Assimilation des expériences problématiques: une étude de cas de psychothérapie psychodynamique limitée dans le temps [Assimilation of problematic experiences: a case study on short-term dynamic psychotherapy].

The assimilation model is a qualitative and integrative approach that enables to study change processes that occur in psychotherapy. According to Stiles, this model conceives the individual's personality as constituent of different voices; the concept of voice is used to describe traces left by past experiences. During the psychotherapy, we can observe the progressive integration of the problematic voices into the patient's personality. We applied the assimilation model to a 34-session-long case of an effective short-term dynamic psychotherapy. We've chosen eight sessions we transcribed and analyzed by establishing points of contact between the case and the theory. The results are presented and discussed in terms of the evolution of the main voices in the patient.

Harvesting and transport of root biomass from fast-growing poplar plantations

Abstract

Short-term stability of testosterone and epitestosterone conjugates in urine samples: quantification by liquid chromatography-linear ion trap mass spectrometry.

A simple method using liquid chromatography-linear ion trap mass spectrometry for simultaneous determination of testosterone glucuronide (TG), testosterone sulfate (TS), epitestosterone glucuronide (EG) and epitestosterone sulfate (ES) in urine samples was developed. For validation purposes, a urine containing no detectable amount of TG, TS and EG was selected and fortified with steroid conjugate standards. Quantification was performed using deuterated testosterone conjugates to correct for ion suppression/enhancement during ESI. Assay validation was performed in terms of lower limit of detection (1-3ng/mL), recovery (89-101%), intraday precision (2.0-6.8%), interday precision (3.4-9.6%) and accuracy (101-103%). Application of the method to short-term stability testing of urine samples at temperature ranging from 4 to 37 degrees C during a time-storage of a week lead to the conclusion that addition of sodium azide (10mg/mL) is required for preservation of the analytes.

Rivascolarizzazione miocardica con arteria radiale: tecniche chirurgiche a confronto e risultati a breve termine [Myocardial revascularization and radial artery: different surgical strategies and short-term results]

BACKGROUND: The radial artery is routinely used as a graft for surgical arterial myocardial revascularization. The proximal radial artery anastomosis site remains unknown. In this study, we analyzed the short-term results and the operative risk determinants after having used four different common techniques for radial artery implantation. METHODS: From January 2000 to December 2004, 571 patients underwent coronary artery bypass grafting with radial arteries. Data were analyzed for the entire population and for subgroups following the proximal radial artery anastomosis site: 140 T-graft with the mammary artery (group A), 316 free-grafts with the proximal anastomosis to the ascending aorta (group B), 55 mammary arteries in situ elongated with the radial artery (group C) and 60 radial arteries elongated with a piece of mammary artery and anastomosed to the ascending aorta (group D). RESULTS: The mean age was 53.8 +/- 7.7 years; 55.5% of patients had a previous myocardial infarction and 73% presented with a satisfactory left ventricular function. A complete arterial myocardial revascularization was achieved in 532 cases (93.2%) and 90.2% of the procedures were performed under cardiopulmonary bypass and cardioplegic arrest. The operative mortality rate was 0.9%, a postoperative myocardial infarction was diagnosed in 19 patients (3.3%), an intra-aortic balloon pump was used in 10 patients (1.7%) and a mechanical circulatory device was implanted in 2 patients. The radial artery harvesting site remained always free from complications. The proximal radial artery anastomosis site was not a determinant of early hospital mortality. Group C showed a higher risk of postoperative myocardial infarction (p = 0.09), together with female gender (p = 0.003), hypertension (p = 0.059) and a longer cardiopulmonary bypass time. CONCLUSIONS: The radial artery and the mammary artery can guarantee multiple arterial revascularization also for patients with contraindications to double mammary artery use. The four most common techniques for proximal radial artery anastomosis are not related to a higher operative risk and they can be used alternatively to reach the best surgical results

The M-Coffee web server: a meta-method for computing multiple sequence alignments by combining alternative alignment methods.

The M-Coffee server is a web server that makes it possible to compute multiple sequence alignments (MSAs) by running several MSA methods and combining their output into one single model. This allows the user to simultaneously run all his methods of choice without having to arbitrarily choose one of them. The MSA is delivered along with a local estimation of its consistency with the individual MSAs it was derived from. The computation of the consensus multiple alignment is carried out using a special mode of the T-Coffee package [Notredame, Higgins and Heringa (T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 2000; 302: 205-217); Wallace, O'Sullivan, Higgins and Notredame (M-Coffee: combining multiple sequence alignment methods with T-Coffee. Nucleic Acids Res. 2006; 34: 1692-1699)] Given a set of sequences (DNA or proteins) in FASTA format, M-Coffee delivers a multiple alignment in the most common formats. M-Coffee is a freeware open source package distributed under a GPL license and it is available either as a standalone package or as a web service from www.tcoffee.org.

Comparing Random-based and k-Anonymity-Based Algorithms for Graph Anonymization

Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on random-ization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in orderto obtain an anonymized graph with a desired k-anonymity value. We want to analyze the complexity of these methods to generate anonymized graphs and the quality of the resulting graphs.

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.

Prolonged but not short negative energy condition restored corticoadrenal leptin sensitivity in the hypothalamic obese rat.

BACKGROUND/AIM: We have reported that neonatal treatment with monosodium L-glutamate (MSG), which causes damage to the arcuate nucleus, leads to severe hyperleptinemia and reduced adrenal leptin receptor (ob-Rb) expression in adulthood. As a result, rats given MSG neonatally display corticoadrenal leptin-resistance, a defect that is overridden by normalization of corticoadrenal hyperfunction. The aim of the present study was to determine whether negative energy conditions could correct corticoadrenal cell dysfunction in rats given MSG neonatally. METHODS: Normal (CTR) and MSG-treated female rats were subjected to food removal for 1-5 days, or prolonged (24-61 days) food restriction (FR). Plasma levels of several biomarkers and in vitro corticoadrenal function were evaluated following starvation or FR. RESULTS: Fasting for 1-5 days reduced plasma leptin levels in CTR and MSG rats, compared to levels in the respective groups fed ad libitum(p < 0.05), but adrenal leptin-resistance was unchanged. With prolonged FR, isolated adrenal cells from MSG rats became sensitive to leptin, which lowered ACTH-induced glucocorticoid release. This restoration of leptin response was associated with normalization of adrenal ob-Rb gene expression. CONCLUSION: Dietary restriction in some leptin-resistant obese phenotypes may normalize adrenocortical function.

Effect of short-term creatine supplementation on renal responses in men.

There is an increasing utilisation of oral creatine (Cr) supplementation among athletes who hope to enhance their performance but it is not known if this ingestion has any detrimental effect on the kidney. Five healthy men ingested either a placebo or 20 g of creatine monohydrate per day for 5 consecutive days. Blood samples and urine collections were analysed for Cr and creatinine (Crn) determination after each experimental session. Total protein and albumin urine excretion rates were also determined. Oral Cr supplementation had a significant incremental impact on arterial content (3.7 fold) and urine excretion rate (90 fold) of this compound. In contrast, arterial and urine Crn values were not affected by the Cr ingestion. The glomerular filtration rate (Crn clearance) and the total protein and albumin excretion rates remained within the normal range. In conclusion, this investigation showed that short-term oral Cr supplementation does not appear to have any detrimental effect on the renal responses of healthy men.

Study of OSEM with different subsets in grating-based X-ray differential phase-contrast imaging.

Impressive developments in X-ray imaging are associated with X-ray phase contrast computed tomography based on grating interferometry, a technique that provides increased contrast compared with conventional absorption-based imaging. A new "single-step" method capable of separating phase information from other contributions has been recently proposed. This approach not only simplifies data-acquisition procedures, but, compared with the existing phase step approach, significantly reduces the dose delivered to a sample. However, the image reconstruction procedure is more demanding than for traditional methods and new algorithms have to be developed to take advantage of the "single-step" method. In the work discussed in this paper, a fast iterative image reconstruction method named OSEM (ordered subsets expectation maximization) was applied to experimental data to evaluate its performance and range of applicability. The OSEM algorithm with different subsets was also characterized by comparison of reconstruction image quality and convergence speed. Computer simulations and experimental results confirm the reliability of this new algorithm for phase-contrast computed tomography applications. Compared with the traditional filtered back projection algorithm, in particular in the presence of a noisy acquisition, it furnishes better images at a higher spatial resolution and with lower noise. We emphasize that the method is highly compatible with future X-ray phase contrast imaging clinical applications.

Does short-term virologic failure translate to clinical events in antiretroviral-naïve patients initiating antiretroviral therapy in clinical practice?

OBJECTIVE: To determine whether differences in short-term virologic failure among commonly used antiretroviral therapy (ART) regimens translate to differences in clinical events in antiretroviral-naïve patients initiating ART. DESIGN: Observational cohort study of patients initiating ART between January 2000 and December 2005. SETTING: The Antiretroviral Therapy Cohort Collaboration (ART-CC) is a collaboration of 15 HIV cohort studies from Canada, Europe, and the United States. STUDY PARTICIPANTS: A total of 13 546 antiretroviral-naïve HIV-positive patients initiating ART with efavirenz, nevirapine, lopinavir/ritonavir, nelfinavir, or abacavir as third drugs in combination with a zidovudine and lamivudine nucleoside reverse transcriptase inhibitor backbone. MAIN OUTCOME MEASURES: Short-term (24-week) virologic failure (>500 copies/ml) and clinical events within 2 years of ART initiation (incident AIDS-defining event, death, and a composite measure of these two outcomes). RESULTS: Compared with efavirenz as initial third drug, short-term virologic failure was more common with all other third drugs evaluated; nevirapine (adjusted odds ratio = 1.87, 95% confidence interval (CI) = 1.58-2.22), lopinavir/ritonavir (1.32, 95% CI = 1.12-1.57), nelfinavir (3.20, 95% CI = 2.74-3.74), and abacavir (2.13, 95% CI = 1.82-2.50). However, the rate of clinical events within 2 years of ART initiation appeared higher only with nevirapine (adjusted hazard ratio for composite outcome measure 1.27, 95% CI = 1.04-1.56) and abacavir (1.22, 95% CI = 1.00-1.48). CONCLUSION: Among antiretroviral-naïve patients initiating therapy, between-ART regimen, differences in short-term virologic failure do not necessarily translate to differences in clinical outcomes. Our results should be interpreted with caution because of the possibility of residual confounding by indication.

Land plants and DNA barcodes: short-term and long-term goals.

Land plants have had the reputation of being problematic for DNA barcoding for two general reasons: (i) the standard DNA regions used in algae, animals and fungi have exceedingly low levels of variability and (ii) the typically used land plant plastid phylogenetic markers (e.g. rbcL, trnL-F, etc.) appear to have too little variation. However, no one has assessed how well current phylogenetic resources might work in the context of identification (versus phylogeny reconstruction). In this paper, we make such an assessment, particularly with two of the markers commonly sequenced in land plant phylogenetic studies, plastid rbcL and internal transcribed spacers of the large subunits of nuclear ribosomal DNA (ITS), and find that both of these DNA regions perform well even though the data currently available in GenBank/EBI were not produced to be used as barcodes and BLAST searches are not an ideal tool for this purpose. These results bode well for the use of even more variable regions of plastid DNA (such as, for example, psbA-trnH) as barcodes, once they have been widely sequenced. In the short term, efforts to bring land plant barcoding up to the standards being used now in other organisms should make swift progress. There are two categories of DNA barcode users, scientists in fields other than taxonomy and taxonomists. For the former, the use of mitochondrial and plastid DNA, the two most easily assessed genomes, is at least in the short term a useful tool that permits them to get on with their studies, which depend on knowing roughly which species or species groups they are dealing with, but these same DNA regions have important drawbacks for use in taxonomic studies (i.e. studies designed to elucidate species limits). For these purposes, DNA markers from uniparentally (usually maternally) inherited genomes can only provide half of the story required to improve taxonomic standards being used in DNA barcoding. In the long term, we will need to develop more sophisticated barcoding tools, which would be multiple, low-copy nuclear markers with sufficient genetic variability and PCR-reliability; these would permit the detection of hybrids and permit researchers to identify the 'genetic gaps' that are useful in assessing species limits.

Les spirochètes dans tous leurs etats [A short journey through spirochetal infections]

Spirochetal infections present with a variety of clinical syndromes and epidemiologic features. Diagnosis remains challenging for the clinician because of the often protean clinical presentation and poor performance of stan-dard microbiological tests. We present 3 clinical cases, illustrating interesting or unusual features of these infections. First, we present a case of leptospirosis acquired in Switzerland after a rat bite. We then present a case of early disseminated Lyme disease with multiple erythema migrans, lymphopenia, thrombocytopenia and liver enzyme elevation. Finally, we present a case of secondary syphilis in an HIV-positive man, complicated by sensorineural deafness. For each case we highlight and discuss the specific epidemiological, clinical and therapeutic features.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen