Surface modification of biomaterials for conjugation with human fetal osteoblasts.

Surface functionalization of hydroxyapatite (HA) and beta-tricalcium phosphate (TCP) bioceramics with chemical ligands containing a pyrrogallol moiety was developed to improve the adhesion of bone cell precursors to the biomaterials. Fast and biocompatible copper-free click reaction with azido-modified human fetal osteoblasts resulted in improved cell binding to both HA and TCP bioceramics, opening the way for using this methodology in the preparation of cell-engineered bone implants.

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.

Association of biomarkers of lipid modification with functional and morphological indices of coronary stenosis severity in stable coronary artery disease.

Biomarkers of blood lipid modification and oxidative stress have been associated with increased cardiovascular morbidity. We sought to determine whether these biomarkers were related to functional indices of stenosis severity among patients with stable coronary artery disease. We studied 197 consecutive patients with stable coronary artery disease due to single vessel disease. Fractional flow reserve (FFR) ≤ 0.80 was assessed as index of a functionally significant lesion. Serum levels of secretory phospholipase A2 (sPLA2) activity, secretory phospholipase A2 type IIA (sPLA2-IIA), myeloperoxydase (MPO), lipoprotein-associated phospholipase A2 (Lp-PLA2), and oxidized low-density lipoprotein (OxLDL) were assessed using commercially available assays. Patients with FFR > 0.8 had higher sPLA2 activity, sPLA2 IIA, and OxLDL levels than patients with FFR ≤ 0.8 (21.25 [16.03-27.28] vs 25.85 [20.58-34.63] U/mL, p < 0.001, 2.0 [1.5-3.4] vs 2.6 [2.0-3.4] ng/mL, p < 0.01; and 53.0 [36.0-71.0] vs 64.5 [50-89.25], p < 0.001 respectively). Patients with FFR > 0.80 had similar Lp-PLA2 and MPO levels versus those with FFR ≤ 0.8. sPLA2 activity, sPLA2 IIA significantly increased area under the curve over baseline characteristics to predict FFR ≤ 0.8 (0.67 to 0.77 (95 % confidence interval [CI]: 0.69-0.85) p < 0.01 and 0.67 to 0.77 (95 % CI: 0.69-0.84) p < 0.01, respectively). Serum sPLA2 activity as well as sPLA2-IIA level is related to functional characteristics of coronary stenoses in patients with stable coronary artery disease.

Las TIC como instrumento para la colaboración en red, en el ámbito de la Formación Profesional : Una aplicación de software libre para colaboración entre centro-empresa en el I.E.S El Palau

Investigaremos cómo las redes de colaboración y el softwarelibre permiten adaptar el centro educativo al entorno, cómo pueden ayudar al centro a potenciar la formación profesional y garantizar la durabilidad de las acciones, con el objetivo que perdure el conocimiento y la propia red de colaboración para una mejora educativa.

Décrets-lois de juillet et août 1935 portant modification des articles des codes

[Décrets-lois. 1935]

Implementación de GNU/Linux para una empresa de desarrollo de software

Trabajo que muestra, haciendo uso de tecnologías libres y basándonos en sistemas operativos abiertos, cómo es posible mantener un nivel alto de trabajo para una empresa que se dedica a implementar y realizar desarrollos en tecnologías de software libre. Se muestra el montaje de un laboratorio de desarrollo que nos va a permitir entender el funcionamiento y la implementación tanto de GNU/Linux como del software que se basa en él dentro de la infraestructura de la empresa.

Effects of epitope modification on T cell receptor-ligand binding and antigen recognition by seven H-2Kd-restricted cytotoxic T lymphocyte clones specific for a photoreactive peptide derivative.

We tested for antigen recognition and T cell receptor (TCR)-ligand binding 12 peptide derivative variants on seven H-2Kd-restricted cytotoxic T lymphocytes (CTL) clones specific for a bifunctional photoreactive derivative of the Plasmodium berghei circumsporozoite peptide 252-260 (SYIPSAEKI). The derivative contained iodo-4-azidosalicylic acid in place of PbCS S-252 and 4-azidobenzoic acid on PbCS K-259. Selective photoactivation of the N-terminal photoreactive group allowed crosslinking to Kd molecules and photoactivation of the orthogonal group to TCR. TCR photoaffinity labeling with covalent Kd-peptide derivative complexes allowed direct assessment of TCR-ligand binding on living CTL. In most cases (over 80%) cytotoxicity (chromium release) and TCR-ligand binding differed by less than fivefold. The exceptions included (a) partial TCR agonists (8 cases), for which antigen recognition was five-tenfold less efficient than TCR-ligand binding, (b) TCR antagonists (2 cases), which were not recognized and capable of inhibiting recognition of the wild-type conjugate, (c) heteroclitic agonists (2 cases), for which antigen recognition was more efficient than TCR-ligand binding, and (d) one partial TCR agonist, which activated only Fas (C1)95), but not perforin/granzyme-mediated cytotoxicity. There was no correlation between these divergences and the avidity of TCR-ligand binding, indicating that other factors than binding avidity determine the nature of the CTL response. An unexpected and novel finding was that CD8-dependent clones clearly incline more to TCR antagonism than CD8-independent ones. As there was no correlation between CD8 dependence and the avidity of TCR-ligand binding, the possibility is suggested that CD8 plays a critical role in aberrant CTL function.

Co- and posttranslational modification of the alpha(1B)-adrenergic receptor: effects on receptor expression and function.

We have characterized the maturation, co- and posttranslational modifications, and functional properties of the alpha(1B)-adrenergic receptor (AR) expressed in different mammalian cells transfected using conventional approaches or the Semliki Forest virus system. We found that the alpha(1B)-AR undergoes N-linked glycosylation as demonstrated by its sensitivity to endoglycosidases and by the effect of tunicamycin on receptor maturation. Pulse-chase labeling experiments in BHK-21 cells demonstrate that the alpha(1B)-AR is synthesized as a 70 kDa core glycosylated precursor that is converted to the 90 kDa mature form of the receptor with a half-time of approximately 2 h. N-Linked glycosylation of the alpha(1B)-AR occurs at four asparagines on the N-terminus of the receptor. Mutations of the N-linked glycosylation sites did not have a significant effect on receptor function or expression. Surprisingly, receptor mutants lacking N-linked glycosylation migrated as heterogeneous bands in SDS-PAGE. Our findings demonstrate that N-linked glycosylation and phosphorylation, but not palmitoylation or O-linked glycosylation, contribute to the structural heterogeneity of the alpha(1B)-AR as it is observed in SDS-PAGE. The modifications found are similar in the different mammalian expression systems explored. Our findings indicate that the Semliki Forest virus system can provide large amounts of functional and fully glycosylated alpha(1B)-AR protein suitable for biochemical and structural studies. The results of this study contribute to elucidate the basic steps involved in the processing of G protein-coupled receptors as well as to optimize strategies for their overexpression.

Software for interpolation of vegetative growth of yerba mate plants in 3D

The objective of this work was to build mock-ups of complete yerba mate plants in several stages of development, using the InterpolMate software, and to compute photosynthesis on the interpolated structure. The mock-ups of yerba-mate were first built in the VPlants software for three growth stages. Male and female plants grown in two contrasting environments (monoculture and forest understory) were considered. To model the dynamic 3D architecture of yerba-mate plants during the biennial growth interval between two subsequent prunings, data sets of branch development collected in 38 dates were used. The estimated values obtained from the mock-ups, including leaf photosynthesis and sexual dimorphism, are very close to those observed in the field. However, this similarity was limited to reconstructions that included growth units from original data sets. The modeling of growth dynamics enables the estimation of photosynthesis for the entire yerba mate plant, which is not easily measurable in the field. The InterpolMate software is efficient for building yerba mate mock-ups.

Quantification of Myocardial Perfusion Using 13N-ammonia PET: a Cross-Comparison Study on Analysis Software Packages - Carimas, PMOD and FlowQuant

Aim. Several software packages (SWP) and models have been released for quantification of myocardial perfusion (MP). Although they all are validated against something, the question remains how well their values agree. The present analysis focused on cross-comparison of three SWP for MP quantification of 13N-ammonia PET studies. Materials & Methods. 48 rest and stress MP 13N-ammonia PET studies of hypertrophic cardiomyopathy (HCM) patients (Sciagrà et al., 2009) were analysed with three SW packages - Carimas, PMOD, and FlowQuant - by three observers blinded to the results of each other. All SWP implement the one-tissue-compartment model (1TCM, DeGrado et al. 1996), and first two - the two-tissue-compartment model (2TCM, Hutchins et al. 1990) as well. Linear mixed model for the repeated measures was fitted to the data. Where appropriate we used Bland-Altman plots as well. The reproducibility was assessed on global, regional and segmental levels. Intraclass correlation coefficients (ICC), differences between the SWPs and between models were obtained. ICC≥0.75 indicated excellent reproducibility, 0.4≤ICC<0.75 indicated fair to good reproducibility, ICC<0.4 - poor reproducibility (Rosner, 2010). Results. When 1TCM MP values were compared, the SW agreement on global and regional levels was excellent, except for Carimas vs. PMOD at RCA: ICC=0.715 and for PMOD vs. FlowQuant at LCX:ICC=0.745 which were good. In segmental analysis in five segments: 7,12,13, 16, and 17 the agreement between all SWP was excellent; in the remaining 12 segments the agreement varied between the compared SWP. Carimas showed excellent agreement with FlowQuant in 13 segments and good in four - 1, 5, 6, 11: 0.687≤ICCs≤0.73; Carimas had excellent agreement with PMOD in 11 segments, good in five_4, 9, 10, 14, 15: 0.682≤ICCs≤0.737, and poor in segment 3: ICC=0.341. PMOD had excellent agreement with FlowQuant in eight segments and substantial-to-good in nine_1, 2, 3, 5, 6,8-11: 0.585≤ICCs≤0.738. Agreement between Carimas and PMOD for 2TCM was good at a global level: ICC=0.745, excellent at LCX (0.780) and RCA (0.774), good at LAD (0.662); agreement was excellent for ten segments, fair-to-substantial for segments 2, 3, 8, 14, 15 (0.431≤ICCs≤0.681), poor for segments 4 (0.384) and 17 (0.278). Conclusions. The three SWP used by different operators to analyse 13N-ammonia PET MP studies provide results that agree well at a global level, regional levels, and mostly well even at a segmental level. Agreement is better for 1TCM. Poor agreement at segments 4 and 17 for 2TCM needs further clarification.

Aging and Disability Resource Center and MHDS Commission Home Modification Assistance Program Plan, December 2015

This report was prepared as a directive to Aging and Disability Resource Centers and The Mental Health and Disability Commission to jointly develop a plan for a home modification assistance program to provide grants and individual income tax credits to assist with expenses related to the making or permanent home modifications that permit individual with a disability to remain in the homes.

DnaSP v5: A software for comprehensive analysis of DNA polymorphism data

DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser.

Transjugular liver biopsy: prospective evaluation of the angle formed between the hepatic veins and the vena cava main axis and modification of a semi-automated biopsy device in cases of an unfavorable angle.

In cases of transjugular liver biopsies, the venous angle formed between the chosen hepatic vein and the vena cava main axis in a frontal plane can be large, leading to technical difficulties. In a prospective study including 139 consecutive patients who underwent transjugular liver biopsy using the Quick-Core biopsy set, the mean venous angle was equal to 49.6 degrees. For 21.1% of the patients, two attempts at hepatic venous catheterization failed because the venous angle was too large, with a mean of 69.7 degrees. In all of these patients, manual reshaping of the distal curvature of the stiffening metallic cannula, by forming a new mean angle equal to 48 degrees , allowed successful completion of the procedure in less than 10 min.

Lawyer-info : Software de atención al cliente para despachos de abogados en dispositivos móviles Android

Este trabajo desarrolla una aplicación basada en la tecnología Android para la atención de clientes en despachos de abogados.