Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data.

Despite the central role of quantitative PCR (qPCR) in the quantification of mRNA transcripts, most analyses of qPCR data are still delegated to the software that comes with the qPCR apparatus. This is especially true for the handling of the fluorescence baseline. This article shows that baseline estimation errors are directly reflected in the observed PCR efficiency values and are thus propagated exponentially in the estimated starting concentrations as well as 'fold-difference' results. Because of the unknown origin and kinetics of the baseline fluorescence, the fluorescence values monitored in the initial cycles of the PCR reaction cannot be used to estimate a useful baseline value. An algorithm that estimates the baseline by reconstructing the log-linear phase downward from the early plateau phase of the PCR reaction was developed and shown to lead to very reproducible PCR efficiency values. PCR efficiency values were determined per sample by fitting a regression line to a subset of data points in the log-linear phase. The variability, as well as the bias, in qPCR results was significantly reduced when the mean of these PCR efficiencies per amplicon was used in the calculation of an estimate of the starting concentration per sample.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Missing value imputation in longitudinal measures of alcohol consumption

Attrition in longitudinal studies can lead to biased results. The study is motivated by the unexpected observation that alcohol consumption decreased despite increased availability, which may be due to sample attrition of heavy drinkers. Several imputation methods have been proposed, but rarely compared in longitudinal studies of alcohol consumption. The imputation of consumption level measurements is computationally particularly challenging due to alcohol consumption being a semi-continuous variable (dichotomous drinking status and continuous volume among drinkers), and the non-normality of data in the continuous part. Data come from a longitudinal study in Denmark with four waves (2003-2006) and 1771 individuals at baseline. Five techniques for missing data are compared: Last value carried forward (LVCF) was used as a single, and Hotdeck, Heckman modelling, multivariate imputation by chained equations (MICE), and a Bayesian approach as multiple imputation methods. Predictive mean matching was used to account for non-normality, where instead of imputing regression estimates, "real" observed values from similar cases are imputed. Methods were also compared by means of a simulated dataset. The simulation showed that the Bayesian approach yielded the most unbiased estimates for imputation. The finding of no increase in consumption levels despite a higher availability remained unaltered. Copyright (C) 2011 John Wiley & Sons, Ltd.

Metodologies de Desenvolupament Dirigides per Models

En aquest treball, s'introduiran dos de les metodologies de desenvolupament dirigides per models més significatives: Model Driven Architecture (MDA) i Domain Specific Modeling (DSM). Així mateix, es presentarà un estudi comparatiu d'algunes de les diferents eines existents actualment al mercat que els hi donen suport.

Quantitative genetic modeling and inference in the presence of nonignorable missing data.

Natural selection is typically exerted at some specific life stages. If natural selection takes place before a trait can be measured, using conventional models can cause wrong inference about population parameters. When the missing data process relates to the trait of interest, a valid inference requires explicit modeling of the missing process. We propose a joint modeling approach, a shared parameter model, to account for nonrandom missing data. It consists of an animal model for the phenotypic data and a logistic model for the missing process, linked by the additive genetic effects. A Bayesian approach is taken and inference is made using integrated nested Laplace approximations. From a simulation study we find that wrongly assuming that missing data are missing at random can result in severely biased estimates of additive genetic variance. Using real data from a wild population of Swiss barn owls Tyto alba, our model indicates that the missing individuals would display large black spots; and we conclude that genes affecting this trait are already under selection before it is expressed. Our model is a tool to correctly estimate the magnitude of both natural selection and additive genetic variance.

Models i mètriques d'estimació d'esforç per la gestió de projectes informàtics.

L'objectiu principal d'aquest treball ha estat buscar i valorar les diferents opcions que hi ha en la gestió de projectes per a estimar-ne els costos relacionats. La primera de les tasques que s'ha fet és definir el terme 'projecte' per esbrinar quins tipus de projectes són susceptibles de ser valorats; aquesta tipificació ha portat a dividir els projectes en dues branques molt diferenciades, tot i que relacionades, durant el seu desenvolupament.

The effect of patient, provider and financing regulations on the intensity of ambulatory physical therapy episodes : a multilevel analysis based on routinely available data

Background: Many studies have found considerable variations in the resource intensity of physical therapy episodes. Although they have identified several patient-and provider-related factors, few studies have examined their relative explanatory power. We sought to quantify the contribution of patients and providers to these differences and examine how effective Swiss regulations are (nine-session ceiling per prescription and bonus for first treatments). Methods: Our sample consisted of 87,866 first physical therapy episodes performed by 3,365 physiotherapists based on referrals by 6,131 physicians. We modeled the number of visits per episode using a multilevel log linear regression with crossed random effects for physiotherapists and physicians and with fixed effects for cantons. The three-level explanatory variables were patient, physiotherapist and physician characteristics. Results: The median number of sessions was nine (interquartile range 6-13). Physical therapy use increased with age, women, higher health care costs, lower deductibles, surgery and specific conditions. Use rose with the share of nine-session episodes among physiotherapists or physicians, but fell with the share of new treatments. Geographical area had no influence. Most of the variance was explained at the patient level, but the available factors explained only 4% thereof. Physiotherapists and physicians explained only 6% and 5% respectively of the variance, although the available factors explained most of this variance. Regulations were the most powerful factors. Conclusion: Against the backdrop of abundant physical therapy supply, Swiss financial regulations did not restrict utilization. Given that patient-related factors explained most of the variance, this group should be subject to closer scrutiny. Moreover, further research is needed on the determinants of patient demand.

Anàlisi de models d'ensenyament-aprenentatge de competències no tècniques als estudis d'Enginyeria en informàtica

Actualment a l'Estat espanyol s'està implantant el Pla Bolonya per incorporar-se a l'Espai Europeu d'Estudis Superiors (l'EEES). Com a un dels principals objectius, l'EEES pretén homogeneïtzar els estudis i de manera concreta les competències adquirides per qualsevol estudiant independentment d'on hagi realitzat els seus estudis. Per això, existeixen iniciatives europees (com el projecte Tuning) que treballen per definir competències per a totes les titulacions universitàries.El projecte presenta l'anàlisi realitzat sobre vint Universitats de diferents continents per identificar models d'ensenyament-aprenentatge de competències no tècniques. La recerca es centra addicionalment en la competència comunicativa escrita.La font principal de dades ha estat la informació proporcionada a les pàgines Web de les universitats i molt especialment els seus plans d'estudi.

Extremal properties of certain risk models

Cette thèse s'intéresse à étudier les propriétés extrémales de certains modèles de risque d'intérêt dans diverses applications de l'assurance, de la finance et des statistiques. Cette thèse se développe selon deux axes principaux, à savoir: Dans la première partie, nous nous concentrons sur deux modèles de risques univariés, c'est-à- dire, un modèle de risque de déflation et un modèle de risque de réassurance. Nous étudions le développement des queues de distribution sous certaines conditions des risques commun¬s. Les principaux résultats sont ainsi illustrés par des exemples typiques et des simulations numériques. Enfin, les résultats sont appliqués aux domaines des assurances, par exemple, les approximations de Value-at-Risk, d'espérance conditionnelle unilatérale etc. La deuxième partie de cette thèse est consacrée à trois modèles à deux variables: Le premier modèle concerne la censure à deux variables des événements extrême. Pour ce modèle, nous proposons tout d'abord une classe d'estimateurs pour les coefficients de dépendance et la probabilité des queues de distributions. Ces estimateurs sont flexibles en raison d'un paramètre de réglage. Leurs distributions asymptotiques sont obtenues sous certaines condi¬tions lentes bivariées de second ordre. Ensuite, nous donnons quelques exemples et présentons une petite étude de simulations de Monte Carlo, suivie par une application sur un ensemble de données réelles d'assurance. L'objectif de notre deuxième modèle de risque à deux variables est l'étude de coefficients de dépendance des queues de distributions obliques et asymétriques à deux variables. Ces distri¬butions obliques et asymétriques sont largement utiles dans les applications statistiques. Elles sont générées principalement par le mélange moyenne-variance de lois normales et le mélange de lois normales asymétriques d'échelles, qui distinguent la structure de dépendance de queue comme indiqué par nos principaux résultats. Le troisième modèle de risque à deux variables concerne le rapprochement des maxima de séries triangulaires elliptiques obliques. Les résultats théoriques sont fondés sur certaines hypothèses concernant le périmètre aléatoire sous-jacent des queues de distributions. -- This thesis aims to investigate the extremal properties of certain risk models of interest in vari¬ous applications from insurance, finance and statistics. This thesis develops along two principal lines, namely: In the first part, we focus on two univariate risk models, i.e., deflated risk and reinsurance risk models. Therein we investigate their tail expansions under certain tail conditions of the common risks. Our main results are illustrated by some typical examples and numerical simu¬lations as well. Finally, the findings are formulated into some applications in insurance fields, for instance, the approximations of Value-at-Risk, conditional tail expectations etc. The second part of this thesis is devoted to the following three bivariate models: The first model is concerned with bivariate censoring of extreme events. For this model, we first propose a class of estimators for both tail dependence coefficient and tail probability. These estimators are flexible due to a tuning parameter and their asymptotic distributions are obtained under some second order bivariate slowly varying conditions of the model. Then, we give some examples and present a small Monte Carlo simulation study followed by an application on a real-data set from insurance. The objective of our second bivariate risk model is the investigation of tail dependence coefficient of bivariate skew slash distributions. Such skew slash distributions are extensively useful in statistical applications and they are generated mainly by normal mean-variance mixture and scaled skew-normal mixture, which distinguish the tail dependence structure as shown by our principle results. The third bivariate risk model is concerned with the approximation of the component-wise maxima of skew elliptical triangular arrays. The theoretical results are based on certain tail assumptions on the underlying random radius.

Beclin 1 mitigates motor and neuropathological deficits in genetic mouse models of Machado-Joseph disease.

Machado-Joseph disease or spinocerebellar ataxia type 3, the most common dominantly-inherited spinocerebellar ataxia, results from translation of the polyglutamine-expanded and aggregation prone ataxin 3 protein. Clinical manifestations include cerebellar ataxia and pyramidal signs and there is no therapy to delay disease progression. Beclin 1, an autophagy-related protein and essential gene for cell survival, is decreased in several neurodegenerative disorders. This study aimed at evaluating if lentiviral-mediated beclin 1 overexpression would rescue motor and neuropathological impairments when administered to pre- and post-symptomatic lentiviral-based and transgenic mouse models of Machado-Joseph disease. Beclin 1-mediated significant improvements in motor coordination, balance and gait with beclin 1-treated mice equilibrating longer periods in the Rotarod and presenting longer and narrower footprints. Furthermore, in agreement with the improvements observed in motor function beclin 1 overexpression prevented neuronal dysfunction and neurodegeneration, decreasing formation of polyglutamine-expanded aggregates, preserving Purkinje cell arborization and immunoreactivity for neuronal markers. These data show that overexpression of beclin 1 in the mouse cerebellum is able to rescue and hinder the progression of motor deficits when administered to pre- and post-symptomatic stages of the disease.

Validation of Logistic Regression Models for Landslide Susceptibility Maps

A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Three Gorges Reservoir region of China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. The validation analysis is exploited to investigate the quality of mapping.

Business Models in OER, a Contingency Approach

We will present an analysis of data from a literature review and semi-structured interviews with experts on OER, to identify different aspects of OER business models and to establish how the success of the OER initiatives is measured. The results collected thus far show that two different business models for OER initiatives exist, but no data on their success or failure is published. We propose a framework for measuring success of OER initiatives.

A discrete forest in a continuous landscape : investigating interactions between natural resource management and sustainable development

Summary Forests are key ecosystems of the earth and associated with a large range of functions. Many of these functions are beneficial to humans and are referred to as ecosystem services. Sustainable development requires that all relevant ecosystem services are quantified, managed and monitored equally. Natural resource management therefore targets the services associated with ecosystems. The main hypothesis of this thesis is that the spatial and temporal domains of relevant services do not correspond to a discrete forest ecosystem. As a consequence, the services are not quantified, managed and monitored in an equal and sustainable manner. The thesis aims were therefore to test this hypothesis, establish an improved conceptual approach and provide spatial applications for the relevant land cover and structure variables. The study was carried out in western Switzerland and based primarily on data from a countrywide landscape inventory. This inventory is part of the third Swiss national forest inventory and assesses continuous landscape variables based on a regular sampling of true colour aerial imagery. In addition, land cover variables were derived from Landsat 5 TM passive sensor data and land structure variables from active sensor data from a small footprint laserscanning system. The results confirmed the main hypothesis, as relevant services did not scale well with the forest ecosystem. Instead, a new conceptual approach for sustainable management of natural resources was described. This concept quantifies the services as a continuous function of the landscape, rather than a discrete function of the forest ecosystem. The explanatory landscape variables are therefore called continuous fields and the forest becomes a dependent and function-driven management unit. Continuous field mapping methods were established for land cover and structure variables. In conclusion, the discrete forest ecosystem is an adequate planning and management unit. However, monitoring the state of and trends in sustainability of services requires them to be quantified as a continuous function of the landscape. Sustainable natural resource management iteratively combines the ecosystem and gradient approaches. Résumé Les forêts sont des écosystèmes-clés de la terre et on leur attribue un grand nombre de fonctions. Beaucoup de ces fonctions sont bénéfiques pour l'homme et sont nommées services écosystémiques. Le développement durable exige que ces services écosystémiques soient tous quantifiés, gérés et surveillés de façon égale. La gestion des ressources naturelles a donc pour cible les services attribués aux écosystèmes. L'hypothèse principale de cette thèse est que les domaines spatiaux et temporels des services attribués à la forêt ne correspondent pas à un écosystème discret. Par conséquent, les services ne sont pas quantifiés, aménagés et surveillés d'une manière équivalente et durable. Les buts de la thèse étaient de tester cette hypothèse, d'établir une nouvelle approche conceptuelle de la gestion des ressources naturelles et de préparer des applications spatiales pour les variables paysagères et structurelles appropriées. L'étude a été menée en Suisse occidentale principalement sur la base d'un inventaire de paysage à l'échelon national. Cet inventaire fait partie du troisième inventaire forestier national suisse et mesure de façon continue des variables paysagères sur la base d'un échantillonnage régulier sur des photos aériennes couleur. En outre, des variables de couverture ? terrestre ont été dérivées des données d'un senseur passif Landsat 5 TM, ainsi que des variables structurelles, dérivées du laserscanning, un senseur actif. Les résultats confirment l'hypothèse principale, car l'échelle des services ne correspond pas à celle de l'écosystème forestier. Au lieu de cela, une nouvelle approche a été élaborée pour la gestion durable des ressources naturelles. Ce concept représente les services comme une fonction continue du paysage, plutôt qu'une fonction discrète de l'écosystème forestier. En conséquence, les variables explicatives de paysage sont dénommées continuous fields et la forêt devient une entité dépendante, définie par la fonction principale du paysage. Des méthodes correspondantes pour la couverture terrestre et la structure ont été élaborées. En conclusion, l'écosystème forestier discret est une unité adéquate pour la planification et la gestion. En revanche, la surveillance de la durabilité de l'état et de son évolution exige que les services soient quantifiés comme fonction continue du paysage. La gestion durable des ressources naturelles joint donc l'approche écosystémique avec celle du gradient de manière itérative.

Intermediary and structural determinants of early childhood health in Colombia: exploring the role of communities

This study examines how structural determinants influence intermediary factors of child health inequities and how they operate through the communities where children live. In particular, we explore individual, family and community level characteristics associated with a composite indicator that quantitatively measures intermediary determinants of early childhood health in Colombia. We use data from the 2010 Colombian Demographic and Health Survey (DHS). Adopting the conceptual framework of the Commission on Social Determinants of Health (CSDH), three dimensions related to child health are represented in the index: behavioural factors, psychosocial factors and health system. In order to generate the weight of the variables and take into account the discrete nature of the data, principal component analysis (PCA) using polychoric correlations are employed in the index construction. Weighted multilevel models are used to examine community effects. The results show that the effect of household’s SES is attenuated when community characteristics are included, indicating the importance that the level of community development may have in mediating individual and family characteristics. The findings indicate that there is a significant variance in intermediary determinants of child health between-community, especially for those determinants linked to the health system, even after controlling for individual, family and community characteristics. These results likely reflect that whilst the community context can exert a greater influence on intermediary factors linked directly to health, in the case of psychosocial factors and the parent’s behaviours, the family context can be more important. This underlines the importance of distinguishing between community and family intervention programmes.

Peripheral inflammation increases the damage in animal models of nigrostriatal dopaminergic neurodegeneration: possible implication in Parkinson's disease incidence.

Inflammatory processes described in Parkinson’s disease (PD) and its animal models appear to be important in the progression of the pathogenesis, or even a triggering factor. Here we review that peripheral inflammation enhances the degeneration of the nigrostriatal dopaminergic system induced by different insults; different peripheral inflammations have been used, such as IL-1β and the ulcerative colitis model, as well as insults to the dopaminergic system such as 6-hydroxydopamine or lipopolysaccharide. In all cases, an increased loss of dopaminergic neurons was described; inflammation in the substantia nigra increased, displaying a great activation of microglia along with an increase in the production of cytokines such as IL-1β and TNF-α. Increased permeability or disruption of the BBB, with overexpression of the ICAM-1 adhesion molecule and infiltration of circulating monocytes into the substantia nigra, is also involved, since the depletion of circulating monocytes prevents the effects of peripheral inflammation. Data are reviewed in relation to epidemiological studies of PD.