MORM--a Petri net based model for assessing OH&S risks in industrial processes: modeling qualitative aspects.

Because of the increase in workplace automation and the diversification of industrial processes, workplaces have become more and more complex. The classical approaches used to address workplace hazard concerns, such as checklists or sequence models, are, therefore, of limited use in such complex systems. Moreover, because of the multifaceted nature of workplaces, the use of single-oriented methods, such as AEA (man oriented), FMEA (system oriented), or HAZOP (process oriented), is not satisfactory. The use of a dynamic modeling approach in order to allow multiple-oriented analyses may constitute an alternative to overcome this limitation. The qualitative modeling aspects of the MORM (man-machine occupational risk modeling) model are discussed in this article. The model, realized on an object-oriented Petri net tool (CO-OPN), has been developed to simulate and analyze industrial processes in an OH&S perspective. The industrial process is modeled as a set of interconnected subnets (state spaces), which describe its constitutive machines. Process-related factors are introduced, in an explicit way, through machine interconnections and flow properties. While man-machine interactions are modeled as triggering events for the state spaces of the machines, the CREAM cognitive behavior model is used in order to establish the relevant triggering events. In the CO-OPN formalism, the model is expressed as a set of interconnected CO-OPN objects defined over data types expressing the measure attached to the flow of entities transiting through the machines. Constraints on the measures assigned to these entities are used to determine the state changes in each machine. Interconnecting machines implies the composition of such flow and consequently the interconnection of the measure constraints. This is reflected by the construction of constraint enrichment hierarchies, which can be used for simulation and analysis optimization in a clear mathematical framework. The use of Petri nets to perform multiple-oriented analysis opens perspectives in the field of industrial risk management. It may significantly reduce the duration of the assessment process. But, most of all, it opens perspectives in the field of risk comparisons and integrated risk management. Moreover, because of the generic nature of the model and tool used, the same concepts and patterns may be used to model a wide range of systems and application fields.

Density-based hierarchical clustering of pyro-sequences on a large scale--the case of fungal ITS1.

MOTIVATION: Analysis of millions of pyro-sequences is currently playing a crucial role in the advance of environmental microbiology. Taxonomy-independent, i.e. unsupervised, clustering of these sequences is essential for the definition of Operational Taxonomic Units. For this application, reproducibility and robustness should be the most sought after qualities, but have thus far largely been overlooked. RESULTS: More than 1 million hyper-variable internal transcribed spacer 1 (ITS1) sequences of fungal origin have been analyzed. The ITS1 sequences were first properly extracted from 454 reads using generalized profiles. Then, otupipe, cd-hit-454, ESPRIT-Tree and DBC454, a new algorithm presented here, were used to analyze the sequences. A numerical assay was developed to measure the reproducibility and robustness of these algorithms. DBC454 was the most robust, closely followed by ESPRIT-Tree. DBC454 features density-based hierarchical clustering, which complements the other methods by providing insights into the structure of the data. AVAILABILITY: An executable is freely available for non-commercial users at ftp://ftp.vital-it.ch/tools/dbc454. It is designed to run under MPI on a cluster of 64-bit Linux machines running Red Hat 4.x, or on a multi-core OSX system. CONTACT: dbc454@vital-it.ch or nicolas.guex@isb-sib.ch.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Evaluation of the user-friendliness of seven new generation intensive care ventilators.

OBJECTIVE: To explore the user-friendliness and ergonomics of seven new generation intensive care ventilators. DESIGN: Prospective task-performing study. SETTING: Intensive care research laboratory, university hospital. METHODS: Ten physicians experienced in mechanical ventilation, but without prior knowledge of the ventilators, were asked to perform eight specific tasks [turning the ventilator on; recognizing mode and parameters; recognizing and setting alarms; mode change; finding and activating the pre-oxygenation function; pressure support setting; stand-by; finding and activating non-invasive ventilation (NIV) mode]. The time needed for each task was compared to a reference time (by trained physiotherapist familiar with the devices). A time >180 s was considered a task failure. RESULTS: For each of the tests on the ventilators, all physicians' times were significantly higher than the reference time (P < 0.001). A mean of 13 +/- 8 task failures (16%) was observed by the ventilator. The most frequently failed tasks were mode and parameter recognition, starting pressure support and finding the NIV mode. Least often failed tasks were turning on the pre-oxygenation function and alarm recognition and management. Overall, there was substantial heterogeneity between machines, some exhibiting better user-friendliness than others for certain tasks, but no ventilator was clearly better that the others on all points tested. CONCLUSIONS: The present study adds to the available literature outlining the ergonomic shortcomings of mechanical ventilators. These results suggest that closer ties between end-users and manufacturers should be promoted, at an early development phase of these machines, based on the scientific evaluation of the cognitive processes involved by users in the clinical setting.

Accuracy of out-of-field dose calculation of tomotherapy and cyberknife treatment planning systems: A dosimetric study.

PURPOSE: Late toxicities such as second cancer induction become more important as treatment outcome improves. Often the dose distribution calculated with a commercial treatment planning system (TPS) is used to estimate radiation carcinogenesis for the radiotherapy patient. However, for locations beyond the treatment field borders, the accuracy is not well known. The aim of this study was to perform detailed out-of-field-measurements for a typical radiotherapy treatment plan administered with a Cyberknife and a Tomotherapy machine and to compare the measurements to the predictions of the TPS. MATERIALS AND METHODS: Individually calibrated thermoluminescent dosimeters were used to measure absorbed dose in an anthropomorphic phantom at 184 locations. The measured dose distributions from 6 MV intensity-modulated treatment beams for CyberKnife and TomoTherapy machines were compared to the dose calculations from the TPS. RESULTS: The TPS are underestimating the dose far away from the target volume. Quantitatively the Cyberknife underestimates the dose at 40cm from the PTV border by a factor of 60, the Tomotherapy TPS by a factor of two. If a 50% dose uncertainty is accepted, the Cyberknife TPS can predict doses down to approximately 10 mGy/treatment Gy, the Tomotherapy-TPS down to 0.75 mGy/treatment Gy. The Cyberknife TPS can then be used up to 10cm from the PTV border the Tomotherapy up to 35cm. CONCLUSIONS: We determined that the Cyberknife and Tomotherapy TPS underestimate substantially the doses far away from the treated volume. It is recommended not to use out-of-field doses from the Cyberknife TPS for applications like modeling of second cancer induction. The Tomotherapy TPS can be used up to 35cm from the PTV border (for a 390 cm(3) large PTV).

Modelling Bone Mineral Density in Swiss Breast Cancer Patients Treated with Letrozole, Tamoxifen and Sequences of Letrozole and Tamoxifen in the BIG 1-98 Study (SAKK 21/07).

Background: Bone health is a concern when treating early stage breast cancer patients with adjuvant aromatase inhibitors. Early detection of patients (pts) at risk of osteoporosis and fractures may be helpful for starting preventive therapies and selecting the most appropriate endocrine therapy schedule. We present statistical models describing the evolution of lumbar and hip bone mineral density (BMD) in pts treated with tamoxifen (T), letrozole (L) and sequences of T and L. Methods: Available dual-energy x-ray absorptiometry exams (DXA) of pts treated in trial BIG 1-98 were retrospectively collected from Swiss centers. Treatment arms: A) T for 5 years, B) L for 5 years, C) 2 years of T followed by 3 years of L and, D) 2 years of L followed by 3 years of T. Pts without DXA were used as a control for detecting selection biases. Patients randomized to arm A were subsequently allowed an unplanned switch from T to L. Allowing for variations between DXA machines and centres, two repeated measures models, using a covariance structure that allow for different times between DXA, were used to estimate changes in hip and lumbar BMD (g/cm2) from trial randomization. Prospectively defined covariates, considered as fixed effects in the multivariable models in an intention to treat analysis, at the time of trial randomization were: age, height, weight, hysterectomy, race, known osteoporosis, tobacco use, prior bone fracture, prior hormone replacement therapy (HRT), bisphosphonate use and previous neo-/adjuvant chemotherapy (ChT). Similarly, the T-scores for lumbar and hip BMD measurements were modeled using a per-protocol approach (allowing for treatment switch in arm A), specifically studying the effect of each therapy upon T-score percentage. Results: A total of 247 out of 546 pts had between 1 and 5 DXA; a total of 576 DXA were collected. Number of DXA measurements per arm were; arm A 133, B 137, C 141 and D 135. The median follow-up time was 5.8 years. Significant factors positively correlated with lumbar and hip BMD in the multivariate analysis were weight, previous HRT use, neo-/adjuvant ChT, hysterectomy and height. Significant negatively correlated factors in the models were osteoporosis, treatment arm (B/C/D vs. A), time since endocrine therapy start, age and smoking (current vs. never).Modeling the T-score percentage, differences from T to L were -4.199% (p = 0.036) and -4.907% (p = 0.025) for the hip and lumbar measurements respectively, before any treatment switch occurred. Conclusions: Our statistical models describe the lumbar and hip BMD evolution for pts treated with L and/or T. The results of both localisations confirm that, contrary to expectation, the sequential schedules do not seem less detrimental for the BMD than L monotherapy. The estimated difference in BMD T-score percent is at least 4% from T to L.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Multi-source composite kernels for urban image classification

This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Interest rates mapping

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well. (C) 2008 Elsevier B.V. All rights reserved.

Detection and chemical profiling of medicine counterfeits by Raman spectroscopy and chemometrics

Raman spectroscopy combined with chemometrics has recently become a widespread technique for the analysis of pharmaceutical solid forms. The application presented in this paper is the investigation of counterfeit medicines. This increasingly serious issue involves networks that are an integral part of industrialized organized crime. Efficient analytical tools are consequently required to fight against it. Quick and reliable authentication means are needed to allow the deployment of measures from the company and the authorities. For this purpose a method in two steps has been implemented here. The first step enables the identification of pharmaceutical tablets and capsules and the detection of their counterfeits. A nonlinear classification method, the Support Vector Machines (SVM), is computed together with a correlation with the database and the detection of Active Pharmaceutical Ingredient (API) peaks in the suspect product. If a counterfeit is detected, the second step allows its chemical profiling among former counterfeits in a forensic intelligence perspective. For this second step a classification based on Principal Component Analysis (PCA) and correlation distance measurements is applied to the Raman spectra of the counterfeits.

Etude des niveaux de concentration d'hydrocarbures aromatiques polycycliques (HAPs) dans la poussière de bois et des marqueurs biologiques d'effets génotoxiques chez les travailleurs du bois

L'exposition aux poussières de bois est associé à un risque accru d'adénocarcinomes des fosses nasales et des sinus paranasaux (SNC, 'Sinonasal cancer') chez les travailleurs du bois. Les poussières de bois sont ainsi reconnues comme cancérogènes avérés pour l'homme par le Centre international de Recherche sur le Cancer (CIRC). Toutefois, l'agent causal spécifique et le mécanisme sous-jacent relatifs au cancer lié aux poussières de bois demeurent inconnus. Une possible explication est une co-exposition aux poussières de bois et aux Hydrocarbures Aromatiques Polycycliques (HAP), ces derniers étant potentiellement cancérogènes. Dans les faits, les travailleurs du bois sont non seulement exposés aux poussières de bois naturel, mais également à celles générées lors d'opérations effectuées à l'aide de machines (ponceuses, scies électriques, etc.) sur des finitions de bois (bois traités) ou sur des bois composites, tels que le mélaminé et les panneaux de fibres à densité moyenne (MDF, 'Medium Density Fiberboard'). Des HAP peuvent en effet être générés par la chaleur produite par l'utilisation de ces machines sur la surface du bois. Les principaux objectifs de cette thèse sont les suivants: (1) quantifier HAP qui sont présents dans les poussières générées lors de diverses opérations courantes effectuées sur différents bois (2) quantifier l'exposition individuelle aux poussières de bois et aux HAP chez les travailleurs, et (3) évaluer les effets génotoxiques (dommages au niveau de l'ADN et des chromosomes) due à l'exposition aux poussières de bois et aux HAP. Cette thèse est composée par une étude en laboratoire (objectif 1) et par une étude de terrain (objectifs 2 et 3). Pour l'étude en laboratoire, nous avons collecté des poussières de différents type de bois (sapin, MDF, hêtre, sipo, chêne, bois mélaminé) générées au cours de différentes opérations (comme le ponçage et le sciage), et ceci dans une chambre expérimentale et dans des conditions contrôlées. Ensuite, pour l'étude de terrain, nous avons suivi, dans le cadre de leur activité professionnelle, 31 travailleurs de sexe masculin (travailleurs du bois et ébenistes) exposés aux poussières de bois pendant deux jours de travail consécutifs. Nous avons également recruté, comme groupe de contrôle, 19 travailleurs non exposés. Pour effectuer une biosurveillance, nous avons collecté des échantillons de sang et des échantillons de cellules nasales et buccales pour chacun des participants. Ces derniers ont également rempli un questionnaire comprenant des données démographiques, ainsi que sur leur style de vie et sur leur exposition professionnelle. Pour les travailleurs du bois, un échantillonnage individuel de poussière a été effectué sur chaque sujet à l'aide d'une cassette fermée, puis nous avons évalué leur exposition à la poussière de bois et aux HAP, respectivement par mesure gravimétrique et par Chromatographie en phase gazeuse combinée à la spectrométrie de masse. L'évaluation des dommages induits à l'ADN et aux chromosomes (génotoxicité) a été, elle, effectuée à l'aide du test des micronoyaux (MN) sur les cellules nasales et buccales et à l'aide du test des comètes sur les échantillons de sang. Nos résultats montrent dans la poussière de la totalité des 6 types de bois étudiés la présence de HAP (dont certains sont cancérogènes). Des différences notoires dans les concentrations ont été néanmoins constatées en fonction du matériau étudié : les concentrations allant de 0,24 ppm pour la poussière de MDF à 7.95 ppm pour le mélaminé. Nos résultats montrent également que les travailleurs ont été exposés individuellement à de faibles concentrations de HAP (de 37,5 à 119,8 ng m-3) durant les opérations de travail du bois, alors que les concentrations de poussières inhalables étaient relativement élevés (moyenne géométrique de 2,8 mg m-3). En ce qui concerne la génotoxicité, les travailleurs exposés à la poussière de bois présentent une fréquence significativement plus élevée en MN dans les cellules nasales et buccales que les travailleurs du groupe témoin : un odds ratio de 3.1 a été obtenu pour les cellules nasales (IC 95% : de 1.8 à 5.1) et un odds ratio de 1,8 pour les cellules buccales (IC 95% : de 1.3 à 2.4). En outre, le test des comètes a montré que les travailleurs qui ont déclaré être exposés aux poussières de MDF et/ou de mélaminé avaient des dommages à l'ADN significativement plus élevés que les deux travailleurs exposés à la poussière de bois naturel (sapin, épicéa, hêtre, chêne) et que les travailleurs du groupe témoin (p <.01). Enfin, la fréquence des MN dans les cellules nasales et buccales augmentent avec les années d'exposition aux poussières de bois. Par contre, il n'y a pas de relation dose-réponse concernant la génotoxicité due à l'exposition journalière à la poussière et aux HAP. Cette étude montre qu'une exposition aux HAP eu bien lieu lors des opérations de travail du bois. Les travailleurs exposés aux poussières de bois, et donc aux HAP, courent un risque plus élevé (génotoxicité) par rapport au groupe témoin. Étant donné que certains des HAP détectés sont reconnus potentiellement cancérogènes, il est envisageable que les HAP générés au cours du travail sur les matériaux de bois sont un des agents responsables de la génotoxicité de la poussière de bois et du risque élevé de SNC observé chez les travailleurs du secteur. Etant donné la corrélation entre augmentation de la fréquence des MN, le test des micronoyaux dans les cellules nasales et buccales constitue sans conteste un futur outil pour la biosurveillance et pour la détection précoce du risque de SNC chez les travailleurs. - Exposures to wood dust have been associated with an elevated risk of adenocarcinomas of the Dasal cavity and the paranasal sinuses (sinonasal cancer or SNC) among wood workers. Wood dust is recognized as a human carcinogen by the International Agency for Research on Cancer. However, the specific cancer causative agent(s) and the mechanism(s) behind wood dust related carcinogenesis remains unknown. One possible explanation is a co-exposure to wood dust and polycyclic aromatic hydrocarbons (PAH), the latter being carcinogenic. In addition, wood workers are not only exposed to natural wood but also to wood finishes and composite woods such as wood melamine and medium density fiber (MDF) boards during the manipulation with power tools. The heat produced by the use of power tools can cause the generation of PAH from wood materials. The main objectives of the present thesis are to: (1) quantify possible PAH concentrations in wood dust generated during various common woodworking operations using different wood materials; (2) quantify personal wood dust concentrations and PAH exposures among wood workers; and (3) assess genotoxic effects (i.e., DNA and chromosomal damage) of wood dust and PAH exposure in wood workers. This thesis is composed by a laboratory study (objective 1) and a field study (objectives 2 and 3). In the laboratory study we collected wood dust from different wood materials (fir, MDF, beech, mahagany, oak, and wood melamine) generated during different wood operations (e.g., sanding and sawing) in an experimental chamber under controlled conditions. In the following field study, we monitored 31 male wood workers (furniture and construction workers) exposed to wood dust during their professional activity for two consecutive work shifts. Additionally, we recruited 19 non exposed workers as a control group. We collected from each participant blood samples, and nasal and buccal cell samples. They answered a questionnaire including demographic and life-style data and occupational exposure (current and past). Personal wood dust samples were collected using a closed-face cassette. We used gravimetrie analysis to determine the personal wood dust concentrations and capillary gas chromatography - mass spectrometry analysis to determine PAH concentrations. Genotoxicity was assessed with the micronucleus (MN) assay for nasal and buccal cells and with the comet assay for blood samples. Our results show that PAH (some of them carcinogenic) were present in dust from all six wood materials tested, yet at different concentrations depending on the material. The highest concentration was found in dust from wood melamine (7.95 ppm) and the lowest in MDF (0.24 ppm). Our results also show that workers were individually exposed to low concentrations of PAHs (37.5-119.8 ng m"3) during wood working operations, whereas the concentrations of inhalable dust were relatively high (geometric mean 2.8 mg m"3). Concerning the genotoxicity, wood workers had a significantly higher MN frequency in nasal and buccal cells than the workers in the control group (odds ratio for nasal cells 3.1 (95%CI 1.8-5.1) and buccal cells 1.8 (95%CI 1.3-2.4)). Furthermore, the comet assay showed that workers who reported to be exposed to dust from wooden boards (MDF and wood melamine) had significantly higher DNA damage than both the workers exposed to natural woods (fir, spruce, beech, oak) and the workers in the control group (p < 0.01). Finally, MN frequency in nasal and buccal cells increased with increasing years of exposure to wood dust. However, there was no genotoxic dose-response relationship with the per present day wood dust and PAH exposure. This study shows that PAH exposure occurred during wood working operations. Workers exposed to wood dust, and thus to PAH, had a higher risk for genotoxicity compared to the control group. Since some of the detected PAH are potentially carcinogenic, PAH generated from operations on wood materials may be one of the causative agents for the observed increased genotoxicity in wood workers. Since increased genotoxicity is manifested in an increased MN frequency, the MN assay in nasal and buccal cells may become a relevant biomonitoring tool in the future for early detection of SNC risk.