hg2+ and Pb2+ proved to inhibit very efficiently the spontaneous refolding of chemically denatured proteins by forming high-affinity multidentate complexes with thiol and other functional groups (IC(50) in the nanomolar range). With similar efficacy, the heavy metal ions inhibited the chaperone-assisted refolding of chemically denatured and heat-denatured proteins. Thus, the toxic effects of heavy metal ions may result as well from their interaction with the more readily accessible functional groups of proteins in nascent and other non-native form. The toxic scope of heavy metals seems to be substantially larger than assumed so far.

The role of cisplatin and NAMI-A plasma-protein interactions in relation to combination therapy

The aim of the study is to evaluate the differences of protein binding of NAMI-A, a new ruthenium drug endowed with selective antimetastatic properties, and of cisplatin and to ascertain the possibility to use two drugs based on heavy metals in combination to treat solid tumour metastases. For this purpose, we have developed a technique that allows the proteins, to which metal drugs bind, to be identified from real protein mixtures. Following incubation with the drugs, the bands containing platinum and/or ruthenium are separated by native PAGE, SDS-PAGE and 2D gel electrophoresis, and identified using laser ablation inductively coupled plasma mass spectrometry. Both drugs interact with essentially the same proteins which, characterised by proteomics, are human serum albumin precursor, macroglobulin alpha 2 and human serotransferrin precursor. The interactions of NAMI-A are largely reversible whereas cisplatin forms stronger interactions that are less reversible. These data correlate well with the MCa mammary carcinoma model on which full doses of NAMI-A combined with cisplatin show additive effects as compared to each treatment taken alone, independently of whether NAMI-A precedes or follows cisplatin. Furthermore, the implication from this study is that the significantly lower toxicity of NAMI-A, compared to cisplatin, could be a consequence of differences in the mode of binding to plasma proteins, involving weaker interactions compared to cisplatin.

Relationship between cigarette smoking and abdominal obesity

Background: Although smokers tend to have a lower body-mass index (BMI) than non-smokers, smoking may affect body fat (BF) distribution. Some studies have assessed the association between smoking, BMI and waist circumference (WC), but, to our knowledge, no population-based studies assessed the relation between smoking and BF composition. We assessed the association between amount of cigarette smoking, BMI, WC and BF composition. Method: Data was analysed from a cross-sectional population-based study including 6'187 Caucasians aged 32-76 and living in Switzerland. Height, weight and WC were measured. BF, expressed in percent of total body weight, was measured by electrical bioimpedance. Abdominal obesity was defined as a WC 0102 cm for men and 088 cm for women and normal WC as <94 cm for men and <80 cm for women. In men, excess BF was defined as %BF 028.1, 28.7, 30.6 and 32.6 for age groups 32-44, 45-54, 55-64 and 65-76, respectively; the corresponding values for women were 35.9, 36.5, 40.5 and 44.4. Cigarette smoking was assessed using a self-reported questionnaire. Results: 29.3% of men and 25.0% of women were smokers. Prevalence of obesity, abdominal obesity, and excess of BF was 16.9% and 26.6% and 14.2% in men and 15.0%, 33.0% and 27.5% in women, respectively. Smokers had lower age-adjusted mean WC and percent of BF compared to non-smokers. However, among smokers, mean age-adjusted WC and BF increased with the number of cigarettes smoked per day: among light (1-10 cig/day), moderate (11-20) and heavy smokers (>20), mean ± SE %BF was 22.4 ± 0.3, 23.1 ± 0.3 and 23.5 ± 0.4 for men, and 31.9 ± 0.3, 32.6 ± 0.3 and 32.9 ± 0.4 for women, respectively. Mean WC was 92.9 ± 0.6, 94.0 ± 0.5 and 96.0 ± 0.6 cm for men, and 80.2 ± 0.5, 81.3 ± 0.5 and 83.3 ± 0.7 for women, respectively. Compared with light smokers, the age-adjusted odds ratio (95% Confidence Interval) for excess of BF was 1.04 (0.58 to 1.85) for moderate smokers and 1.06 (0.57 to 1.99) for heavy smokers in men (p-trend = 0.9), and 1.35 (0.92 to 1.99) and 2.26 (1.38 to 3.72), respectively, in women (p-trend = 0.04). Odds ratio for abdominal obesity vs. normal WC was 1.32 (0.81 to 2.15) for moderate smokers and 1.95 (1.16 to 3.27) for heavy smokers in men (p-trend <0.01), and 1.15 (0.79 to 1.69) and 2.36 (1.41 to 3.93) in women (p-trend = 0.03). Conclusion: WC and BF were positively and dose-dependently associated with the number of cigarettes smoked per day in women, whereas only WC was dose dependently and significantly associated with the amount of cigarettes smoked per day in men. This suggests that heavy smokers, especially women, are more likely to have an excess of BF and to accumulate BF in the abdomen compared to lighter smokers.