Multi-layer amniotic membrane (MLAM) transplantation for non traumatic corneal perforations or descemetoceles

Purpose:To describe the indications, the surgical procedure and the clinical outcome of MLAM in the treatment of non traumatic corneal perforations and descemetoceles . Methods:A prospective, non comparative, interventional case series of eight consecutive patients (mean age 59 years old, 6 men and 2 women) with non traumatic corneal perforations or descemetoceles.The surgery consisted in a MLAM transplantation of a cryopreservated human amniotic membrane. The series included: three active herpetic keratitis, one rosacea, one perforation of an hydrops, one cicatricial pemphigoid, one perforation after an abcess in a corneal graft and one perforation after protonbeamtherapy. The clinical outcome included: the follow-up, the integrity of the eye, corneal epithelialization, inflammation and neovascularization, and the integration of the MLAM. Stromal thickness was followed precisely with the slit lamp. A corneal graft was performed at one patient after the MLAM, allowing microscopic investigation of the removed MLAM integrated in the cornea. Results:The mean follow-up was 8.78 months (range 3.57 to 30.17). Amniotic membrane transplantation was successful and reduced inflammation in 7 patients out of 8 ,after one procedure.One patient who presented a large herpetic keratitis epithelial defect with corneal anaesthesia had his MLAM dissolved after two weeks with an aqueous leakage. Epithelium healed within 3 weeks above 7 MLAM and remained stable at 3 months in 7 out of 8 patients. MLAM opacification gradually disappeared over a few months, however, stromal layers filling in the corneal perforations or above the descemetoceles remained stable. Conclusions:MLAM transplantation is a safe, effective and useful technique to cure non traumatic corneal perforations and descemetoceles. It can be performed in emergency despite the presence of an active inflammation or infection. By facilitating epithelialization, reducing inflammation and neovascularization, it allows corneal surface reconstruction in patients with persistent epithelial defects and corneal melting that usually ends in a perforation. For full visual rehabilitation, a delayed penetrating keratoplasty is required.

Error and attack tolerance of layered complex networks.

Many complex systems may be described by not one but a number of complex networks mapped on each other in a multi-layer structure. Because of the interactions and dependencies between these layers, the state of a single layer does not necessarily reflect well the state of the entire system. In this paper we study the robustness of five examples of two-layer complex systems: three real-life data sets in the fields of communication (the Internet), transportation (the European railway system), and biology (the human brain), and two models based on random graphs. In order to cover the whole range of features specific to these systems, we focus on two extreme policies of system's response to failures, no rerouting and full rerouting. Our main finding is that multi-layer systems are much more vulnerable to errors and intentional attacks than they appear from a single layer perspective.

Patterns of gravity induced aggregate migration during casting of fluid concretes

In this paper, aggregate migration patterns during fluid concrete castings are studied through experiments, dimensionless approach and numerical modeling. The experimental results obtained on two beams show that gravity induced migration is primarily affecting the coarsest aggregates resulting in a decrease of coarse aggregates volume fraction with the horizontal distance from the pouring point and in a puzzling vertical multi-layer structure. The origin of this multi layer structure is discussed and analyzed with the help of numerical simulations of free surface flow. Our results suggest that it finds its origin in the non Newtonian nature of fresh concrete and that increasing casting rate shall decrease the magnitude of gravity induced particle migration. (C) 2012 Elsevier Ltd. All rights reserved.

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.

Fate of the herbicide glyphosate and its metabolite AMPA in soils and their transfer to surface waters: A multi-scale approach in the Lavaux vineyards, western Switzerland

The use of herbicides in agriculture may lead to environmental problems, such as surface water pollution, with a potential risk for aquatic organisms. The herbicide glyphosate is the most used active ingredient in the world and in Switzerland. In the Lavaux vineyards it is nearly the only molecule applied. This work aimed at studying its fate in soils and its transfer to surface waters, using a multi-scale approach: from molecular (10-9 m) and microscopic scales (10-6 m), to macroscopic (m) and landscape ones (103 m). First of all, an analytical method was developed for the trace level quantification of this widely used herbicide and its main by-product, aminomethylphosphonic acid (AMPA). Due to their polar nature, their derivatization with 9-fluorenylmethyl chloroformate (FMOC-Cl) was done prior to their concentration and purification by solid phase extraction. They were then analyzed by ultra performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). The method was tested in different aqueous matrices with spiking tests and validated for the matrix effect correction in relevant environmental samples. Calibration curves established between 10 and 1000ng/l showed r2 values above 0.989, mean recoveries varied between 86 and 133% and limits of detection and quantification of the method were as low as 5 and 10ng/l respectively. At the parcel scale, two parcels of the Lavaux vineyard area, located near the Lutrive River at 6km to the east of Lausanne, were monitored to assess to which extent glyphosate and AMPA were retained in the soil or exported to surface waters. They were equipped at their bottom with porous ceramic cups and runoff collectors, which allowed retrieving water samples for the growing seasons 2010 and 2011. Results revealed that the mobility of glyphosate and AMPA in the unsaturated zone was likely driven by the precipitation regime and the soil characteristics, such as slope, porosity structure and layer permeability discrepancy. Elevated glyphosate and AMPA concentrations were measured at 60 and 80 cm depth at parcel bottoms, suggesting their infiltration in the upper parts of the parcels and the presence of preferential flow in the studied parcels. Indeed, the succession of rainy days induced the gradual saturation of the soil porosity, leading to rapid infiltration through macropores, as well as surface runoff formation. Furthermore, the presence of more impervious weathered marls at 100 cm depth induced throughflows, the importance of which for the lateral transport of the herbicide molecules was determined by the slope steepness. Important rainfall events (>10 mm/day) were clearly exporting molecules from the soil top layer, as indicated by important concentrations in runoff samples. A mass balance showed that total loss (10-20%) mainly occurred through surface runoff (96%) and, to a minor extent, by throughflows in soils (4%), with subsequent exfiltration to surface waters. Observations made in the Lutrive River revealed interesting details of glyphosate and AMPA dynamics in urbanized landscapes, such as the Lavaux vineyards. Indeed, besides their physical and chemical properties, herbicide dynamics at the catchment level strongly depend on application rates, precipitation regime, land use and also on the presence of drains or constructed channels. Elevated concentrations, up to 4970 ng/l, observed just after the application, confirmed the diffuse export of these compounds from the vineyard area by surface runoff during main rain events. From April to September 2011, a total load of 7.1 kg was calculated, with 85% coming from vineyards and minor urban sources and 15% from arable crops. Small vineyard surfaces could generate high concentrations of herbicides and contribute considerably to the total load calculated at the outlet, due to their steep slopes (~10%). The extrapolated total amount transferred yearly from the Lavaux vineyards to the Lake of Geneva was of 190kg. At the molecular scale, the possible involvement of dissolved organic matter (DOM) in glyphosate and copper transport was studied using UV/Vis fluorescence spectroscopy. Combined with parallel factor (PARAFAC) analysis, this technique allowed characterizing DOM of soil and surface water samples from the studied vineyard area. Glyphosate concentrations were linked to the fulvic-like spectroscopic signature of DOM in soil water samples, as well as to copper, suggesting the formation of ternary complexes. In surface water samples, its concentrations were also correlated to copper ones, but not in a significant way to the fulvic-like signature. Quenching experiments with standards confirmed field tendencies in the laboratory, with a stronger decrease in fluorescence intensity for fulvic-like fluorophore than for more aromatic ones. Lastly, based on maximum concentrations measured in the river, an environmental risk for these compounds was assessed, using laboratory tests and ecotoxicity data from the literature. In our case and with the methodology applied, the risk towards aquatic species was found negligible (RF<1).

Gains from switching and evolutionary stability in multi-player matrix games.

In this paper we unify, simplify, and extend previous work on the evolutionary dynamics of symmetric N-player matrix games with two pure strategies. In such games, gains from switching strategies depend, in general, on how many other individuals in the group play a given strategy. As a consequence, the gain function determining the gradient of selection can be a polynomial of degree N-1. In order to deal with the intricacy of the resulting evolutionary dynamics, we make use of the theory of polynomials in Bernstein form. This theory implies a tight link between the sign pattern of the gains from switching on the one hand and the number and stability of the rest points of the replicator dynamics on the other hand. While this relationship is a general one, it is most informative if gains from switching have at most two sign changes, as is the case for most multi-player matrix games considered in the literature. We demonstrate that previous results for public goods games are easily recovered and extended using this observation. Further examples illustrate how focusing on the sign pattern of the gains from switching obviates the need for a more involved analysis.

Regulation of horizontal gene transfer of the catabolic island ICEclc

ICEclc is a mobile genetic element found in two copies on the chromosome of the bacterium Pseudomonas knackmussii B13. ICEclc harbors genes encoding metabolic pathways for the degradation of chlorocatechols (CLC) and 2-aminophenol (2AP). At low frequencies, ICEclc excises from the chromosome, closes into a circular DNA molecule which can transfer to another bacterium via conjugation. Once in the recipient cell, ICEclc can reintegrate into the chromosome by site-specific recombination. This thesis aimed at identifying the regulatory network underlying the decisions for ICEclc horizontal transfer (HGT). The first chapter is an introduction on integrative and conjugative elements (ICEs) more in general, of which ICEclc is one example. In particular I emphasized the current knowledge of regulation and conjugation machineries of the different classes of ICE. In the second chapter, I describe a transcriptional analysis using microarrays and other experiments to understand expression of ICEclc in exponential and stationary phase. By overlaying transcriptomic profiles with Northern hybridizations and RT- PCR data, we established a transcription map for the entire core region of ICEclc, a region assumed to encode the ICE conjugation process. We also demonstrated how transcription of the ICEclc core is maximal in stationary phase, which correlates to expression of reporter genes fused to key ICEclc promoters. In the third chapter, I present a transcriptome analysis of ICEclc in a variety of different host species, in order to explore whether there are species-specific differences. In the fourth chapter, I focus on the role of a curious ICEclc-encoded TetR-type transcriptional repressor. We find that this gene, which we name mfsR, not only controls its own expression but that of a set of genes for a putative multi-drug efflux pump (mfsABC) as well. By using a combination of biochemical and molecular biology techniques, I could show that MfsR specifically binds to operator boxes in two ICEclc promoters (PmfsR and PmfsA), inhibiting the transcription of both the mfsR and mfsABC-orf38184 operons. Although we could not detect a clear phenotype of an mfsABC deletion, we discuss the implications of pump gene reorganizations in ICEclc and close relatives. In the fifth chapter, we find that mfsR not only controls its own expression and that of the mfsABC operon, but is also indirectly controlling ICEclc transfer. Using gene deletions, microarrays, transfer assays and microscopy-based reporter fusions, we demonstrate that mfsR actually controls a small operon of three regulatory genes. The last gene of this mfsR operon, orf17162, encodes a LysR-type activator that when deleted strongly impairs ICEclc transfer. Interestingly, deletion of mfsR leads to transfer competence in almost all cells, thereby overruling the bistability process in the wild-type. In the final sixth chapter, I discuss the relevance of the present thesis and the resulting perspectives for future studies.

Micro-Structural Brain Alterations in Aviremic HIV+ Patients with Minor Neurocognitive Disorders: A Multi-Contrast Study at High Field.

OBJECTIVE: Mild neurocognitive disorders (MND) affect a subset of HIV+ patients under effective combination antiretroviral therapy (cART). In this study, we used an innovative multi-contrast magnetic resonance imaging (MRI) approach at high-field to assess the presence of micro-structural brain alterations in MND+ patients. METHODS: We enrolled 17 MND+ and 19 MND- patients with undetectable HIV-1 RNA and 19 healthy controls (HC). MRI acquisitions at 3T included: MP2RAGE for T1 relaxation times, Magnetization Transfer (MT), T2* and Susceptibility Weighted Imaging (SWI) to probe micro-structural integrity and iron deposition in the brain. Statistical analysis used permutation-based tests and correction for family-wise error rate. Multiple regression analysis was performed between MRI data and (i) neuropsychological results (ii) HIV infection characteristics. A linear discriminant analysis (LDA) based on MRI data was performed between MND+ and MND- patients and cross-validated with a leave-one-out test. RESULTS: Our data revealed loss of structural integrity and micro-oedema in MND+ compared to HC in the global white and cortical gray matter, as well as in the thalamus and basal ganglia. Multiple regression analysis showed a significant influence of sub-cortical nuclei alterations on the executive index of MND+ patients (p = 0.04 he and R(2) = 95.2). The LDA distinguished MND+ and MND- patients with a classification quality of 73% after cross-validation. CONCLUSION: Our study shows micro-structural brain tissue alterations in MND+ patients under effective therapy and suggests that multi-contrast MRI at high field is a powerful approach to discriminate between HIV+ patients on cART with and without mild neurocognitive deficits.

Ultrastructural and molecular analysis of Bowman's layer corneal dystrophies: an epithelial origin?

PURPOSE: Two mutations (R555Q and R124L) in the BIGH3 gene have been described in anterior or Bowman's layer dystrophies (CDB). The clinical, molecular, and ultrastructural findings of five families with CDB was reviewed to determine whether there is a consistent genotype:phenotype correlation. METHODS: Keratoplasty tissue from each patient was examined by light and electron microscopy (LM and EM). DNA was obtained, and exons 4 and 12 of BIGH3 were analyzed by polymerase chain reaction and single-stranded conformation polymorphism/heteroduplex analysis. Abnormally migrating products were analyzed by direct sequencing. RESULTS: In two families with type I CDB (CDBI), the R124L mutation was defined. There were light and ultrastructural features of superficial granular dystrophy and atypical banding of the "rod-shaped bodies" ultrastructurally. Patients from three families with "honeycomb" dystrophy were found to carry the R555Q mutation and had characteristic features of Bowman's dystrophy type II (CDBII). CONCLUSIONS: There is a strong genotype:phenotype correlation among CBDI (R124L) and CDBII (R555Q). LM and EM findings suggest that epithelial abnormalities may underlie the pathology of both conditions. The findings clarify the confusion over classification of the Bowman's layer dystrophies.

In vitro functional correction of Hermansky-Pudlak Syndrome type-1 by lentiviral-mediated gene transfer.

Hermansky-Pudlak syndrome (HPS) is a genetic disorder characterized by oculocutaneous albinism, bleeding tendency and susceptibility to pulmonary fibrosis. No curative therapy is available. Genetic correction directed to the lungs, bone marrow and/or gastro-intestinal tract might provide alternative forms of treatment for the diseases multi-systemic complications. We demonstrate that lentiviral-mediated gene transfer corrects the expression and function of the HPS1 gene in patient dermal melanocytes, which opens the way to development of gene therapy for HPS.

Retrovirus-mediated gene transfer in polyclonal T cells results in lower apoptosis and enhanced ex vivo cell expansion of CMV-reactive CD8 T cells as compared with EBV-reactive CD8 T cells.

To modulate alloreactivity after hematopoietic stem cell transplantation, "suicide" gene-modified donor T cells (GMCs) have been administered with an allogeneic T-cell-depleted marrow graft. We previously demonstrated that such GMCs, generated after CD3 activation, retrovirus-mediated transduction, and G418 selection, had an impaired Epstein-Barr virus (EBV) reactivity, likely to result in an altered control of EBV-induced lymphoproliferative disease. To further characterize the antiviral potential of GMCs, we compared the frequencies of cytomegalovirus (CMV)-specific CD8+ T (CMV-T) cells and EBV-specific CD8+ T (EBV-T) cells within GMCs from CMV- and EBV-double seropositive donors. Unlike anti-EBV responses, the anti-CMV responses were not altered by GMC preparation. During the first days of culture, CMV-T cells exhibited a lower level of CD3-induced apoptosis than did EBV-T cells. In addition, the CMV-T cells escaping initial apoptosis subsequently underwent a higher expansion rate than EBV-T cells. The differential early sensitivity to apoptosis could be in relation to the "recent activation" phenotype of EBV-T cells as evidenced by a higher level of CD69 expression. Furthermore, EBV-T cells were found to have a CD45RA-CD27+CCR7- effector memory phenotype, whereas CMV-T cells had a CD45RA+CD27-CCR7- terminal effector phenotype. Such differences could be contributive, because bulk CD8+CD27- cells had a higher expansion than did bulk CD8+CD27+ cells. Overall, ex vivo T-cell culture differentially affects apoptosis, long-term proliferation, and overall survival of CMV-T and EBV-T cells. Such functional differences need to be taken into account when designing cell and/or gene therapy protocols involving ex vivo T-cell manipulation.

Simultaneous determination of antidementia drugs in human plasma: Procedure transfer from HPLC-MS to UPLC-MS/MS.

A previously developed high performance liquid chromatography mass spectrometry (HPLC-MS) procedure for the simultaneous determination of antidementia drugs, including donepezil, galantamine, memantine, rivastigmine and its metabolite NAP 226-90, was transferred to an ultra performance liquid chromatography system coupled to a tandem mass spectrometer (UPLC-MS/MS). The drugs and their internal standards ([(2)H(7)]-donepezil, [(13)C,(2)H(3)]-galantamine, [(13)C(2),(2)H(6)]-memantine, [(2)H(6)]-rivastigmine) were extracted from 250μL human plasma by protein precipitation with acetonitrile. Chromatographic separation was achieved on a reverse phase column (BEH C18 2.1mm×50mm; 1.7μm) with a gradient elution of an ammonium acetate buffer at pH 9.3 and acetonitrile at a flow rate of 0.4mL/min and an overall run time of 4.5min. The analytes were detected on a tandem quadrupole mass spectrometer operated in positive electrospray ionization mode, and quantification was performed using multiple reaction monitoring. The method was validated according to the recommendations of international guidelines over a calibration range of 1-300ng/mL for donepezil, galantamine and memantine, and 0.2-50ng/mL for rivastimgine and NAP 226-90. The trueness (86-108%), repeatability (0.8-8.3%), intermediate precision (2.3-10.9%) and selectivity of the method were found to be satisfactory. Matrix effects variability was inferior to 15% for the analytes and inferior to 5% after correction by internal standards. A method comparison was performed with patients' samples showing similar results between the HPLC-MS and UPLC-MS/MS procedures. Thus, this validated UPLC-MS/MS method allows to reduce the required amount of plasma, to use a simplified sample preparation, and to obtain a higher sensitivity and specificity with a much shortened run-time.

High-level transgene expression by homologous recombination-mediated gene transfer.

Gene transfer and expression in eukaryotes is often limited by a number of stably maintained gene copies and by epigenetic silencing effects. Silencing may be limited by the use of epigenetic regulatory sequences such as matrix attachment regions (MAR). Here, we show that successive transfections of MAR-containing vectors allow a synergistic increase of transgene expression. This finding is partly explained by an increased entry into the cell nuclei and genomic integration of the DNA, an effect that requires both the MAR element and iterative transfections. Fluorescence in situ hybridization analysis often showed single integration events, indicating that DNAs introduced in successive transfections could recombine. High expression was also linked to the cell division cycle, so that nuclear transport of the DNA occurs when homologous recombination is most active. Use of cells deficient in either non-homologous end-joining or homologous recombination suggested that efficient integration and expression may require homologous recombination-based genomic integration of MAR-containing plasmids and the lack of epigenetic silencing events associated with tandem gene copies. We conclude that MAR elements may promote homologous recombination, and that cells and vectors can be engineered to take advantage of this property to mediate highly efficient gene transfer and expression.

Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps.

Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures.