Statistical methods for river runoff prediction

Methods used to analyze one type of nonstationary stochastic processes?the periodically correlated process?are considered. Two methods of one-step-forward prediction of periodically correlated time series are examined. One-step-forward predictions made in accordance with an autoregression model and a model of an artificial neural network with one latent neuron layer and with an adaptation mechanism of network parameters in a moving time window were compared in terms of efficiency. The comparison showed that, in the case of prediction for one time step for time series of mean monthly water discharge, the simpler autoregression model is more efficient.

Pathological Reorganization of NMDA Receptors Subunits and Postsynaptic Protein PSD-95 Distribution in Alzheimer's Disease.

In Alzheimer's disease (AD), synaptic alterations play a major role and are often correlated with cognitive changes. In order to better understand synaptic modifications, we compared alterations in NMDA receptors and postsynaptic protein PSD-95 expression in the entorhinal cortex (EC) and frontal cortex (FC; area 9) of AD and control brains. We combined immunohistochemical and image analysis methods to quantify on consecutive sections the distribution of PSD-95 and NMDA receptors GluN1, GluN2A and GluN2B in EC and FC from 25 AD and control cases. The density of stained receptors was analyzed using multivariate statistical methods to assess the effect of neurodegeneration. In both regions, the number of neuronal profiles immunostained for GluN1 receptors subunit and PSD-95 protein was significantly increased in AD compared to controls (3-6 fold), while the number of neuronal profiles stained for GluN2A and GluN2B receptors subunits was on the contrary decreased (3-4 fold). The increase in marked neuronal profiles was more prominent in a cortical band corresponding to layers 3 to 5 with large pyramidal cells. Neurons positive for GluN1 or PSD-95 staining were often found in the same localization on consecutive sections and they were also reactive for the anti-tau antibody AD2, indicating a neurodegenerative process. Differences in the density of immunoreactive puncta representing neuropile were not statistically significant. Altogether these data indicate that GluN1 and PSD-95 accumulate in the neuronal perikarya, but this is not the case for GluN2A and GluN2B, while the neuropile compartment is less subject to modifications. Thus, important variations in the pattern of distribution of the NMDA receptors subunits and PSD-95 represent a marker in AD and by impairing the neuronal network, contribute to functional deterioration.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

Development of a retrospective job exposure matrix for PCB-exposed workers in capacitor manufacturing

Objectives: Polychlorinated biphenyls (PCBs) are considered probable human carcinogens by the International Agency for Research on Cancer and one congener, PCB126, has been rated as a known human carcinogen. A period-specific job exposure matrix (JEM) was developed for former PCB-exposed capacitor manufacturing workers (n=12,605) (1938-1977). Methods: A detailed exposure assessment for this plant was based on a number of exposure determinants (proximity, degree of contact with PCBs, temperature, ventilation, process control, job mobility). The intensity and frequency of PCB exposures by job for both inhalation and dermal exposures, and additional chemical exposures were reviewed. The JEM was developed in nine steps: (1) all unique jobs (n=1,684) were assessed using (2) defined PCB exposure determinants; (3) the exposure determinants were used to develop exposure profiles; (4) similar exposure profiles were combined into categories having similar PCB exposures; (5) qualitative intensity (high-medium-low-baseline) and frequency (continuous-intermittent) ratings were developed, and (6) used to qualitatively rate inhalation and dermal exposure separately for each category; (7) quantitative intensity ratings based on available air concentrations were developed for inhalation and dermal exposures based on equal importance of both routes of exposure; (8) adjustments were made for overall exposure, and (9) for each category the product of intensity and frequency was calculated, and exposure in the earlier era was weighted. Results: A period-specific JEM modified for two eras of stable PCB exposure conditions. Conclusions: These exposure estimates, derived from a systematic and rigorous use of the exposure determinant data, lead to cumulative PCB exposure-response relationships in the epidemiological cancer mortality and incidence studies of this cohort. [Authors]

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Analysis of susceptibility to human immunodeficiency virus 1 (HIV-1) by comparative genetics

Summary [résumé français voir ci-dessous] From the beginning of the 20th century the world population has been confronted with the human immune deficiency virus 1 (HIV-1). This virus has the particularity to mutate fast, and could thus evade and adapt to the human host. Our closest evolutionary related organisms, the non-human primates, are less susceptible to HIV-1. In a broader sense, primates are differentially susceptible to various retrovirus. Species specificity may be due to genetic differences among primates. In the present study we applied evolutionary and comparative genetic techniques to characterize the evolutionary pattern of host cellular determinants of HIV-1 pathogenesis. The study of the evolution of genes coding for proteins participating to the restriction or pathogenesis of HIV-1 may help understanding the genetic basis of modern human susceptibility to infection. To perform comparative genetics analysis, we constituted a collection of primate DNA and RNA to allow generation of de novo sequence of gene orthologs. More recently, release to the public domain of two new primate complete genomes (bornean orang-utan and common marmoset) in addition of the three previously available genomes (human, chimpanzee and Rhesus monkey) help scaling up the evolutionary and comparative genome analysis. Sequence analysis used phylogenetic and statistical methods for detecting molecular adaptation. We identified different selective pressures acting on host proteins involved in HIV-1 pathogenesis. Proteins with HIV-1 restriction properties in non-human primates were under strong positive selection, in particular in regions of interaction with viral proteins. These regions carried key residues for the antiviral activity. Proteins of the innate immunity presented an evolutionary pattern of conservation (purifying selection) but with signals of relaxed constrain if we compared them to the average profile of purifying selection of the primate genomes. Large scale analysis resulted in patterns of evolutionary pressures according to molecular function, biological process and cellular distribution. The data generated by various analyses served to guide the ancestral reconstruction of TRIM5a a potent antiviral host factor. The resurrected TRIM5a from the common ancestor of Old world monkeys was effective against HIV-1 and the recent resurrected hominoid variants were more effective against other retrovirus. Thus, as the result of trade-offs in the ability to restrict different retrovirus, human might have been exposed to HIV-1 at a time when TRIM5a lacked the appropriate specific restriction activity. The application of evolutionary and comparative genetic tools should be considered for the systematical assessment of host proteins relevant in viral pathogenesis, and to guide biological and functional studies. Résumé La population mondiale est confrontée depuis le début du vingtième siècle au virus de l'immunodéficience humaine 1 (VIH-1). Ce virus a un taux de mutation particulièrement élevé, il peut donc s'évader et s'adapter très efficacement à son hôte. Les organismes évolutivement le plus proches de l'homme les primates nonhumains sont moins susceptibles au VIH-1. De façon générale, les primates répondent différemment aux rétrovirus. Cette spécificité entre espèces doit résider dans les différences génétiques entre primates. Dans cette étude nous avons appliqué des techniques d'évolution et de génétique comparative pour caractériser le modèle évolutif des déterminants cellulaires impliqués dans la pathogenèse du VIH- 1. L'étude de l'évolution des gènes, codant pour des protéines impliquées dans la restriction ou la pathogenèse du VIH-1, aidera à la compréhension des bases génétiques ayant récemment rendu l'homme susceptible. Pour les analyses de génétique comparative, nous avons constitué une collection d'ADN et d'ARN de primates dans le but d'obtenir des nouvelles séquences de gènes orthologues. Récemment deux nouveaux génomes complets ont été publiés (l'orang-outan du Bornéo et Marmoset commun) en plus des trois génomes déjà disponibles (humain, chimpanzé, macaque rhésus). Ceci a permis d'améliorer considérablement l'étendue de l'analyse. Pour détecter l'adaptation moléculaire nous avons analysé les séquences à l'aide de méthodes phylogénétiques et statistiques. Nous avons identifié différentes pressions de sélection agissant sur les protéines impliquées dans la pathogenèse du VIH-1. Des protéines avec des propriétés de restriction du VIH-1 dans les primates non-humains présentent un taux particulièrement haut de remplacement d'acides aminés (sélection positive). En particulier dans les régions d'interaction avec les protéines virales. Ces régions incluent des acides aminés clé pour l'activité de restriction. Les protéines appartenant à l'immunité inné présentent un modèle d'évolution de conservation (sélection purifiante) mais avec des traces de "relaxation" comparé au profil général de sélection purifiante du génome des primates. Une analyse à grande échelle a permis de classifier les modèles de pression évolutive selon leur fonction moléculaire, processus biologique et distribution cellulaire. Les données générées par les différentes analyses ont permis la reconstruction ancestrale de TRIM5a, un puissant facteur antiretroviral. Le TRIM5a ressuscité, correspondant à l'ancêtre commun entre les grands singes et les groupe des catarrhiniens, est efficace contre le VIH-1 moderne. Les TRIM5a ressuscités plus récents, correspondant aux ancêtres des grands singes, sont plus efficaces contre d'autres rétrovirus. Ainsi, trouver un compromis dans la capacité de restreindre différents rétrovirus, l'homme aurait été exposé au VIH-1 à une période où TRIM5a manquait d'activité de restriction spécifique contre celui-ci. L'application de techniques d'évolution et de génétique comparative devraient être considérées pour l'évaluation systématique de protéines impliquées dans la pathogenèse virale, ainsi que pour guider des études biologiques et fonctionnelles

Biological control of soil-borne pathogens by fluorescent pseudomonads.

Particular bacterial strains in certain natural environments prevent infectious diseases of plant roots. How these bacteria achieve this protection from pathogenic fungi has been analysed in detail in biocontrol strains of fluorescent pseudomonads. During root colonization, these bacteria produce antifungal antibiotics, elicit induced systemic resistance in the host plant or interfere specifically with fungal pathogenicity factors. Before engaging in these activities, biocontrol bacteria go through several regulatory processes at the transcriptional and post-transcriptional levels.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

Evaluation of a multicomponent worksite health promotion program for cardiovascular risk factors-correcting for the regression towards the mean effect.

BACKGROUND: We assessed the impact of a multicomponent worksite health promotion program for0 reducing cardiovascular risk factors (CVRF) with short intervention, adjusting for regression towards the mean (RTM) affecting such nonexperimental study without control group. METHODS: A cohort of 4,198 workers (aged 42 +/- 10 years, range 16-76 years, 27% women) were analyzed at 3.7-year interval and stratified by each CVRF risk category (low/medium/high blood pressure [BP], total cholesterol [TC], body mass index [BMI], and smoking) with RTM and secular trend adjustments. Intervention consisted of 15 min CVRF screening and individualized counseling by health professionals to medium- and high-risk individuals, with eventual physician referral. RESULTS: High-risk groups participants improved diastolic BP (-3.4 mm Hg [95%CI: -5.1, -1.7]) in 190 hypertensive patients, TC (-0.58 mmol/l [-0.71, -0.44]) in 693 hypercholesterolemic patients, and smoking (-3.1 cig/day [-3.9, -2.3]) in 808 smokers, while systolic BP changes reflected RTM. Low-risk individuals without counseling deteriorated TC and BMI. Body weight increased uniformly in all risk groups (+0.35 kg/year). CONCLUSIONS: In real-world conditions, short intervention program participants in high-risk groups for diastolic BP, TC, and smoking improved their CVRF, whereas low-risk TC and BMI groups deteriorated. Future programs may include specific advises to low-risk groups to maintain a favorable CVRF profile.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.

Bayesian Networks for the Age Classification of Living Individuals: a Study on Transition Analysis

Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.

Probabilistic graphical models to deal with age estimation of living persons

Due to the rise of criminal, civil and administrative judicial situations involving people lacking valid identity documents, age estimation of living persons has become an important operational procedure for numerous forensic and medicolegal services worldwide. The chronological age of a given person is generally estimated from the observed degree of maturity of some selected physical attributes by means of statistical methods. However, their application in the forensic framework suffers from some conceptual and practical drawbacks, as recently claimed in the specialised literature. The aim of this paper is therefore to offer an alternative solution for overcoming these limits, by reiterating the utility of a probabilistic Bayesian approach for age estimation. This approach allows one to deal in a transparent way with the uncertainty surrounding the age estimation process and to produce all the relevant information in the form of posterior probability distribution about the chronological age of the person under investigation. Furthermore, this probability distribution can also be used for evaluating in a coherent way the possibility that the examined individual is younger or older than a given legal age threshold having a particular legal interest. The main novelty introduced by this work is the development of a probabilistic graphical model, i.e. a Bayesian network, for dealing with the problem at hand. The use of this kind of probabilistic tool can significantly facilitate the application of the proposed methodology: examples are presented based on data related to the ossification status of the medial clavicular epiphysis. The reliability and the advantages of this probabilistic tool are presented and discussed.

Les traces de pneumatiques en sciences forensiques : de l'analyse chimique à l'évaluation de la force probante

This work is focused on the development of a methodology for the use of chemical characteristic of tire traces to help answer the following question: "Is the offending tire at the origin of the trace found on the crime scene?". This methodology goes from the trace sampling on the road to statistical analysis of its chemical characteristics. Knowledge about the composition and manufacture of tread tires as well as a review of instrumental techniques used for the analysis of polymeric materials were studied to select, as an ansi vi cal technique for this research, pyrolysis coupled to a gas Chromatograph with a mass spectrometry detector (Py-GC/MS). An analytical method was developed and optimized to obtain the lowest variability between replicates of the same sample. Within-variability of the tread was evaluated regarding width and circumference with several samples taken from twelve tires of different brands and/or models. The variability within each of the treads (within-variability) and between the treads (between-variability) could be quantified. Different statistical methods have shown that within-variability is lower than between-variability, which helped differentiate these tires. Ten tire traces were produced with tires of different brands and/or models by braking tests. These traces have been adequately sampled using sheets of gelatine. Particles of each trace were analysed using the same methodology as for the tires at their origin. The general chemical profile of a trace or of a tire has been characterized by eighty-six compounds. Based on a statistical comparison of the chemical profiles obtained, it has been shown that a tire trace is not differentiable from the tire at its origin but is generally differentiable from tires that are not at its origin. Thereafter, a sample containing sixty tires was analysed to assess the discrimination potential of the developed methodology. The statistical results showed that most of the tires of different brands and models are differentiable. However, tires of the same brand and model with identical characteristics, such as country of manufacture, size and DOT number, are not differentiable. A model, based on a likelihood ratio approach, was chosen to evaluate the results of the comparisons between the chemical profiles of the traces and tires. The methodology developed was finally blindly tested using three simulated scenarios. Each scenario involved a trace of an unknown tire as well as two tires possibly at its origin. The correct results for the three scenarios were used to validate the developed methodology. The different steps of this work were useful to collect the required information to test and validate the underlying assumption that it is possible to help determine if an offending tire » or is not at the origin of a trace, by means of a statistical comparison of their chemical profile. This aid was formalized by a measure of the probative value of the evidence, which is represented by the chemical profile of the trace of the tire. - Ce travail s'est proposé de développer une méthodologie pour l'exploitation des caractéristiques chimiques des traces de pneumatiques dans le but d'aider à répondre à la question suivante : «Est-ce que le pneumatique incriminé est ou n'est pas à l'origine de la trace relevée sur les lieux ? ». Cette méthodologie s'est intéressée du prélèvement de la trace de pneumatique sur la chaussée à l'exploitation statistique de ses caractéristiques chimiques. L'acquisition de connaissances sur la composition et la fabrication de la bande de roulement des pneumatiques ainsi que la revue de techniques instrumentales utilisées pour l'analyse de matériaux polymériques ont permis de choisir, comme technique analytique pour la présente recherche, la pyrolyse couplée à un chromatographe en phase gazeuse avec un détecteur de spectrométrie de masse (Py-GC/MS). Une méthode analytique a été développée et optimisée afin d'obtenir la plus faible variabilité entre les réplicas d'un même échantillon. L'évaluation de l'intravariabilité de la bande de roulement a été entreprise dans sa largeur et sa circonférence à l'aide de plusieurs prélèvements effectués sur douze pneumatiques de marques et/ou modèles différents. La variabilité au sein de chacune des bandes de roulement (intravariabilité) ainsi qu'entre les bandes de roulement considérées (intervariabilité) a pu être quantifiée. Les différentes méthodes statistiques appliquées ont montré que l'intravariabilité est plus faible que l'intervariabilité, ce qui a permis de différencier ces pneumatiques. Dix traces de pneumatiques ont été produites à l'aide de pneumatiques de marques et/ou modèles différents en effectuant des tests de freinage. Ces traces ont pu être adéquatement prélevées à l'aide de feuilles de gélatine. Des particules de chaque trace ont été analysées selon la même méthodologie que pour les pneumatiques à leur origine. Le profil chimique général d'une trace de pneumatique ou d'un pneumatique a été caractérisé à l'aide de huitante-six composés. Sur la base de la comparaison statistique des profils chimiques obtenus, il a pu être montré qu'une trace de pneumatique n'est pas différenciable du pneumatique à son origine mais est, généralement, différenciable des pneumatiques qui ne sont pas à son origine. Par la suite, un échantillonnage comprenant soixante pneumatiques a été analysé afin d'évaluer le potentiel de discrimination de la méthodologie développée. Les méthodes statistiques appliquées ont mis en évidence que des pneumatiques de marques et modèles différents sont, majoritairement, différenciables entre eux. La méthodologie développée présente ainsi un bon potentiel de discrimination. Toutefois, des pneumatiques de la même marque et du même modèle qui présentent des caractéristiques PTD (i.e. pays de fabrication, taille et numéro DOT) identiques ne sont pas différenciables. Un modèle d'évaluation, basé sur une approche dite du likelihood ratio, a été adopté pour apporter une signification au résultat des comparaisons entre les profils chimiques des traces et des pneumatiques. La méthodologie mise en place a finalement été testée à l'aveugle à l'aide de la simulation de trois scénarios. Chaque scénario impliquait une trace de pneumatique inconnue et deux pneumatiques suspectés d'être à l'origine de cette trace. Les résultats corrects obtenus pour les trois scénarios ont permis de valider la méthodologie développée. Les différentes étapes de ce travail ont permis d'acquérir les informations nécessaires au test et à la validation de l'hypothèse fondamentale selon laquelle il est possible d'aider à déterminer si un pneumatique incriminé est ou n'est pas à l'origine d'une trace, par le biais d'une comparaison statistique de leur profil chimique. Cette aide a été formalisée par une mesure de la force probante de l'indice, qui est représenté par le profil chimique de la trace de pneumatique.

COLOX: a new blood-based test for colorectal cancer screening

INTRODUCTION/OBJECTIVES: Detection rates for adenoma and early colorectal cancer (CRC) are insufficient due to low compliance towards invasive screening procedures, like colonoscopy.Available non-invasive screening tests have unfortunately low sensitivity and specificity performances.Therefore, there is a large unmet need calling for a cost-effective, reliable and non-invasive test to screen for early neoplastic and pre-neoplastic lesions AIMS & Methods: The objective is to develop a screening test able to detect early CRCs and adenomas.This test is based on a nucleic acids multi-gene assay performed on peripheral blood mononuclear cells (PBMCs).A colonoscopy-controlled feasibility study was conducted on 179 subjects.The first 92 subjects was used as training set to generate a statistical significant signature.Colonoscopy revealed 21 subjects with CRC,30 with adenoma bigger than 1 cm and 41 with no neoplastic or inflammatory lesions.The second group of 48 subjects (controls, CRC and polyps) was used as a test set and will be kept blinded for the entire data analysis.To determine the organ and disease specificity 38 subjects were used:24 with inflammatory bowel disease (IBD),14 with other cancers than CRC (OC).Blood samples were taken from each patient the day of the colonoscopy and PBMCs were purified. Total RNA was extracted following standard procedures.Multiplex RT-qPCR was applied on 92 different candidate biomarkers.Different univariate and multivariate statistical methods were applied on these candidates and among them 60 biomarkers with significant p-values (<0.01) were selected.These biomarkers are involved in several different biological functions as cellular movement,cell signaling and interaction,tissue and cellular development,cancer and cell growth and proliferation.Two distinct biomarker signatures are used to separate patients without lesion from those with cancer or with adenoma, named COLOX CRC and COLOX POL respectively.COLOX performances were validated using random resampling method, bootstrap. RESULTS: COLOX CRC and POL tests successfully separate patients without lesions from those with CRC (Se 67%,Sp 93%,AUC 0.87) and from those with adenoma bigger than 1cm (Se 63%,Sp 83%,AUC 0.77),respectively. 6/24 patients in the IBD group and 1/14 patients in the OC group have a positive COLOX CRC CONCLUSION: The two COLOX tests demonstrated a high sensitivity and specificity to detect the presence of CRCs and adenomas bigger than 1 cm.A prospective, multicenter, pivotal study is underway in order to confirm these promising results in a larger cohort.