La prévention du tabagisme passif [Prevention of air pollution by indoor tobacco smoke in France]

A majority of smokers and non-smokers mind tobacco smoke. Passive smoking causes death by sudden infant death, lung cancer and coronary heart disease. 3000 to 6000 persons are killed every year in France. The lack of implementation of the Evin's law published in 1991 explains why non-smokers are not given the protection they can expect. The trend of scientific knowledge and of French and international public opinions support a growing demand for a complete protection of non-smokers with a total ban of smoking in all public or working places.

High-dose sequential chemotherapy (ICE) under circulating progenitor cells (CPC) protection in patients with small cell lung carcinoma (SCLC). Preliminary report on a multicentric study

Starting in February 1994, 20 patients (pt) with a median age of 50 years(range 41-63) from 7 European centers have been included. Completedata were obtained in 16 patients so far. CPC were mobilized with chemo(Epirubicine 75 mg/m2 /d, 01 + 02) followed by G-CSF 5 p.gfkg/d for14 days. HD chemo consisted in 3 sequential courses of ICE regimen(UOs. 10 g/m2 , Carbo. 1200 mg/m2 and Etop. 1200 mg/m2 ) underCPC protection and G-CSF 5 p.g/kg/d. Out of the 16 pt, 12 completedfull program (3 cycles). One pt died of septic shock before receivingany ICE course. One pt died during the first ICE of renal insufficiency.Two pt had only 2 courses because of toxicity. Among the 16 pt, responserate (RR) was: 7 CR, 6 PR, 1 PO; 3 pt are not evaluable dueto early withdrawal (overall RR: 13/16 = 81 %). Thirty-nine cycles ofHD chemo were given with a median hematological recovery of 9 days(range 7-12) until neutro. counts> 1.0 x 109 /1 and 9 days (range 717)until thrombo. > 20 x 109 /1. No cumulative, hematological toxicitywas seen. Accrual of patients is still ongoing and updated results will bepresented.

The TetR-type MfsR protein of the integrative and conjugative element (ICE) ICEclc controls both a putative efflux system and initiation of ICE transfer.

Integrative and conjugating elements (ICE) are self-transferable DNAs widely present in bacterial genomes, which often carry a variety of auxiliary genes of potential adaptive benefit. One of the model ICE is ICEclc, an element originally found in Pseudomonas knackmussii B13 and known for its propensity to provide its host with the capacity to metabolize chlorocatechols and 2-aminophenol. In this work, we studied the mechanism and target of regulation of MfsR, a TetR-type repressor previously found to exert global control on ICEclc horizontal transfer. By using a combination of ICEclc mutant and transcriptome analysis, gene reporter fusions, and DNA binding assays, we found that MfsR is a repressor of both its own expression and that of a gene cluster putatively coding for a major facilitator superfamily efflux system on ICEclc (named mfsABC). Phylogenetic analysis suggests that mfsR was originally located immediately adjacent to the efflux pump genes but became displaced from its original cis target DNA by a gene insertion. This resulted in divergence of the original bidirectional promoters into two separated individual regulatory units. Deletion of mfsABC did not result in a strong phenotype, and despite screening a large number of compounds and conditions, we were unable to define the precise current function or target of the putative efflux pump. Our data reconstruct how the separation of an ancestor mfsR-mfsABC system led to global control of ICEclc transfer by MfsR.

Neutron structure of type-III antifreeze protein allows the reconstruction of AFP-ice interface.

Antifreeze proteins (AFPs) inhibit ice growth at sub-zero temperatures. The prototypical type-III AFPs have been extensively studied, notably by X-ray crystallography, solid-state and solution NMR, and mutagenesis, leading to the identification of a compound ice-binding surface (IBS) composed of two adjacent ice-binding sections, each which binds to particular lattice planes of ice crystals, poisoning their growth. This surface, including many hydrophobic and some hydrophilic residues, has been extensively used to model the interaction of AFP with ice. Experimentally observed water molecules facing the IBS have been used in an attempt to validate these models. However, these trials have been hindered by the limited capability of X-ray crystallography to reliably identify all water molecules of the hydration layer. Due to the strong diffraction signal from both the oxygen and deuterium atoms, neutron diffraction provides a more effective way to determine the water molecule positions (as D(2) O). Here we report the successful structure determination at 293 K of fully perdeuterated type-III AFP by joint X-ray and neutron diffraction providing a very detailed description of the protein and its solvent structure. X-ray data were collected to a resolution of 1.05 Å, and neutron Laue data to a resolution of 1.85 Å with a "radically small" crystal volume of 0.13 mm(3). The identification of a tetrahedral water cluster in nuclear scattering density maps has allowed the reconstruction of the IBS-bound ice crystal primary prismatic face. Analysis of the interactions between the IBS and the bound ice crystal primary prismatic face indicates the role of the hydrophobic residues, which are found to bind inside the holes of the ice surface, thus explaining the specificity of AFPs for ice versus water.

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.

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.

Contribution of fine particulate matter sources to indoor exposure in bars, restaurants, and cafes

This study investigated the contribution of sources and establishment characteristics, on the exposure to fine particulate matter (PM(2.5)) in the non-smoking sections of bars, cafes, and restaurants in central Zurich. PM(2.5)-exposure was determined with a nephelometer. A random sample of hospitality establishments was investigated on all weekdays, from morning until midnight. Each visit lasted 30 min. Numbers of smokers and other sources, such as candles and cooking processes, were recorded, as were seats, open windows, and open doors. Ambient air pollution data were obtained from public authorities. Data were analysed using robust MM regression. Over 14 warm, sunny days, 102 establishments were measured. Average establishment PM(2.5) concentrations were 64.7 microg/m(3) (s.d. = 73.2 microg/m(3), 30-min maximum 452.2 microg/m(3)). PM(2.5) was significantly associated with the number of smokers, percentage of seats occupied by smokers, and outdoor PM. Each smoker increased PM(2.5) on average by 15 microg/m(3). No associations were found with other sources, open doors or open windows. Bars had more smoking guests and showed significantly higher concentrations than restaurants and cafes. Smokers were the most important PM(2.5)-source in hospitality establishments, while outdoor PM defined the baseline. Concentrations are expected to be even higher during colder, unpleasant times of the year. PRACTICAL IMPLICATIONS: Smokers and ambient air pollution are the most important sources of fine airborne particulate matter (PM(2.5)) in the non-smoking sections of bars, restaurants, and cafes. Other sources do not significantly contribute to PM(2.5)-levels, while opening doors and windows is not an efficient means of removing pollutants. First, this demonstrates the impact that even a few smokers can have in affecting particle levels. Second, it implies that creating non-smoking sections, and using natural ventilation, is not sufficient to bring PM(2.5) to levels that imply no harm for employees and non-smoking clients. [Authors]

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

The phylogeography of an alpine leaf beetle: divergence within Oreina elongata spans several ice ages.

The genetic landscape of the European flora and fauna was shaped by the ebb and flow of populations with the shifting ice during Quaternary climate cycles. While this has been well demonstrated for lowland species, less is known about high altitude taxa. Here we analyze the phylogeography of the leaf beetle Oreina elongata from 20 populations across the Alps and Apennines. Three mitochondrial and one nuclear region were sequenced in 64 individuals. Within an mtDNA phylogeny, three of seven subspecies are monophyletic. The species is chemically defended and aposematic, with green and blue forms showing geographic variation and unexpected within-population polymorphism. These warning colors show pronounced east-west geographical structure in distribution, but the phylogeography suggests repeated origin and loss. Basal clades come from the central Alps. Ancestors of other clades probably survived across northern Italy and the northern Adriatic, before separation of eastern, southern and western populations and rapid spread through the western Alps. After reviewing calibrated gene-specific substitution rates in the literature, we use partitioned Bayesian coalescent analysis to date our phylogeography. The major clades diverged long before the last glacial maximum, suggesting that O. elongata persisted many glacial cycles within or at the edges of the Alps and Apennines. When analyzing additional barcoding pairwise distances, we find strong evidence to consider O. elongata as a species complex rather than a single species.