Impact of a smoking ban in hospitality venues on second hand smoke exposure: a comparison of exposure assessment methods

BACKGROUND: In May 2010, Switzerland introduced a heterogeneous smoking ban in the hospitality sector. While the law leaves room for exceptions in some cantons, it is comprehensive in others. This longitudinal study uses different measurement methods to examine airborne nicotine levels in hospitality venues and the level of personal exposure of non-smoking hospitality workers before and after implementation of the law. METHODS: Personal exposure to second hand smoke (SHS) was measured by three different methods. We compared a passive sampler called MoNIC (Monitor of NICotine) badge, to salivary cotinine and nicotine concentration as well as questionnaire data. Badges allowed the number of passively smoked cigarettes to be estimated. They were placed at the venues as well as distributed to the participants for personal measurements. To assess personal exposure at work, a time-weighted average of the workplace badge measurements was calculated. RESULTS: Prior to the ban, smoke-exposed hospitality venues yielded a mean badge value of 4.48 (95%-CI: 3.7 to 5.25; n = 214) cigarette equivalents/day. At follow-up, measurements in venues that had implemented a smoking ban significantly declined to an average of 0.31 (0.17 to 0.45; n = 37) (p = 0.001). Personal badge measurements also significantly decreased from an average of 2.18 (1.31-3.05 n = 53) to 0.25 (0.13-0.36; n = 41) (p = 0.001). Spearman rank correlations between badge exposure measures and salivary measures were small to moderate (0.3 at maximum). CONCLUSIONS: Nicotine levels significantly decreased in all types of hospitality venues after implementation of the smoking ban. In-depth analyses demonstrated that a time-weighted average of the workplace badge measurements represented typical personal SHS exposure at work more reliably than personal exposure measures such as salivary cotinine and nicotine.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Therapeutic drug monitoring of seven psychotropic drugs and four metabolites in human plasma by HPLC-MS.

A simple and sensitive LC-MS method was developed and validated for the simultaneous quantification of aripiprazole (ARI), atomoxetine (ATO), duloxetine (DUL), clozapine (CLO), olanzapine (OLA), sertindole (STN), venlafaxine (VEN) and their active metabolites dehydroaripiprazole (DARI), norclozapine (NCLO), dehydrosertindole (DSTN) and O-desmethylvenlafaxine (OVEN) in human plasma. The above mentioned compounds and the internal standard (remoxipride) were extracted from 0.5 mL plasma by solid-phase extraction (mix mode support). The analytical separation was carried out on a reverse phase liquid chromatography at basic pH (pH 8.1) in gradient mode. All analytes were monitored by MS detection in the single ion monitoring mode and the method was validated covering the corresponding therapeutic range: 2-200 ng/mL for DUL, OLA, and STN, 4-200 ng/mL for DSTN, 5-1000 ng/mL for ARI, DARI and finally 2-1000 ng/mL for ATO, CLO, NCLO, VEN, OVEN. For all investigated compounds, good performance in terms of recoveries, selectivity, stability, repeatability, intermediate precision, trueness and accuracy, was obtained. Real patient plasma samples were then successfully analysed.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Combining protein identification and quantification: C-terminal isotope-coded tagging using sulfanilic acid.

Two methods of differential isotopic coding of carboxylic groups have been developed to date. The first approach uses d0- or d3-methanol to convert carboxyl groups into the corresponding methyl esters. The second relies on the incorporation of two 18O atoms into the C-terminal carboxylic group during tryptic digestion of proteins in H(2)18O. However, both methods have limitations such as chromatographic separation of 1H and 2H derivatives or overlap of isotopic distributions of light and heavy forms due to small mass shifts. Here we present a new tagging approach based on the specific incorporation of sulfanilic acid into carboxylic groups. The reagent was synthesized in a heavy form (13C phenyl ring), showing no chromatographic shift and an optimal isotopic separation with a 6 Da mass shift. Moreover, sulfanilic acid allows for simplified fragmentation in matrix-assisted laser desorption/ionization (MALDI) due the charge fixation of the sulfonate group at the C-terminus of the peptide. The derivatization is simple, specific and minimizes the number of sample treatment steps that can strongly alter the sample composition. The quantification is reproducible within an order of magnitude and can be analyzed either by electrospray ionization (ESI) or MALDI. Finally, the method is able to specifically identify the C-terminal peptide of a protein by using GluC as the proteolytic enzyme.

Les méthodes d'études de cas en psychothérapie: perspectives actuelles [Methods of psychotherapy case studies: current perspectives].

There is a renewal of interest among psychotherapy researchers and psychotherapists towards psychotherapy case studies. This article presents two paradigms that have greatly influenced this increasing interest in psychotherapy case studies : the pragmatic case study and the theory-building case study paradigm. The origins, developments and key-concepts of both paradigms are presented, as well as their methodological and ethical specificities. Examples of case studies, along with models developed, are cited. The differential influence of the post-modern schools on both paradigms are presented, as well as their contribution to the field of methods of psychotherapy case studies discussed and assessed in terms of relevance for the researcher and the psychotherapist.

Counterfactuals and causal inference: Methods and principles for social research

"Most quantitative empirical analyses are motivated by the desire to estimate the causal effect of an independent variable on a dependent variable. Although the randomized experiment is the most powerful design for this task, in most social science research done outside of psychology, experimental designs are infeasible. (Winship & Morgan, 1999, p. 659)." This quote from earlier work by Winship and Morgan, which was instrumental in setting the groundwork for their book, captures the essence of our review of Morgan and Winship's book: It is about causality in nonexperimental settings.

Development of a two-step screening ESI-TOF-MS method for rapid determination of significant stress-induced metabolome modifications in plant leaf extracts: the wound response in Arabidopsis thaliana as a case study.

To study the stress-induced effects caused by wounding under a new perspective, a metabolomic strategy based on HPLC-MS has been devised for the model plant Arabidopsis thaliana. To detect induced metabolites and precisely localise these compounds among the numerous constitutive metabolites, HPLC-MS analyses were performed in a two-step strategy. In a first step, rapid direct TOF-MS measurements of the crude leaf extract were performed with a ballistic gradient on a short LC-column. The HPLC-MS data were investigated by multivariate analysis as total mass spectra (TMS). Principal components analysis (PCA) and hierarchical cluster analysis (HCA) on principal coordinates were combined for data treatment. PCA and HCA demonstrated a clear clustering of plant specimens selecting the highest discriminating ions given by the complete data analysis, leading to the specific detection of discrete-induced ions (m/z values). Furthermore, pool constitution with plants of homogeneous behaviour was achieved for confirmatory analysis. In this second step, long high-resolution LC profilings on an UPLC-TOF-MS system were used on pooled samples. This allowed to precisely localise the putative biological marker induced by wounding and by specific extraction of accurate m/z values detected in the screening procedure with the TMS spectra.

Identification of GPx8 as a novel cellular substrate of the hepatitis C virus NS3-4A protease

Background: The hepatitis C virus (HCV) NS3-4A protease is not only an essential component of the viral replication complex and a prime target for a ntiviral intervention but also a key player i n the persistence and pathogenesis of HCV. It cleaves and thereby inactivates two crucial adaptor proteins in viral RNA sensing and innate immunity (MAVS and TRIF) as well as a phosphatase involved in growth factor signaling (TCPTP). T he aim of this study was to identify novel cellular substrates o f the N S3-4A protease and to investigate their role in the replication and pathogenesis of HCV. Methods: Cell lines inducibly expressing t he NS3-4A protease were analyzed in basal as well as interferon-α-stimulated states by stable isotopic l abeling using amino acids in cell culture (SILAC) coupled with protein separation and mass spectrometry. Candidates fulfilling stringent criteria for potential substrates or products of the NS3-4A protease were further i nvestigated in different experimental systems as well a s in liver biopsies from patients with chronic hepatitis C. Results: SILAC coupled with protein separation and mass spectrometry yielded > 5000 proteins of which 18 candidates were selected for further analyses. These allowed us to identify GPx8, a membrane-associated peroxidase involved in disulfide bond formation in the endoplasmic reticulum, as a n ovel cellular substrate of the H CV NS3-4A protease. Cleavage occurs at cysteine in position 11, removing the cytosolic tip of GPx8, and was observed in different experimental systems as well as in liver biopsies from patients with chronic hepatitis C. Further functional studies, involving overexpression and RNA silencing, revealed that GPx8 is a p roviral factor involved in viral particle production but not in HCV entry or HCV RNA replication. Conclusions: GPx8 is a proviral host factor cleaved by the HCV NS3-4A protease. Studies investigating the consequences of GPx8 cleavage for protein function are underway. The identification of novel cellular substrates o f the HCV N S3-4A protease should yield new insights i nto the HCV life cycle and the pathogenesis of hepatitis C and may reveal novel targets for antiviral intervention.