The glycoinositol-phospholipids of Phytomonas.

The Phytomonas spp. are trypanosomatid parasites of plants. A polar glycolipid fraction of a Phytomonas sp., isolated from the plant Euphorbia characias and grown in culture, was fractionated into four major glycolipid species (Phy 1-4). The glycolipids were analysed by chemical and enzymic modifications, composition and methylation analyses, electrospray mass spectrometry and microsequencing after HNO2 deamination and NaB3H4 reduction. The water-soluble headgroup of the Phy2 glycolipid was also analysed by 1H NMR. All four glycolipids were shown to be glycoinositol-phospholipids (GIPLs) with phosphatidylinositol (PI) moieties containing the fully saturated alkylacylglycerol lipids 1-O-hexadecyl-2-O-palmitoylglycerol and 1-O-hexadecyl-2-O-stearoylglycerol. The structures of the Phy 1-4 GIPLs are: Man alpha 1-2Man alpha 1-6Man alpha 1-4GlcN alpha 1-6PI, Glc alpha 1-2(NH2-CH2CH2-HPO4-)Man alpha 1-2Man alpha 1-6Man alpha 1-4GlcN alpha 1-6PI, [formula: see text] Glc alpha 1-2(NH2CH2CH2-HPO4-)Man alpha 1-2Man alpha 1-6Man alpha 1-4(NH2-CH2CH2-HPO4-)GlcN alpha 1-6PI [formula: see text] and Glc alpha 1-2Glc alpha 1-2(NH2CH2-CH2-HPO4-)Man alpha 1-2Man alpha 1-6Man alpha 1-4(NH2CH2CH2-HPO4-)-GlcN alpha 1-6PI. [formula: see text] The Phytomonas GIPLs represent a novel series of structures. This is the first description of the chemical structure of cell-surface molecules of this plant pathogen. The Phytomonas GIPLs are compared with those of other trypanosomatid parasites and are discussed with respect to trypanosomatid phylogenetic relationships.

National Clinical Audit Support Programme: National Diabetes Audit

The National Diabetes Audit (NDA) is a national audit service sponsored by the Healthcare Commission. Available since April 2004, it enables routine data collection, analysis and feedback of diabetes related data for all adults and children with diabetes in England.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

A Bayesian approach for pervasive estimation of breaststroke velocity using a wearable IMU

A ubiquitous assessment of swimming velocity (main metric of the performance) is essential for the coach to provide a tailored feedback to the trainee. We present a probabilistic framework for the data-driven estimation of the swimming velocity at every cycle using a low-cost wearable inertial measurement unit (IMU). The statistical validation of the method on 15 swimmers shows that an average relative error of 0.1 ± 9.6% and high correlation with the tethered reference system (rX,Y=0.91 ) is achievable. Besides, a simple tool to analyze the influence of sacrum kinematics on the performance is provided.

Integrating forensic information in a crime intelligence database

Since 2008, Intelligence units of six states of the western part of Switzerland have been sharing a common database for the analysis of high volume crimes. On a daily basis, events reported to the police are analysed, filtered and classified to detect crime repetitions and interpret the crime environment. Several forensic outcomes are integrated in the system such as matches of traces with persons, and links between scenes detected by the comparison of forensic case data. Systematic procedures have been settled to integrate links assumed mainly through DNA profiles, shoemarks patterns and images. A statistical outlook on a retrospective dataset of series from 2009 to 2011 of the database informs for instance on the number of repetition detected or confirmed and increased by forensic case data. Time needed to obtain forensic intelligence in regard with the type of marks treated, is seen as a critical issue. Furthermore, the underlying integration process of forensic intelligence into the crime intelligence database raised several difficulties in regards of the acquisition of data and the models used in the forensic databases. Solutions found and adopted operational procedures are described and discussed. This process form the basis to many other researches aimed at developing forensic intelligence models.

Determinants of school efficiency : The case of primary schools in the State of Geneva, Switzerland

The public primary school system in the State of Geneva, Switzerland, is characterized by centrally evaluated pupil performance measured with the use of standardized tests. As a result, consistent data are collected among the system. The 2010-2011 dataset is used to develop a two-stage data envelopment analysis (DEA) of school efficiency. In the first stage, DEA is employed to calculate an individual efficiency score for each school. It shows that, on average, each school could reduce its inputs by 7% whilst maintaining the same quality of pupil performance. The cause of inefficiency lies in perfectible management. In the second stage, efficiency is regressed on school characteristics and environmental variables;external factors outside of the control of headteachers. The model is tested for multicollinearity, heteroskedasticity and endogeneity. Four variables are identified as statistically significant. School efficiency is negatively influenced by (1) the provision of special education, (2) the proportion of disadvantaged pupils enrolled at the school and (3) operations being held on multiple sites, but positively influenced by school size (captured by the number of pupils). The proportion of allophone pupils; schools located in urban areas and the provision of reception classes for immigrant pupils are not significant. Although the significant variables influencing school efficiency are outside of the control of headteachers, it is still possible to either boost the positive impact or curb the negative impact. Dans le canton de Genève (Suisse), les écoles publiques primaires sont caractérisées par un financement assuré par les collectivités publiques (canton et communes) et par une évaluation des élèves à l'aide d'épreuves standardisées à trois moments distincts de leur scolarité. Cela permet de réunir des informations statistiques consistantes. La base de données de l'année 2010-2011 est utilisée dans une analyse en deux étapes de l'efficience des écoles. Dans une première étape, la méthode d'analyse des données par enveloppement (DEA) est utilisée pour calculer un score d'efficience pour chaque école. Cette analyse démontre que l'efficience moyenne des écoles s'élève à 93%. Chaque école pourrait, en moyenne, réduire ses ressources de 7% tout en conservant constants les résultats des élèves aux épreuves standardisées. La source de l'inefficience réside dans un management des écoles perfectible. Dans une seconde étape, les scores d'efficience sont régressés sur les caractéristiques des écoles et sur des variables environnementales. Ces variables ne sont pas sous le contrôle (ou l'influence) des directeurs d'école. Le modèle est testé pour la multicolinéartié, l'hétéroscédasticité et l'endogénéité. Quatre variables sont statistiquement significatives. L'efficience des écoles est influencée négativement par (1) le fait d'offrir un enseignement spécialisé en classe séparée, (2) la proporition d'élèves défavorisés et (3) le fait d'opérer sur plusieurs sites différents. L'efficience des écoles est influencée positivement par la taille de l'école, mesurée par le nombre d'élèves. La proporition d'élèves allophones, le fait d'être situé dans une zone urbaine et d'offrir des classes d'accueil pour les élèves immigrants constituent autant de variables non significatives. Le fait que les variables qui influencent l'efficience des écoles ne soient pas sous le contrôle des directeurs ne signifie pas qu'il faille céder au fatalisme. Différentes pistes sont proposées pour permettre soit de réduire l'impact négatif soit de tirer parti de l'impact positif des variables significatives.

Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort.

BACKGROUND Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is difficult to diagnose and has limited treatment options with a low survival rate. Aside from a few key risk factors, such as hepatitis, high alcohol consumption, smoking, obesity, and diabetes, there is incomplete etiologic understanding of the disease and little progress in identification of early risk biomarkers. METHODS To address these aspects, an untargeted nuclear magnetic resonance metabolomic approach was applied to pre-diagnostic serum samples obtained from first incident, primary HCC cases (n = 114) and matched controls (n = 222) identified from amongst the participants of a large European prospective cohort. RESULTS A metabolic pattern associated with HCC risk comprised of perturbations in fatty acid oxidation and amino acid, lipid, and carbohydrate metabolism was observed. Sixteen metabolites of either endogenous or exogenous origin were found to be significantly associated with HCC risk. The influence of hepatitis infection and potential liver damage was assessed, and further analyses were made to distinguish patterns of early or later diagnosis. CONCLUSION Our results show clear metabolic alterations from early stages of HCC development with application for better etiologic understanding, prevention, and early detection of this increasingly common cancer.

A combination of ascorbic acid and α-tocopherol to test the effectiveness and safety in the fragile X syndrome: study protocol for a phase II, randomized, placebo-controlled trial.

BACKGROUND Fragile X syndrome (FXS) is an inherited neurodevelopmental condition characterised by behavioural, learning disabilities, physical and neurological symptoms. In addition, an important degree of comorbidity with autism is also present. Considered a rare disorder affecting both genders, it first becomes apparent during childhood with displays of language delay and behavioural symptoms.Main aim: To show whether the combination of 10 mg/kg/day of ascorbic acid (vitamin C) and 10 mg/kg/day of α-tocopherol (vitamin E) reduces FXS symptoms among male patients ages 6 to 18 years compared to placebo treatment, as measured on the standardized rating scales at baseline, and after 12 and 24 weeks of treatment.Secondary aims: To assess the safety of the treatment. To describe behavioural and cognitive changes revealed by the Developmental Behaviour Checklist Short Form (DBC-P24) and the Wechsler Intelligence Scale for Children-Revised. To describe metabolic changes revealed by blood analysis. To measure treatment impact at home and in an academic environment. METHODS/DESIGN A phase II randomized, double-blind pilot clinical trial. SCOPE male children and adolescents diagnosed with FXS, in accordance with a standardized molecular biology test, who met all the inclusion criteria and none of the exclusion criteria. INSTRUMENTATION clinical data, blood analysis, Wechsler Intelligence Scale for Children-Revised, Conners parent and teacher rating scale scores and the DBC-P24 results will be obtained at the baseline (t0). Follow up examinations will take place at 12 weeks (t1) and 24 weeks (t2) of treatment. DISCUSSION A limited number of clinical trials have been carried out on children with FXS, but more are necessary as current treatment possibilities are insufficient and often provoke side effects. In the present study, we sought to overcome possible methodological problems by conducting a phase II pilot study in order to calculate the relevant statistical parameters and determine the safety of the proposed treatment. The results will provide evidence to improve hyperactivity control and reduce behavioural and learning problems using ascorbic acid (vitamin C) and α-tocopherol (vitamin E). The study protocol was approved by the Regional Government Committee for Clinical Trials in Andalusia and the Spanish agency for drugs and health products. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT01329770 (29 March 2011).

Desempenho dos municípios paulistas: uma avaliação de eficiência da atenção básica à saúde

Neste trabalho, teve-se por objetivo construir uma metodologia de avaliação de desempenho dos municípios paulistas quanto à eficiência técnica na aplicação de recursos públicos nas ações de atenção básica à saúde e analisar a influência de variáveis não controláveis no processo de produção em tal área. A eficiência técnica é um dos parâmetros de avaliação de desempenho dos gestores públicos, refletindo a capacidade de uma entidade obter máximos outputs com o menor consumo de inputs. O alcance de tal métrica pode ser prejudicado ou favorecido pelas variáveis ambientais ou não controláveis que, se não forem consideradas na avaliação de desempenho, podem gerar vieses. Nesse sentido, por meio da metodologia Data Envelopment Analysis (DEA) em dois estágios, os escores de eficiência dos municípios foram estimados e depois ajustados com o uso da análise de regressão. Os resultados indicaram que seria possível aumentar, consideravelmente, a quantidade de serviços prestados à população sem a necessidade de novas dotações orçamentárias na maioria dos municípios. Além disso, verificou-se que a maior proporção de idosos em uma jurisdição torna a prestação de serviços mais cara; por sua vez, maiores densidade populacional, grau de urbanização e escala dos estabelecimentos de saúde favorecem o gasto público com eficiência. Os cinco municípios paulistas considerados mais eficientes foram Tuiuti, Nova Guataporanga, Sabino, Lins e Santos.

Impacto de las inversiones en TI en la eficiencia de los bancos argentinos

Las tecnologías de información (TI) constituyen en la actualidad una herramienta fundamental para el logro de ventajas competitivas en un contexto sumamente cambiante. Resulta relevante conocer el impacto de su utilización sobre el desempeño de las organizaciones, en particular en sectores como el bancario, que realizan un uso intensivo de información. En el presente trabajo se desarrolla un modelo que posibilita evaluar el impacto de las inversiones en TI sobre la eficiencia de las entidades bancarias. Se utilizó la técnica Data Envelopment Analysis (DEA) como herramienta de medición de la eficiencia de acuerdo al modelo elaborado sobre la información obtenida de los estados contables de entidades bancarias de la República Argentina. Los resultados obtenidos demostraron que los bancos alcanzaron un buen nivel de eficiencia global. Se encontró que los más eficientes son las sucursales de entidades financieras del exterior y los bancos locales de capital extranjero.

Metabolomic insights into the intricate gut microbial-host interaction in the development of obesity and type 2 diabetes.

Gut microbiota has recently been proposed as a crucial environmental factor in the development of metabolic diseases such as obesity and type 2 diabetes, mainly due to its contribution in the modulation of several processes including host energy metabolism, gut epithelial permeability, gut peptide hormone secretion, and host inflammatory state. Since the symbiotic interaction between the gut microbiota and the host is essentially reflected in specific metabolic signatures, much expectation is placed on the application of metabolomic approaches to unveil the key mechanisms linking the gut microbiota composition and activity with disease development. The present review aims to summarize the gut microbial-host co-metabolites identified so far by targeted and untargeted metabolomic studies in humans, in association with impaired glucose homeostasis and/or obesity. An alteration of the co-metabolism of bile acids, branched fatty acids, choline, vitamins (i.e., niacin), purines, and phenolic compounds has been associated so far with the obese or diabese phenotype, in respect to healthy controls. Furthermore, anti-diabetic treatments such as metformin and sulfonylurea have been observed to modulate the gut microbiota or at least their metabolic profiles, thereby potentially affecting insulin resistance through indirect mechanisms still unknown. Despite the scarcity of the metabolomic studies currently available on the microbial-host crosstalk, the data-driven results largely confirmed findings independently obtained from in vitro and animal model studies, putting forward the mechanisms underlying the implication of a dysfunctional gut microbiota in the development of metabolic disorders.

A nonapeptide encoded by human gene MAGE-1 is recognized on HLA-A1 by cytolytic T lymphocytes directed against tumor antigen MZ2-E.

We have reported the identification of human gene MAGE-1, which directs the expression of an antigen recognized on a melanoma by autologous cytolytic T lymphocytes (CTL). We show here that CTL directed against this antigen, which was named MZ2-E, recognize a nonapeptide encoded by the third exon of gene MAGE-1. The CTL also recognize this peptide when it is presented by mouse cells transfected with an HLA-A1 gene, confirming the association of antigen MZ2-E with the HLA-A1 molecule. Other members of the MAGE gene family do not code for the same peptide, suggesting that only MAGE-1 produces the antigen recognized by the anti-MZ2-E CTL. Our results open the possibility of immunizing HLA-A1 patients whose tumor expresses MAGE-1 either with the antigenic peptide or with autologous antigen-presenting cells pulsed with the peptide.

Monetary unions and the transaction cost savings of a single currency

This paper studies the transaction cost savings of moving froma multi-currency exchange system to a single currency one. Theanalysis concentrates exclusively on the transaction andprecautionary demand for money and abstracts from any othermotives to hold currency. A continuous-time, stochastic Baumol-like model similar to that in Frenkel and Jovanovic (1980) isgeneralized to include several currencies and calibrated to fitEuropean data. The analysis implies an upper bound for thesavings associated with reductions of transaction costs derivedfrom the European Monetary Union of approximately 0.6\% of theCommunity GDP. Additionally, the magnitudes of the brokeragefee and the volatility of transactions, whose estimation hastraditionally been difficult to address empirically, areapproximated for Europe.

La evaluación del gasto sanitario en España en sus niveles y determinantes

El análisis empírico del presente trabajo trata de evaluar los resultados asociables al gasto sanitario español y como consecuencia calibrar las distintas orientaciones para el crecimiento futuro del gasto sanitario público. A la vista del estudio de los potenciales impactos procedente de las variaciones de gasto en los márgenes sobre los niveles actuales de recursos, se trataría de valorar los siguientes objetivos: a) Las mejoras de eficiencia (performance) del sistema sanitario en su conjunto; b) La mejor consecución de cotas de equidad (tanto en contribuciones finacieras como en el acceso a prestaciones); c) El incremento de la capacidad de respuesta por parte de la oferta y del dispositivo asistencial del sistema a las necesidades percibidas (índice de responsiveness).Se trata de aproximar con ello la valoración de cuantos recursos se requerirían para cerrar la brecha entre los niveles de gasto observados en España y las mejores prácticas observadas de la muestra estimada. Ello equivale a la cuantificación del gasto sanitario adicional necesario por punto marginal de ganancia en el valor de los indicadores observados, de acuerdo con los valores estimados en el análisis empírico.Para la definición de las potencialidades en resultados, la aproximación no paramétrica del análisis envolvente de datos (AED, Data Envelopment Analysis) es particularmente apropiada para ello. El objetivo es evaluar distancias para los valores españoles respecto del benchmarking derivado de la estimación, y cuantificar con ello el coste de cierre de brecha óptimo (entre los outputs de dimensiones múltiples considerados y los diversos recursos puestos a disposición del sistema sanitario).