Screening premorbid metabolic syndrome in community pharmacies: a cross-sectional descriptive study

Background: Premorbid metabolic syndrome (pre-MetS) is a cluster of cardiometabolic risk factors characterised by central obesity, elevated fasting glucose, atherogenic dyslipidaemia and hypertension without established cardiovascular disease or diabetes. Community pharmacies are in an excellent position to develop screening programmes because of their direct contact with the population. The main aim of the study was to determine the prevalence of pre-MetS in people who visited community pharmacies for measurement of any of its five risk factors to detect the presence of other risk factors. The secondary aims were to study the presence of other cardiovascular risk factors and determine patients" cardiovascular risk. Methods: Cross-sectional, descriptive, multicentre study. Patients meeting selection criteria aged between 18 and 65 years who visited participating community pharmacies to check any of five pre-MetS diagnostic factors were included. The study involved 23 community pharmacies in Catalonia (Spain). Detection criteria for pre-MetS were based on the WHO proposal following IDF and AHA/NHBI consensus. Cardiovascular risk (CVR) was calculated by Regicor and Score methods. Other variables studied were smoking habit, physical activity, body mass index (BMI), and pharmacological treatment of dyslipidemia and hypertension. The data were collected and analysed with the SPSS programme. Comparisons of variables were carried out using the Student"s T-test, Chi-Squared test or ANOVA test. Level of significance was 5% (0.05). Results: The overall prevalence of pre-MetS was 21.9% [95% CI 18.7-25.2]. It was more prevalent in men, 25.5% [95% CI 22.1-28.9], than in women, 18.6% [95% CI 15.5-21.7], and distribution increased with age. The most common risk factors were high blood pressure and abdominal obesity. About 70% of people with pre-MetS were sedentary and over 85% had a BMI ≥25 Kg/m2 . Some 22.4% had two metabolic criteria and 27.2% of patients with pre-MetS had no previous diagnosis. Conclusions: The prevalence of pre-MetS in our study (21.9%) was similar to that found in other studies carried out in Primary Care in Spain. The results of this study confirm emergent cardiometabolic risk factors such as hypertension, obesity and physical inactivity. Our study highlights the strategic role of the community pharmacy in the detection of pre-MetS in the apparently healthy population.

Tools and data services registry: a community effort to document bioinformatics resources.

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.

A Social Network Analysis of Twitter: Mapping the Digital Humanities Community

Defining digital humanities might be an endless debate if we stick to the discussion about the boundaries of this concept as an academic "discipline". In an attempt to concretely identify this field and its actors, this paper shows that it is possible to analyse them through Twitter, a social media widely used by this "community of practice". Based on a network analysis of 2,500 users identified as members of this movement, the visualisation of the "who's following who?" graph allows us to highlight the structure of the network's relationships, and identify users whose position is particular. Specifically, we show that linguistic groups are key factors to explain clustering within a network whose characteristics look similar to a small world.

Interdisciplinary Medication Adherence Program: The Example of a University Community Pharmacy in Switzerland.

The Community Pharmacy of the Department of Ambulatory Care and Community Medicine (Policlinique Médicale Universitaire, PMU), University of Lausanne, developed and implemented an interdisciplinary medication adherence program. The program aims to support and reinforce medication adherence through a multifactorial and interdisciplinary intervention. Motivational interviewing is combined with medication adherence electronic monitors (MEMS, Aardex MWV) and a report to patient, physician, nurse, and other pharmacists. This program has become a routine activity and was extended for use with all chronic diseases. From 2004 to 2014, there were 819 patient inclusions, and 268 patients were in follow-up in 2014. This paper aims to present the organization and program's context, statistical data, published research, and future perspectives.

Building an open social learning community around a Dspace repository on Statistics

LORs, addressing content management and preservation, have the positive collaterals of institutional positioning and dissemination, but their main benefit is the empowerment of interest-centred learning communities, as we recognise that learning is much more than content, which becomes infrastructure: the LOR provides the learner interaction with the LOs, but also with other learners and teachers.

On Variational Ensemble Kalman Filtering

The Extended Kalman Filter (EKF) and four dimensional assimilation variational method (4D-VAR) are both advanced data assimilation methods. The EKF is impractical in large scale problems and 4D-VAR needs much effort in building the adjoint model. In this work we have formulated a data assimilation method that will tackle the above difficulties. The method will be later called the Variational Ensemble Kalman Filter (VEnKF). The method has been tested with the Lorenz95 model. Data has been simulated from the solution of the Lorenz95 equation with normally distributed noise. Two experiments have been conducted, first with full observations and the other one with partial observations. In each experiment we assimilate data with three-hour and six-hour time windows. Different ensemble sizes have been tested to examine the method. There is no strong difference between the results shown by the two time windows in either experiment. Experiment I gave similar results for all ensemble sizes tested while in experiment II, higher ensembles produce better results. In experiment I, a small ensemble size was enough to produce nice results while in experiment II the size had to be larger. Computational speed is not as good as we would want. The use of the Limited memory BFGS method instead of the current BFGS method might improve this. The method has proven succesful. Even if, it is unable to match the quality of analyses of EKF, it attains significant skill in forecasts ensuing from the analysis it has produced. It has two advantages over EKF; VEnKF does not require an adjoint model and it can be easily parallelized.

Childhood community-acquired pneumonia

Background: Community-acquired pneumonia is a leading cause of morbidity and mortality in children worldwide. New, rapid methods are needed to improve the microbiologic diagnosis of pneumonia in clinical practice. The increasing incidence of parapneumonic empyema in children accentuates the importance of the identification of the causative agent and clinical predictors of empyema. Aims and methods: Two prospective studies were conducted to find feasible diagnostic methods for the detection of causative agents of pneumonia. The usefulness of pneumolysin-targeted real-time PCR in the diagnosis of pneumococcal disease was studied in children with pneumonia and empyema, and the clinical utility of induced sputum analysis in the microbiologic diagnosis of pneumonia was investigated in children with pneumonia. In addition, two retrospective clinical studies were performed to describe the frequency and clinical profile of influenza pneumonia in children and the frequency, clinical profile and clinical predictors of empyema in children. Results: Pneumolysin-PCR in pleural fluid significantly improved the microbiologic diagnosis of empyema by increasing the detection rate of pneumococcus almost tenfold to that of pleural fluid culture (75 % vs. 8 %). In whole blood samples, PCR detected pneumococcus in only one child with pneumonia and one child with pneumococcal empyema. Sputum induction provided good-quality sputum specimens with high microbiologic yield. Streptococcus pneumoniae (46 %) and rhinovirus (29 %) were the most common microbes detected. The quantification results of the paired sputum and nasopharyngeal aspirate specimens provided support that the majority of the bacteria (79 %) and viruses (55 %) found in sputum originated from the lower airways. Pneumonia was detected in 14 % of children with influenza infection. A history of prolonged duration of fever, tachypnea, and pain on abdominal palpation were found to be independently significant predictors of empyema. Conclusions: Pneumolysin-targeted real-time PCR is a useful and rapid method for the diagnosis of pneumococcal empyema in children. Induced sputum analysis with paired nasopharyngeal aspirate analysis can be of clinical value in the microbiologic diagnosis of pneumonia. Influenza pneumonia is an infrequent and generally benign disease in children with rare fatalities. Repeat chest radiograph and ultrasound imaging are recommended in children with pneumonia presenting with clinical predictors of empyema and in children with persistent fever and high CRP levels during hospitalization.

La gestión de la reputación on-line por parte de las compañías hoteleras : El papel del community manager

Mi trabajo de disertación se desarrolla en el contexto del Grado de Comunicación de la UOC y la finalidad es establecer un marco teórico actualizado sobre el concepto de reputación on-line aplicable al sector hotelero. En este sentido entiendo necesario partir del análisis de tres hoteles de Palma de Mallorca para obtener un diagnóstico de reputación y poderafirmar que existe una relación entre su reputación on-line y la obtención de mayores ingresos. Por este motivo, llevaré a cabo un estudio de caso comparativo con la herramienta de monitorización SocialVane del Hotel Gran Meliá Victoria con sus competidores más directos.

Authentication and authorization service for a community network

Wireless community networks became popular in uniting people with common interests. This thesis presents authentication and authorization service for a wireless community network using captive portal approach including ability to authenticate clients from associated networks thereby combining multiple communities in a syndicate. The system is designed and implemented to be reliable, scalable and flexible. Moreover, the result includes software management system, which automatically performs software updates at network’s access points. Future development of the system can be concentrated on an improvement of the software management system.

The use of COOP/WONCA charts as a screen for mental disorders in the immigrant community in primary care in primary care

Aim. To evaluate the usefulness of COOP/WONCA charts as a screening tool for mental disorders in primary care in the immigrant healthcare users in Salt. To measure self-rated health of Salt immigration population using the COOP / WONCA charts and to assess its associated factorsDesign. Descriptive and transversal study, Participants. 370 non-EU immigrants seniors selected by consecutive sampling stratified by sexMain measures. Personal information will be collected (age, sex, country of origin, years of residency in Spain, number of people living in the household and associated comorbidities). Each participant will complete the COOP/WONCA charts. An analysis of the validity of the diagnostic test will be done: sensibility, specificity, positive predictive value, negative predictive value, ROC curve and area under the curve (AUC). All variables will be subjected to descriptive analysis. Bivariate and multivariate analysis between the variables collected (sex, years of residency in Spain... ) and the results of COOP / WONCA charts will be performedResults. Preliminary results are available on a pilot test with 30 patients. The mental disorder prevalence is around 30%. Sensibility (0,89), specificity (0,89), VPP (0,80), VPN (0,94) cutoff score (3.5) and AUC (0,941). Women, people with 10 or more years of residency in Spain and unemployed people have worse self-rated healthConclusions. Based on the preliminary results, is possible to conclude that COOP/WONCA charts could be an useful, valid and applicable screening test for mental disorders in primary care with immigrant population

Online Community System Development, Case PROFCOM

The purpose of this bachelor's thesis is the development of online community. Nowadays Internet lets user to collaborate and share information online. Internet is also full of communities and the number of community users is continuously rising. Companies have also noticed this and want to make use of it. The result of the work was an online community for the use of PROFCOM research project. At the same time information was gathered about what kind of platforms are available as a backbone for an online community. Designing and developing of the online community provided experience about Drupal-environment. It also gave pros and cons of Drupal’s features. Drupal is a multifunctional software, which can handle big online communities, but its installation and maintenance is, however, reasonably simple.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Kalman Filtering in Online Paper Quality Assessment

Controlling the quality variables (such as basis weight, moisture etc.) is a vital part of making top quality paper or board. In this thesis, an advanced data assimilation tool is applied to the quality control system (QCS) of a paper or board machine. The functionality of the QCS is based on quality observations that are measured with a traversing scanner making a zigzag path. The basic idea is the following: The measured quality variable has to be separated into its machine direction (MD) and cross direction (CD) variations due to the fact that the QCS works separately in MD and CD. Traditionally this is done simply by assuming one scan of the zigzag path to be the CD profile and its mean value to be one point of the MD trend. In this thesis, a more advanced method is introduced. The fundamental idea is to use the signals’ frequency components to represent the variation in both CD and MD. To be able to get to the frequency domain, the Fourier transform is utilized. The frequency domain, that is, the Fourier components are then used as a state vector in a Kalman filter. The Kalman filter is a widely used data assimilation tool to combine noisy observations with a model. The observations here refer to the quality measurements and the model to the Fourier frequency components. By implementing the two dimensional Fourier transform into the Kalman filter, we get an advanced tool for the separation of CD and MD components in total variation or, to be more general, for data assimilation. A piece of a paper roll is analyzed and this tool is applied to model the dataset. As a result, it is clear that the Kalman filter algorithm is able to reconstruct the main features of the dataset from a zigzag path. Although the results are made with a very short sample of paper roll, it seems that this method has great potential to be used later on as a part of the quality control system.

Effects of Temperature, Salinity and Fish in Structuring the Macroinvertebrate Community in Shallow Lakes: Implications for Effects of Climate Change

Climate warming may lead to changes in the trophic structure and diversity of shallow lakes as a combined effect of increased temperature and salinity and likely increased strength of trophic interactions. We investigated the potential effects of temperature, salinity and fish on the plant-associated macroinvertebrate community by introducing artificial plants in eight comparable shallow brackish lakes located in two climatic regions of contrasting temperature: cold-temperate and Mediterranean. In both regions, lakes covered a salinity gradient from freshwater to oligohaline waters. We undertook day and night-time sampling of macroinvertebrates associated with the artificial plants and fish and free-swimming macroinvertebrate predators within artificial plants and in pelagic areas. Our results showed marked differences in the trophic structure between cold and warm shallow lakes. Plant-associated macroinvertebrates and free-swimming macroinvertebrate predators were more abundant and the communities richer in species in the cold compared to the warm climate, most probably as a result of differences in fish predation pressure. Submerged plants in warm brackish lakes did not seem to counteract the effect of fish predation on macroinvertebrates to the same extent as in temperate freshwater lakes, since small fish were abundant and tended to aggregate within the macrophytes. The richness and abundance of most plant-associated macroinvertebrate taxa decreased with salinity. Despite the lower densities of plant-associated macroinvertebrates in the Mediterranean lakes, periphyton biomass was lower than in cold temperate systems, a fact that was mainly attributed to grazing and disturbance by fish. Our results suggest that, if the current process of warming entails higher chances of shallow lakes becoming warmer and more saline, climatic change may result in a decrease in macroinvertebrate species richness and abundance in shallow lakes