Les Imitacions de vaixella fina importada a la Hispania Citerior (segles I aC-I dC)

Aquesta obra és fruit de la taula rodona «Imitatio Vasaria. Les imitacions de vaixella fina a la Hispània Citerior en època tardorepublicana i altimperial: producció i comercialització», que va reunir durant tres dies representants de tretze grups de recerca per reflexionar sobre les imitacions de les ceràmiques fines d’importació d’època romana tardorepublicana i altimperial. Com a resultat d’aquestes xerrades es van redactar 14 articles que presenten els estats de la qüestió que cada grup de recerca va portar a terme en el seu àmbit geogràfic i en els jaciments on van desenvolupar el seu treball de camp: diverses zones de la Hispània Citerior des de les universitats de Girona, Barcelona, Autònoma de Barcelona, València, Alacant i Valladolid, així com des dels Museus d’Arqueologia de Catalunya-Empúries, Mataró i Badalona, i de l’ICAC. Dos articles del llibre, però, se centren en un altre punt de la Mediterrània occidental, el Llenguadoc, aportat per un grup del Centre National de la Recherche Scientifique (CNRS).

El Cáncer de mama en la prensa española : la cobertura informativa del cáncer de mama en ABC, El País, El Mundo, La Vanguardia y El Periódico de Catalunya, de 2006 a 2010

Los medios de comunicación tienen un papel muy importante en la divulgación de la información sobre la salud. Una información de calidad sobre el cáncer de mama puede ayudar a miles de mujeres a prevenir y a detectar precozmente esta enfermedad, mejorando su pronóstico y su calidad de vida. El objetivo de este trabajo es analizar la cobertura informativa sobre el cáncer de mama en los cinco diarios de mayor difusión en España: “El País”, “El Mundo”, “ABC”, “La Vanguardia” y “El Periódico de Catalunya”, de 2006 a 2010. La metodología utilizada es el análisis de contenido.

Magmatic fluids in the Breccia-hosted epithermal Au-Ag deposit of Rosia Montana, Romania

The breccia-hosted epithermal Au-Ag deposit of Rosia Montana is located 7 kin northeast of Abrud, in the northern part of the South Apuseni Mountains, Romania. Estimated total reserves of 214.91 million metric toils (Mt) of ore at 1.46 g/t An and 6.9 g/t Ag (10.1 Moz of An and 47.6 Moz of Ag) make Rosia Montana one of the largest gold deposits in Europe. At this location, Miocene calc-alkaline magmatic and hydrothermal activity was associated with local extensional tectonics within a strike-slip regime related to the indentation of the Adriatic microplate into the European plate during the Carpathian orogenesis. The host rocks of the magmatic complex consist of pre-Mesozoic metamorphosed continental crust covered by Cretaceous turbiditic sediment (flysch). Magmatic activity at Rosia Montana and its surroundings occurred in several pulses and lasted about 7 m.y, Rosia Montana is a breccia-hosted epithermal system related to strong phreatomagmatic activity due to the shallow emplacement of the Montana dacite. The Montana dacite intruded Miocene volcaniclastic material (volcaniclastic breccias) and crops out at Cetate and Carnic Hills. Current mining is focused primarily on the Cetate open pit, which was mapped in detail, leading to the recognition of three distinct breccia bodies: the dacite breccia with a dominantly hydrothermal matrix, the gray polymict breccia with a greater proportion of sand-sized matrix support, and the black polymict breccia, which reached to the surface, contains carbonized tree trunks and has a dominantly barren elastic matrix. The hydrothermal alteration is pervasive. Adularia alteration with a phyllic overprint is ubiquitous; silicification and argillic alteration occur locally. Mineralization consists of quartz, adularia, carbonates (commonly Mn-rich), pyrite, Fe-poor sphalerite, galena, chalcopyrite, tetrahedrite, and native gold and occurs as disseminations, as well as in veins and filling vugs within the Montana dacite and the different breccias. The age of mineralization (12.85 +/- 0.07 Ma) was determined by Ar-40- Ar-39 dating on hydrothermal adularia crystals from vugs in the dacite breccia in the Cetate open pit. Microthermometric measurements of fluid inclusions in quartz phenocrysts from the Montana dacite revealed two fluid types that are absent from the hydrothermal breccia and must have been trapped at depth prior to dacite dome emplacement: brine inclusions (32-55 -wt % NaCl equiv, homogenizing at T-h > 460 degrees C) and intermediate density fluids (4.9-15.6 wt % NaCl equiv, T, between 345 degrees-430 degrees C). Secondary aqueous fluid inclusion assemblages in the phenocrysts have salinities of 0.2 to 2.2 wt percent NaCl equiv and T-h of 200 degrees to 280 degrees C. Fluid inclusion assemblages in hydrothermal quartz from breccias and veins have salinities of 0.2 to 3.4 wt percent NaCl equiv and T-h, from 200 degrees to 270 degrees C. The oxygen isotope composition of several zones of an ore-related epithermal quartz crystal indicate a very constant delta O-18 of 4.5 to 5.0 per mil for the mineralizing fluid, despite significant salinity and temperature variation over time. Following microthermometry, selected fluid inclusion assemblages were analyzed by laser ablation-inductively coupled-plasma mass spectrometry (LA-ICMS). Despite systematic differences in salinity between phenocryst-hosted fluids trapped at depth and fluids from quartz in the epithermal breccias, all fluids have overlapping major and trace cation ratios, including identical Na/K/Rb/Sr/Cs/Ba. Consistent with the constant near-magmatic oxygen isotope composition of the hydrothermal fluids, these data strongly indicate a common magmatic component of these chemically conservative solutes in all fluids. Cu, Pb, Zn, and Mn show variations in concentration relative to the relatively non-reactive alkalis, reflecting the precipitation of sulfide minerals together with An in the epithermal breccia, and possibly of Cu in an inferred subjacent porphyry environment. The magmatic-hydrothermal processes responsible for epithermal Au-Ag mineralization at Rosia Montana are, however, not directly related to the formation of the spatially associated porphyry Cu-Au deposit of Rosia Poieni, which occurred lout 3 m.y. later.

Iowa Department of Transportation Major Items - Preliminary Quantity Report, December 15, 2015; With Addendums

Weekly letting report

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.

Development of pharmaceutical care services in nursing homes: practice and research in a Swiss canton.

OBJECTIVE: The aim of this study was to assess the implementation process and economic impact of a new pharmaceutical care service provided since 2002 by pharmacists in Swiss nursing homes. SETTING: The setting was 42 nursing homes located in the canton of Fribourg, Switzerland under the responsibility of 22 pharmacists. METHOD: We developed different facilitators, such as a monitoring system, a coaching program, and a research project, to help pharmacists change their practice and to improve implementation of this new service. We evaluated the implementation rate of the service delivered in nursing homes. We assessed the economic impact of the service since its start in 2002 using statistical evaluation (Chow test) with retrospective analysis of the annual drug costs per resident over an 8-year period (1998-2005). MAIN OUTCOME MEASURES: The description of the facilitators and their implications in implementation of the service; the economic impact of the service since its start in 2002. RESULTS: In 2005, after a 4-year implementation period supported by the introduction of facilitators of practice change, all 42 nursing homes (2,214 residents) had implemented the pharmaceutical care service. The annual drug costs per resident decreased by about 16.4% between 2002 and 2005; this change proved to be highly significant. The performance of the pharmacists continuously improved using a specific coaching program including an annual expert comparative report, working groups, interdisciplinary continuing education symposia, and individual feedback. This research project also determined priorities to develop practice guidelines to prevent drug-related problems in nursing homes, especially in relation to the use of psychotropic drugs. CONCLUSION: The pharmaceutical care service was fully and successfully implemented in Fribourg's nursing homes within a period of 4 years. These findings highlight the importance of facilitators designed to assist pharmacists in the implementation of practice changes. The economic impact was confirmed on a large scale, and priorities for clinical and pharmacoeconomic research were identified in order to continue to improve the quality of integrated care for the elderly.

The T309G Murine Double Minute 2 Gene Polymorphism Is an Independent Prognostic Factor for Patients with Renal Cell Carcinoma.

The purpose of this study was to evaluate the association of the T309G MDM2 gene polymorphism with renal cell carcinoma (RCC) risk, pathology, and cancer-specific survival (CSS). T309G MDM2 was genotyped in 449 Caucasians, including 240 with RCC and 209 cancer-free controls. The T309G MDM2 genotype was TT in 174 (38.8%), GT in 214 (47.7%), and GG in 61 (13.6%) subjects, without any significant differences between cases and controls on both univariable (p=0.58) and multivariable logistic regression (each p>0.25). Furthermore, T309G MDM2 was not linked with T stage (p=0.75), N stage (p=0.37), M stage (p=0.94), grade (p=0.21), and subtype (p=0.55). There was, however, a statistically significant association of T309G MDM2 with CSS (p=0.022): patients with TT had significantly worse survival than GG/GT (p=0.009), while those with GT and GG had similar outcomes (p=0.92). The 5-year survival rate for patients with TT, GT, and GG was 69.5%, 84.5%, and 89.7%, respectively. On the multivariable analysis, T309G was identified as an independent prognostic factor. The T309G MDM2 polymorphism is an independent prognostic factor for patients with RCC, with the TT genotype being associated with worse prognosis. In this study, there were no significant associations with RCC risk and pathology.

Anàlisi de la petjada de carboni de la gestió i el tractament dels residus municipals a Menorca

En aquesta investigació s’analitza l’impacte que ocasiona les emissions de CO2eq en la gestió i tractament dels residus municipals de l’illa de Menorca. El present treball s’ha realitzat mitjançant l’eina innovadora d’anàlisi ambiental CO2ZW que segueix un protocol de càlcul per a la identificació i quantificació dels gasos d’efecte hivernacle al llarg del cicle de vida de la gestió dels residus municipals. Aquest treball caracteritza l’impacte generat i evitat recomanats segons els informes de l’IPCC i esdevé com un punt de partida per la reducció de les emissions en el conjunt de l’economia. L’objectiu del treball és observar l’estat actual de la petjada de carboni per poder així establir una sèrie de escenaris futurs que facilitin el camí per arribar a la petjada de carboni zero.

[Monreale. Duomo Santa Maria Nuova]

Référence bibliographique : Weigert, 214

Is screening for fetal anomalies reliable in HIV-infected pregnant women? A multicentre study.

OBJECTIVE: To assess the impact of HIV infection on the reliability of the first-trimester screening for Down syndrome, using free beta-human chorionic gonadotrophin, pregnancy-associated plasma protein-A and fetal nuchal translucency, and of the second-trimester screening for neural tube defects, using alpha-fetoprotein. PATIENTS AND METHODS: Multicentre study comparing the multiples of the median of markers for Down syndrome and neural tube defect screening among 214 HIV-infected pregnant women and 856 HIV-negative controls undergoing a first-trimester Down syndrome screening test, and 209 HIV-positive women and 836 HIV-negative controls with a risk evaluation for neural tube defect. The influence of treatment, chronic hepatitis and HIV disease characteristics were also evaluated. RESULTS: Multiples of the median medians for pregnancy-associated plasma protein-A and beta-human chorionic gonadotrophin were lower in HIV-positive women than controls (0.88 vs. 1.05 and 0.84 vs. 1.09, respectively; P < 0.005), but these differences had no impact on risk estimation; no differences were observed for the other markers. No association was found between HIV disease characteristics, antiretroviral treatment use at the time of screening or chronic hepatitis and marker levels. CONCLUSION: Screening for Down syndrome during the first trimester and for neural tube defect during the second trimester is accurate for HIV-infected women and should be offered, similar to HIV-negative women.