Trends in self-reported prevalence and management of hypertension, dyslipidaemia and diabetes in adults in Switzerland, 1992-2007

Purpose: to assess the trends of self-reported prevalence of cardiovascular risk factors (CV RFs: hypertension, dyslipidaemia, diabetes) and their management for period 1992 to 2007 in the Swiss population. Methods: four National health interview surveys conducted between 1992 and 2007 in representative samples of the Swiss population (63,782 subjects overall). Self-reported CV RFs prevalence, treatment and controllevels were computed after weighting. Weights were calculated by raking ratio such that the marginal distribution of the weighted totals conforms to the marginal distribution of the targeted population. Multivariate analysis adjusted on age, sex, education, nationality and SMI was conducted using logistic regression. Results: prevalence of ail CV RFs increased between 1992 and 2007, see table. Although the self-reported prevalence of treatment among subjects with CV RFs increased, and this was confirmed by multivariate analysis: OR for hypocholesterolaemic treatment relative to 1992: 0.64 [0.52-0.78]; 1.39 [1.18-1.65] and 2.00 [1.69-2.36] for 1997, 2002 and 2007, respectively. Still, in 2007, circa 40% of hypertensive, 60% of dyslipidaemic and 50% of diabetic subjects weren't treated. Conversely, an adequate control of CV RFs was reported by treated subjects, with an increase during the study period. This increase was confirmed by multivariate analysis (not shown). Conclusion: the self-reported prevalence of hypertension, dyslipidaemia and diabetes increased between 1992 and 2007 in the Swiss population. Despite a good control of treated subjects, still a significant percentage of subjects with CV RFs are not treated.

Novel MLPA procedure using self-designed probes allows comprehensive analysis for CNVs of the genes involved in Hirschsprung disease.

Background: Hirschsprung disease is characterized by the absence of intramural ganglion cells in the enteric plexuses, due to a fail during enteric nervous system formation. Hirschsprung has a complex genetic aetiology and mutations in several genes have been related to the disease. There is a clear predominance of missense/nonsense mutations in these genes whereas copy number variations (CNVs) have been seldom described, probably due to the limitations of conventional techniques usually employed for mutational analysis. In this study, we have looked for CNVs in some of the genes related to Hirschsprung (EDNRB, GFRA1, NRTN and PHOX2B) using the Multiple Ligation-dependent Probe Amplification (MLPA) approach. Methods: CNVs screening was performed in 208 HSCR patients using a self-designed set of MLPA probes, covering the coding region of those genes. Results: A deletion comprising the first 4 exons in GFRA1 gene was detected in 2 sporadic HSCR patients and in silico approaches have shown that the critical translation initiation signal in the mutant gene was abolished. In this study, we have been able to validate the reliability of this technique for CNVs screening in HSCR. Conclusions: The implemented MLPA based technique presented here allows CNV analysis of genes involved in HSCR that have not been not previously evaluated. Our results indicate that CNVs could be implicated in the pathogenesis of HSCR, although they seem to be an uncommon molecular cause of HSCR.

Effectiveness of two interventions based on improving patient-practitioner communication on diabetes self-management in patients with low educational level: study protocol of a clustered randomized trial in primary care.

BACKGROUND In the last decades the presence of social inequalities in diabetes care has been observed in multiple countries, including Spain. These inequalities have been at least partially attributed to differences in diabetes self-management behaviours. Communication problems during medical consultations occur more frequently to patients with a lower educational level. The purpose of this cluster randomized trial is to determine whether an intervention implemented in a General Surgery, based in improving patient-provider communication, results in a better diabetes self-management in patients with lower educational level. A secondary objective is to assess whether telephone reinforcement enhances the effect of such intervention. We report the design and implementation of this on-going study. METHODS/DESIGN The study is being conducted in a General Practice located in a deprived neighbourhood of Granada, Spain. Diabetic patients 18 years old or older with a low educational level and inadequate glycaemic control (HbA1c > 7%) were recruited. General Practitioners (GPs) were randomised to three groups: intervention A, intervention B and control group. GPs allocated to intervention groups A and B received training in communication skills and are providing graphic feedback about glycosylated haemoglobin levels. Patients whose GPs were allocated to group B are additionally receiving telephone reinforcement whereas patients from the control group are receiving usual care. The described interventions are being conducted during 7 consecutive medical visits which are scheduled every three months. The main outcome measure will be HbA1c; blood pressure, lipidemia, body mass index and waist circumference will be considered as secondary outcome measures. Statistical analysis to evaluate the effectiveness of the interventions will include multilevel regression analysis with three hierarchical levels: medical visit level, patient level and GP level. DISCUSSION The results of this study will provide new knowledge about possible strategies to promote a better diabetes self-management in a particularly vulnerable group. If effective, this low cost intervention will have the potential to be easily incorporated into routine clinical practice, contributing to decrease health inequalities in diabetic patients. TRIAL REGISTRATION Clinical Trials U.S. National Institutes of Health, NCT01849731.

Capitalism in the classroom : the impact of self-enhancement values and normative assessment on student motivation, goals and attitudes towards cheating

Abstract The neo-liberal capitalist ideology has come under heavy fire with anecdotal evidence indicating a link between these same values and unethical behavior. Academic institutions reflect social values and act as socializing agents for the young. Can this explain the high and increasing rates of cheating that currently prevail in education? Our first chapter examines the question of whether self-enhancement values of power and açhievement, the individual level equivalent of neo-liberal capitalist values, predict positive attitudes towards cheating. Furthermore, we explore the mediating role of motivational factors. Results of four studies reveal that self-enhancement value endorsement predicts the adoption of performance-approach goals, a relationship mediated by introjected regulation, namely desire for social approval and that self-enhancement value endorsement also predicts the condoning of cheating, a relationship mediated by performance-approach goal adoption. However, self-transcendence values prescribed by a normatively salient source have the potential to reduce the link between self-enhancement value endorsément and attitudes towards cheating. Normative assessment constitutes a key tool used by academic institutions to socialize young people to accept the competitive, meritocratic nature of a sociéty driven by a neo-liberal capitalist ideology. As such, the manifest function of grades is to motivate students to work hard and to buy into the competing ethos. Does normative assessment fulfill these functions? Our second chapter explores the reward-intrinsic motivation question in the context of grading, arguably a high-stakes reward. In two experiments, the relative capacity of graded high performance as compared to the task autonomy experienced in an ungraded task to predict post-task intrinsic motivation is assessed. Results show that whilst the graded task performance predicts post-task appreciation, it fails to predict ongoing motivation. However, perceived autonomy experienced in non-graded condition, predicts both post-task appreciation and ongoing motivation. Our third chapter asks whether normative assessment inspires the spirit of competition in students. Results of three experimental studies reveal that expectation of a grade for a task, compared to no grade, induces greater adoption of performance-avoidance, but not performance-approach, goals. Experiment 3 provides an explanatory mechanism for this, showing that reduced autonomous motivation experienced in previous graded tasks mediates the relationship between grading and adoption of performance avoidance goals in a subsequent task. The above results, when combined, provide evidence as to the deleterious effects of self enhancement values and the associated practice of normative assessment in school on student motivation, goals and ethics. We conclude by using value and motivation theory to explore solutions to this problem.

Ability for self-care and its association with sociodemographic factors of people with spinal cord injury

This study aimed to verify the association between self-care ability and sociodemographic factors of people with spinal cord injury (SCI). It was a cross-sectional study, conducted in 2012, in all 58 Basic Health Units of Natal/RN, Brazil. Seventy-three subjects completed a sociodemographic form andSelf-Care Agency Scale. Statistical analyses were performed using SPSS,including Cronbach’s Alpha, Chi-square, Fisher’s and contingency coefficient tests. The Cronbach's alpha was 0.788. The result verified that sex (p = 0.028), religion (p <0.001), education (p = 0.046), current age (p = 0.027), SCI time (p = 0.020) and the SCI type (p = 0.012) were variables associated with self-care ability of the subjects. It was concluded that sociodemographic factors may interfere with the self-care ability of persons with SCI, and nurses should consider this aspect during the execution of the nursing process.

Computer-assisted cartography using topographic properties: precision and accuracy of local soil maps in central Mexico

Map units directly related to properties of soil-landscape are generated by local soil classes. Therefore to take into consideration the knowledge of farmers is essential to automate the procedure. The aim of this study was to map local soil classes by computer-assisted cartography (CAC), using several combinations of topographic properties produced by GIS (digital elevation model, aspect, slope, and profile curvature). A decision tree was used to find the number of topographic properties required for digital cartography of the local soil classes. The maps produced were evaluated based on the attributes of map quality defined as precision and accuracy of the CAC-based maps. The evaluation was carried out in Central Mexico using three maps of local soil classes with contrasting landscape and climatic conditions (desert, temperate, and tropical). In the three areas the precision (56 %) of the CAC maps based on elevation as topographical feature was higher than when based on slope, aspect and profile curvature. The accuracy of the maps (boundary locations) was however low (33 %), in other words, further research is required to improve this indicator.

Analysis, modelling and visualisation of spatiotemporal data for urban studies

The proportion of population living in or around cites is more important than ever. Urban sprawl and car dependence have taken over the pedestrian-friendly compact city. Environmental problems like air pollution, land waste or noise, and health problems are the result of this still continuing process. The urban planners have to find solutions to these complex problems, and at the same time insure the economic performance of the city and its surroundings. At the same time, an increasing quantity of socio-economic and environmental data is acquired. In order to get a better understanding of the processes and phenomena taking place in the complex urban environment, these data should be analysed. Numerous methods for modelling and simulating such a system exist and are still under development and can be exploited by the urban geographers for improving our understanding of the urban metabolism. Modern and innovative visualisation techniques help in communicating the results of such models and simulations. This thesis covers several methods for analysis, modelling, simulation and visualisation of problems related to urban geography. The analysis of high dimensional socio-economic data using artificial neural network techniques, especially self-organising maps, is showed using two examples at different scales. The problem of spatiotemporal modelling and data representation is treated and some possible solutions are shown. The simulation of urban dynamics and more specifically the traffic due to commuting to work is illustrated using multi-agent micro-simulation techniques. A section on visualisation methods presents cartograms for transforming the geographic space into a feature space, and the distance circle map, a centre-based map representation particularly useful for urban agglomerations. Some issues on the importance of scale in urban analysis and clustering of urban phenomena are exposed. A new approach on how to define urban areas at different scales is developed, and the link with percolation theory established. Fractal statistics, especially the lacunarity measure, and scale laws are used for characterising urban clusters. In a last section, the population evolution is modelled using a model close to the well-established gravity model. The work covers quite a wide range of methods useful in urban geography. Methods should still be developed further and at the same time find their way into the daily work and decision process of urban planners. La part de personnes vivant dans une région urbaine est plus élevé que jamais et continue à croître. L'étalement urbain et la dépendance automobile ont supplanté la ville compacte adaptée aux piétons. La pollution de l'air, le gaspillage du sol, le bruit, et des problèmes de santé pour les habitants en sont la conséquence. Les urbanistes doivent trouver, ensemble avec toute la société, des solutions à ces problèmes complexes. En même temps, il faut assurer la performance économique de la ville et de sa région. Actuellement, une quantité grandissante de données socio-économiques et environnementales est récoltée. Pour mieux comprendre les processus et phénomènes du système complexe "ville", ces données doivent être traitées et analysées. Des nombreuses méthodes pour modéliser et simuler un tel système existent et sont continuellement en développement. Elles peuvent être exploitées par le géographe urbain pour améliorer sa connaissance du métabolisme urbain. Des techniques modernes et innovatrices de visualisation aident dans la communication des résultats de tels modèles et simulations. Cette thèse décrit plusieurs méthodes permettant d'analyser, de modéliser, de simuler et de visualiser des phénomènes urbains. L'analyse de données socio-économiques à très haute dimension à l'aide de réseaux de neurones artificiels, notamment des cartes auto-organisatrices, est montré à travers deux exemples aux échelles différentes. Le problème de modélisation spatio-temporelle et de représentation des données est discuté et quelques ébauches de solutions esquissées. La simulation de la dynamique urbaine, et plus spécifiquement du trafic automobile engendré par les pendulaires est illustrée à l'aide d'une simulation multi-agents. Une section sur les méthodes de visualisation montre des cartes en anamorphoses permettant de transformer l'espace géographique en espace fonctionnel. Un autre type de carte, les cartes circulaires, est présenté. Ce type de carte est particulièrement utile pour les agglomérations urbaines. Quelques questions liées à l'importance de l'échelle dans l'analyse urbaine sont également discutées. Une nouvelle approche pour définir des clusters urbains à des échelles différentes est développée, et le lien avec la théorie de la percolation est établi. Des statistiques fractales, notamment la lacunarité, sont utilisées pour caractériser ces clusters urbains. L'évolution de la population est modélisée à l'aide d'un modèle proche du modèle gravitaire bien connu. Le travail couvre une large panoplie de méthodes utiles en géographie urbaine. Toutefois, il est toujours nécessaire de développer plus loin ces méthodes et en même temps, elles doivent trouver leur chemin dans la vie quotidienne des urbanistes et planificateurs.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

The French adaptation of the Health of the Nation Outcome Scale for Children and Adolescents Self-Rated Form (F-HoNOSCA-SR): Validation and clinical routine use.

The current study aimed to explore the validity of an adaptation into French of the self-rated form of the Health of the Nation Outcome Scales for Children and Adolescents (F-HoNOSCA-SR) and to test its usefulness in a clinical routine use. One hundred and twenty nine patients, admitted into two inpatient units, were asked to participate in the study. One hundred and seven patients filled out the F-HoNOSCA-SR (for a subsample (N=17): at two occasions, one week apart) and the strengths and difficulties questionnaire (SDQ). In addition, the clinician rated the clinician-rated form of the HoNOSCA (HoNOSCA-CR, N=82). The reliability (assessed with split-half coefficient, item response theory (IRT) models and intraclass correlations (ICC) between the two occasions) revealed that the F-HoNSOCA-SR provides reliable measures. The concurrent validity assessed by correlating the F-HoNOSCA-SR and the SDQ revealed a good convergent validity of the instrument. The relationship analyses between the F-HoNOSCA-SR and the HoNOSCA-CR revealed weak but significant correlations. The comparison between the F-HoNOSCA-SR and the HoNOSCA-CR with paired sample t-tests revealed a higher score for the self-rated version. The F-HoNSOCA-SR was reported to provide reliable measures. In addition, it allows us to measure complementary information when used together with the HoNOSCA-CR.

Clustering of grape yield maps to delineate site-specific management zones

Zonal management in vineyards requires the prior delineation of stable yield zones within the parcel. Among the different methodologies used for zone delineation, cluster analysis of yield data from several years is one of the possibilities cited in scientific literature. However, there exist reasonable doubts concerning the cluster algorithm to be used and the number of zones that have to be delineated within a field. In this paper two different cluster algorithms have been compared (k-means and fuzzy c-means) using the grape yield data corresponding to three successive years (2002, 2003 and 2004), for a ‘Pinot Noir’ vineyard parcel. Final choice of the most recommendable algorithm has been linked to obtaining a stable pattern of spatial yield distribution and to allowing for the delineation of compact and average sized areas. The general recommendation is to use reclassified maps of two clusters or yield classes (low yield zone and high yield zone) and, consequently, the site-specific vineyard management should be based on the prior delineation of just two different zones or sub-parcels. The two tested algorithms are good options for this purpose. However, the fuzzy c-means algorithm allows for a better zoning of the parcel, forming more compact areas and with more equilibrated zonal differences over time.

A note on the periodic orbits and topological entropy of graph maps

This paper deals with the relationship between the periodic orbits of continuous maps on graphs and the topological entropy of the map. We show that the topological entropy of a graph map can be approximated by the entropy of its periodic orbits.

Bio-inspired Mechanisms for Artificial Self-organised Systems

Self-organization is a growing interdisciplinary field of research about a phenomenon that can be observed in the Universe, in Nature and in social contexts. Research on self-organization tries to describe and explain forms, complex patterns and behaviours that arise from a collection of entities without an external organizer. As researchers in artificial systems, our aim is not to mimic self-organizing phenomena arising in Nature, but to understand and to control underlying mechanisms allowing desired emergence of forms, complex patterns and behaviours. Rather than attempting to eliminate such self-organization in artificial systems, we think that this might be deliberately harnessed in order to reach desirable global properties. In this paper we analyze three forms of self-organization: stigmergy, reinforcement mechanisms and cooperation. The amplification phenomena founded in stigmergic process or in reinforcement process are different forms of positive feedbacks that play a major role in building group activity or social organization. Cooperation is a functional form for self-organization because of its ability to guide local behaviours in order to obtain a relevant collective one. For each forms of self-organisation, we present a case study to show how we transposed it to some artificial systems and then analyse the strengths and weaknesses of such an approach

On the Connectivity of the Julia sets of meromorphic functions

We prove that every transcendental meromorphic map $f$ with disconnected Julia set has a weakly repelling fixed point. This implies that the Julia set of Newton's method for finding zeroes of an entire map is connected. Moreover, extending a result of Cowen for holomorphic self-maps of the disc, we show the existence of absorbing domains for holomorphic self-maps of hyperbolic regions, whose iterates tend to a boundary point. In particular, the results imply that periodic Baker domains of Newton's method for entire maps are simply connected, which solves a well-known open question.

From "being to the self" to "become to be": on development of autonomy construction in nursing students

Objective: To understand nursing student's self-consciousness and his/her autonomy in the discipline of fundamentals of professional care in the context of a liberating pedagogical proposal. Methodology. This qualitative, case-based research in the model of Ludke and André involved 14 students participating in the discipline. Data were collected by non-participatory observation and analysis of documents. Field observation was conducted from March to July 2010 and data were collected according to the proposal of Minayo: pre-analysis, exploration of material and treatment of results. Results. We constructed two thematic units of analysis: from "being to the self" and exercise of "become to be". Conclusion. When nursing students feel more liberty, they have the opportunity to substitute the scary prospect of learning something new material to something that motivates their curiosity and leads them to become more autonomous.

Bag-of-Features Approach to Unsupervised Visual Object Categorisation

The large and growing number of digital images is making manual image search laborious. Only a fraction of the images contain metadata that can be used to search for a particular type of image. Thus, the main research question of this thesis is whether it is possible to learn visual object categories directly from images. Computers process images as long lists of pixels that do not have a clear connection to high-level semantics which could be used in the image search. There are various methods introduced in the literature to extract low-level image features and also approaches to connect these low-level features with high-level semantics. One of these approaches is called Bag-of-Features which is studied in the thesis. In the Bag-of-Features approach, the images are described using a visual codebook. The codebook is built from the descriptions of the image patches using clustering. The images are described by matching descriptions of image patches with the visual codebook and computing the number of matches for each code. In this thesis, unsupervised visual object categorisation using the Bag-of-Features approach is studied. The goal is to find groups of similar images, e.g., images that contain an object from the same category. The standard Bag-of-Features approach is improved by using spatial information and visual saliency. It was found that the performance of the visual object categorisation can be improved by using spatial information of local features to verify the matches. However, this process is computationally heavy, and thus, the number of images must be limited in the spatial matching, for example, by using the Bag-of-Features method as in this study. Different approaches for saliency detection are studied and a new method based on the Hessian-Affine local feature detector is proposed. The new method achieves comparable results with current state-of-the-art. The visual object categorisation performance was improved by using foreground segmentation based on saliency information, especially when the background could be considered as clutter.