What Was I Thinking? Eye-Tracking Experiments Underscore the Bias that Architecture Exerts on Nuclear Grading in Prostate Cancer.

We previously reported that nuclear grade assignment of prostate carcinomas is subject to a cognitive bias induced by the tumor architecture. Here, we asked whether this bias is mediated by the non-conscious selection of nuclei that "match the expectation" induced by the inadvertent glance at the tumor architecture. 20 pathologists were asked to grade nuclei in high power fields of 20 prostate carcinomas displayed on a computer screen. Unknown to the pathologists, each carcinoma was shown twice, once before a background of a low grade, tubule-rich carcinoma and once before the background of a high grade, solid carcinoma. Eye tracking allowed to identify which nuclei the pathologists fixated during the 8 second projection period. For all 20 pathologists, nuclear grade assignment was significantly biased by tumor architecture. Pathologists tended to fixate on bigger, darker, and more irregular nuclei when those were projected before kigh grade, solid carcinomas than before low grade, tubule-rich carcinomas (and vice versa). However, the morphometric differences of the selected nuclei accounted for only 11% of the architecture-induced bias, suggesting that it can only to a small part be explained by the unconscious fixation on nuclei that "match the expectation". In conclusion, selection of « matching nuclei » represents an unconscious effort to vindicate the gravitation of nuclear grades towards the tumor architecture.

Parameters for successful monthly extended dosing of darbepoetin-alpha in patients undergoing hemodialysis.

AIM: To document the feasibility and report the results of dosing darbepoetin-alpha at extended intervals up to once monthly (QM) in a large dialysis patient population. MATERIAL: 175 adult patients treated, at 23 Swiss hemodialysis centres, with stable doses of any erythropoiesis-stimulating agent who were switched by their physicians to darbepoetin-alpha treatment at prolonged dosing intervals (every 2 weeks [Q2W] or QM). METHOD: Multicentre, prospective, observational study. Patients' hemoglobin (Hb) levels and other data were recorded 1 month before conversion (baseline) to an extended darbepoetin-alpha dosing interval, at the time of conversion, and once monthly thereafter up to the evaluation point (maximum of 12 months or until loss to follow-up). RESULTS: Data for 161 evaluable patients from 23 sites were included in the final analysis. At 1 month prior to conversion, 73% of these patients were receiving darbepoetin-alpha weekly (QW) and 27% of the patients biweekly (Q2W). After a mean follow-up of 9.5 months, 34% received a monthly (QM) dosing regimen, 52% of the patients were receiving darbepoetin-alpha Q2W, and 14% QW. The mean (SD) Hb concentration at baseline was 12.3 +/- 1.2 g/dl, compared to 11.9 +/- 1.2 g/dl at the evaluation point. The corresponding mean weekly darbepoetin-alpha dose was 44.3 +/- 33.4 microg at baseline and 37.7 +/- 30.8 microg at the evaluation point. CONCLUSIONS: Conversion to extended darbepoetin-alpha dosing intervals of up to QM, with maintenance of initial Hb concentrations, was successful for the majority of stable dialysis patients.

Adaptive tracking of EEG oscillations.

Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.

Direct three-dimensional myocardial strain tensor quantification and tracking using zHARP.

Images of myocardial strain can be used to diagnose heart disease, plan and monitor treatment, and to learn about cardiac structure and function. Three-dimensional (3D) strain is typically quantified using many magnetic resonance (MR) images obtained in two or three orthogonal planes. Problems with this approach include long scan times, image misregistration, and through-plane motion. This article presents a novel method for calculating cardiac 3D strain using a stack of two or more images acquired in only one orientation. The zHARP pulse sequence encodes in-plane motion using MR tagging and out-of-plane motion using phase encoding, and has been previously shown to be capable of computing 3D displacement within a single image plane. Here, data from two adjacent image planes are combined to yield a 3D strain tensor at each pixel; stacks of zHARP images can be used to derive stacked arrays of 3D strain tensors without imaging multiple orientations and without numerical interpolation. The performance and accuracy of the method is demonstrated in vitro on a phantom and in vivo in four healthy adult human subjects.

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms.

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Monte Carlo simulation of a clearance box monitor used for nuclear power plant decommissioning.

When decommissioning a nuclear facility it is important to be able to estimate activity levels of potentially radioactive samples and compare with clearance values defined by regulatory authorities. This paper presents a method of calibrating a clearance box monitor based on practical experimental measurements and Monte Carlo simulations. Adjusting the simulation for experimental data obtained using a simple point source permits the computation of absolute calibration factors for more complex geometries with an accuracy of a bit more than 20%. The uncertainty of the calibration factor can be improved to about 10% when the simulation is used relatively, in direct comparison with a measurement performed in the same geometry but with another nuclide. The simulation can also be used to validate the experimental calibration procedure when the sample is supposed to be homogeneous but the calibration factor is derived from a plate phantom. For more realistic geometries, like a small gravel dumpster, Monte Carlo simulation shows that the calibration factor obtained with a larger homogeneous phantom is correct within about 20%, if sample density is taken as the influencing parameter. Finally, simulation can be used to estimate the effect of a contamination hotspot. The research supporting this paper shows that activity could be largely underestimated in the event of a centrally-located hotspot and overestimated for a peripherally-located hotspot if the sample is assumed to be homogeneously contaminated. This demonstrates the usefulness of being able to complement experimental methods with Monte Carlo simulations in order to estimate calibration factors that cannot be directly measured because of a lack of available material or specific geometries.

The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in GC

Positive selection is widely estimated from protein coding sequence alignments by the nonsynonymous-to-synonymous ratio omega. Increasingly elaborate codon models are used in a likelihood framework for this estimation. Although there is widespread concern about the robustness of the estimation of the omega ratio, more efforts are needed to estimate this robustness, especially in the context of complex models. Here, we focused on the branch-site codon model. We investigated its robustness on a large set of simulated data. First, we investigated the impact of sequence divergence. We found evidence of underestimation of the synonymous substitution rate for values as small as 0.5, with a slight increase in false positives for the branch-site test. When dS increases further, underestimation of dS is worse, but false positives decrease. Interestingly, the detection of true positives follows a similar distribution, with a maximum for intermediary values of dS. Thus, high dS is more of a concern for a loss of power (false negatives) than for false positives of the test. Second, we investigated the impact of GC content. We showed that there is no significant difference of false positives between high GC (up to similar to 80%) and low GC (similar to 30%) genes. Moreover, neither shifts of GC content on a specific branch nor major shifts in GC along the gene sequence generate many false positives. Our results confirm that the branch-site is a very conservative test.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Eye-tracking of nodule detection in lung CT volumetric data.

PURPOSE: Signal detection on 3D medical images depends on many factors, such as foveal and peripheral vision, the type of signal, and background complexity, and the speed at which the frames are displayed. In this paper, the authors focus on the speed with which radiologists and naïve observers search through medical images. Prior to the study, the authors asked the radiologists to estimate the speed at which they scrolled through CT sets. They gave a subjective estimate of 5 frames per second (fps). The aim of this paper is to measure and analyze the speed with which humans scroll through image stacks, showing a method to visually display the behavior of observers as the search is made as well as measuring the accuracy of the decisions. This information will be useful in the development of model observers, mathematical algorithms that can be used to evaluate diagnostic imaging systems. METHODS: The authors performed a series of 3D 4-alternative forced-choice lung nodule detection tasks on volumetric stacks of chest CT images iteratively reconstructed in lung algorithm. The strategy used by three radiologists and three naïve observers was assessed using an eye-tracker in order to establish where their gaze was fixed during the experiment and to verify that when a decision was made, a correct answer was not due only to chance. In a first set of experiments, the observers were restricted to read the images at three fixed speeds of image scrolling and were allowed to see each alternative once. In the second set of experiments, the subjects were allowed to scroll through the image stacks at will with no time or gaze limits. In both static-speed and free-scrolling conditions, the four image stacks were displayed simultaneously. All trials were shown at two different image contrasts. RESULTS: The authors were able to determine a histogram of scrolling speeds in frames per second. The scrolling speed of the naïve observers and the radiologists at the moment the signal was detected was measured at 25-30 fps. For the task chosen, the performance of the observers was not affected by the contrast or experience of the observer. However, the naïve observers exhibited a different pattern of scrolling than the radiologists, which included a tendency toward higher number of direction changes and number of slices viewed. CONCLUSIONS: The authors have determined a distribution of speeds for volumetric detection tasks. The speed at detection was higher than that subjectively estimated by the radiologists before the experiment. The speed information that was measured will be useful in the development of 3D model observers, especially anthropomorphic model observers which try to mimic human behavior.

Tracking the ghost of coevolution in specific plant-insect interactions : the case of the pollination system between the globeflower Trollius europaeus and seven european Chiastocheta flies

This study aims at understanding the evolutionary processes at work in specialized species interactions. Prom the macroevolutionary perspective, coevolution among specialized taxa was proposed to be one of the major processes generating biodiversity. We challenge this idea from the theoretical and practical perspective and through a literature review and show that the major hypotheses linking coevolutionary process with macroevolutionary patterns do not necessarily predict lineage co diversification and parallel speciation, limit¬ing the utility of the comparative phylogenenetic approach for investigating coevolution¬ary processes. We also point to the rarity of observed long-term coevolutionary dynamics among lineages and propose that coevolution rather occurs in shorter timescales, followed by ecological fitting. Prom the empirical point, we focus on the nursery pollination interaction between the European globeflower Trollius europaeus (Ranunculaceae) and its associated Chiastocheta flies (Anthomyiidae; Diptera) as a model system of evolution and maintenance of special¬ized interactions. The flies are obligate parasites of the seeds, but also pollinate the plant - it was thus proposed that both species are mutually dependent. Contrasting with the paradigm used for two decades of research on this system, we show that the female fitness component of the plant is similar in the populations with and without Chiastocheta. The plant is thus not exclusively dependent on the flies for reproduction. We discuss this result in the context of the factors responsible for the evolution of mutualistic systems. Understanding the evolution of a biological system requires understanding of its phylo- genetic context. Previous studies showed large mismatch between mtDNA phylogeny and morphological taxonomy in Chiastocheta. By using a large set of RAD-sequencing loci, we delineate the species limits that are congruent with morphology, and show that the discordance is best explained by the scenario of mitochondrial capture among fly species. Finally, we examine this system from a phylogeographic perspective, and identify the lack of congruence in spatial genetic structures of the plant and associated insects across their whole geographic range. The flies show lower numbers of spatial genetic groups than the plant, indicating that not all of the plant réfugia were shared by all the fly species or that the migration dynamics homogenized some of the groups. The incongruence in spatial genetic patterns indicates that fly migrations were largely independent from the genetic background of the plant, following rather a scenario of resource tracking, without the signature of coevolutionary process at this scale. Indeed, while the flies require the plant to survive climatic oscillations, the opposite is not true. Eventually, we show that there is no phylogenetic signal of spatial genetic structures, meaning that neither histories nor life- history traits are shared among closely related species and that species are characterized by unique trajectories of their genes. -- Cette étude vise à comprendre les processus évolutifs à l'oeuvre au sein d'interactions en¬tre espèces spécialisées. Du point de vue macroévolutif, la coévolution entre les taxons spécialisée a été considérée comme l'un des principaux processus générateur de biodiversité. Nous contestons cette idée du point de vue théorique et pratique à travers une revue de la littérature. Nous montrons que les hypothèses majeures reliant les processus coévolutifs avec les patterns de diversité au niveau macroévolutif ne prédisent pas nécessairement la co- diversification des lignées et leur spéciation parallèle, ce qui limite l'utilité de l'approche de phylogénie comparative pour étudier les processus coévolutifs . Nous rappelons également le peu d'exemples de dynamique coévolutive à long terme et proposons que la coévolution se produit plutôt dans des intervalles courts, suivis d'ajustements écologiques. Du point empirique, nous nous concentrons sur l'interaction de pollinisation entre le Trolle d'Europe Trollius europaeus (Ranunculaceae) et ses pollinisateurs associés, du genre Chiastocheta (Anthomyiidae; Diptera) en tant que système-modèle pour étudier l'évolution et le maintien des interactions spécialisées. Les mouches sont des parasites obligatoires des semences, mais pollinisent également la plante. Il a donc été proposé que les deux espèces soient mutuellement dépendantes. Contrastant avec le paradigme utilisé pendant deux décennies de recherche sur ce système, nous montrons, que la composante de fitness femelle de la plante est similaire dans les populations avec et sans Chiastocheta. La plante ne dépend donc pas exclusivement de son interaction avec les mouches pour la reproduction. Nous discutons de ce résultat dans le contexte des facteurs responsables de l'évolution des systèmes mutualistes. Comprendre l'évolution d'un système biologique nécessite la compréhension de son con- texte phylogénétique. Des études antérieures ont montré, chez Chiastocheta, de grandes disparités entre les phylogénies obtenues à partir d'ADN mitochondrial et la taxonomie basée sur les critères morphologiques. En utilisant un grand nombre de loci obtenus par RAD-sequencing, nous traçons les limites des espèces, qui concordent avec les car¬actéristiques morphologies, et montrons que la discordance s'explique en fait par un scénario de capture mitochondriale entre espèces de mouches. Enfin, nous examinons le système d'un point de vue phylogéographique, et identi¬fions les incohérences entre structurations génétiques spatiales de la plante et des insectes associés dans toute leur aire de distribution géographique. Les mouches présentent un nombre de groupes génétiques inférieur à la plante, indiquant que tous les refuges de la plante n'étaient pas partagés par toutes les espèces de mouches ou que les dynamiques migratoires ont homogénéisés certains des groupes chez les mouches. Les différences ob¬servées dans les patrons de structuration génétique spatiale indique que les migrations et dispersions des mouches ont été indépendantes du contexte génétique de la plante, et ces dernières ont été uniquement tributaires de la disponibilité des ressources, sans qu'il n'y ait de signature du processus de coévolution à cette échelle. En effet, tandis que les mouches ont besoin de la plante pour survivre aux oscillations climatiques, le contraire n'est pas exact. Finalement, nous montrons qu'il n'y a pas de signal phylogénétique des structurations génétiques spatiales chez les mouches, ce qui signifie que ni l'histoire, ni les traits d'histoire de vie ne sont partagés entre les espèces phylogénétiquement proches et que les espèces sont caractérisées par des trajectoires uniques de leurs gènes.

Rockfall detection from LIDAR point clouds: A clustering approach using R

The analysis of rockfall characteristics and spatial distribution is fundamental to understand and model the main factors that predispose to failure. In our study we analysed LiDAR point clouds aiming to: (1) detect and characterise single rockfalls; (2) investigate their spatial distribution. To this end, different cluster algorithms were applied: 1a) Nearest Neighbour Clutter Removal (NNCR) in combination with the Expectation?Maximization (EM) in order to separate feature points from clutter; 1b) a density based algorithm (DBSCAN) was applied to isolate the single clusters (i.e. the rockfall events); 2) finally we computed the Ripley's K-function to investigate the global spatial pattern of the extracted rockfalls. The method allowed proper identification and characterization of more than 600 rockfalls occurred on a cliff located in Puigcercos (Catalonia, Spain) during a time span of six months. The spatial distribution of these events proved that rockfall were clustered distributed at a welldefined distance-range. Computations were carried out using R free software for statistical computing and graphics. The understanding of the spatial distribution of precursory rockfalls may shed light on the forecasting of future failures.

Tabacologie: mise au point 2015 [Smoking cessation: update 2015].

La prévalence mondiale du tabagisme est environ cinq fois plus importante chez les hommes que chez les femmes, toutefois cet écart tend à s'égaliser. En ce qui concerne les conséquences sur la santé du tabagisme, les femmes semblent plus susceptibles que les hommes. Elles sont notamment plus à risque de présenter certains cancers pulmonaires ou de décéder de maladies cardiovasculaires. Si les hommes sont moins enclins à demander de l'aide pour arrêter de fumer, les femmes quant à elles ont moins de succès dans leurs tentatives d'arrêt et les traitements semblent moins efficaces chez ces dernières. Des interventions d'aide à l'arrêt et des mesures de prévention du tabagisme adaptées aux spécificités de genre ont le potentiel d'améliorer la prise en charge des fumeurs et de diminuer les disparités de genre en santé. Smoking remains a major public health problem in Switzerland and is responsible for about 9000 deaths annually. In 2013, a quarter of the Swiss population (15 and over) were smokers and more than half of them wanted to quit smoking. This article provides an update of Swiss clinical practice guidelines published in 2011 and covers several new features, including views regarding smoking reduction, gradual quitting, use of nicotine replacement therapy for a short period prior to quitting, nicotine mouth spray marketing and the reimbursement of varenicline and bupropion treatments (under certain conditions) by basic health insurance. An algorithm summarizes the different stages of management of patients who smoke.