Análisis del manejo de la información por parte de la Revista Semana respecto al Polo Democrático Alternativo y al Movimiento Progresistas: periodo: 2008-2012

A través del tiempo se ha logrado ver cómo los medios de comunicación penetran cada vez más la forma de pensar, e incluso de actuar, de la gente. Políticamente se han convertido en herramientas de disuasión, alienación o simplemente distracción. En Colombia, revista Semana se ha caracterizado por tener un criterio y una práctica seria del periodismo, en este sentido, vale la pena analizar cómo se ha desempeñado este medio ante el tratamiento de la información frente a los movimientos de izquierda con participación en el Estado, específicamente el Polo Democrático y el movimiento Progresistas, con el fin analizar la calidad y objetividad de la información basados en puntos específicos como el discurso mediático, el análisis gráfico y la percepción de los principales líderes políticos de estos movimientos y periodistas.

Análisis en componentes principales de la Obesidad-Adiposidad y su asociación con factores de riesgo cardiometabólico en mujeres. Estudio transversal en un entorno laboral

Los factores de riesgo para el desarrollo de eventos cardiometabólicos, constituyen un set de variables útiles como predictores de enfermedades cardiovasculares y metabólicas. Uno de los factores de riesgo que recibe mayor atención en la deteccion y prevencion de eventos cardiometabolicos, es la obesidad y la herramienta más común para diagnosticarla es el índice de masa corporal. Sin embargo, existen imprecisiones y sesgos en su concepto actual y en la forma de medirla. Nuevas alternativas de valoracion y tamizaje deben incluir porcentaje de grasa corporal y su distribución, dada la relevancia que adquiere la adiposidad en la definicion de obesidad y por ende en la mejoría del pronóstico de eventos cardiometabólicos. Los entornos laborales son ambientes vulnerables que se beneficiarían ampliamente de la aplicación de estas nuevas alternativas para predecir e intervenir tempranamente el riesgo cardiometabólico desde el correcto tamizaje de obesidad, dado el volumen poblacional que se puede abordar.

Factores asociados a mortalidad en pacientes colombianos con enfermedades autoinmunes en la unidad de cuidado intensivo, de un hospital de tercer nivel en Bogotá Colombia

Antecedentes: El interés en las enfermedades autoinmunes (EA) y su desenlace en la unidad de cuidado intensivo (UCI) han incrementado debido al reto clínico que suponen para el diagnóstico y manejo, debido a que la mortalidad en UCI fluctúa entre el 17 – 55 %. El siguiente trabajo representa la experiencia de un año de nuestro grupo en un hospital de tercer nivel. Objetivo: Identificar factores asociados a mortalidad particulares de los pacientes con enfermedades autoinmunes que ingresan a una UCI, de un hospital de tercer nivel en Bogotá, Colombia. Métodos: El uso de análisis de componentes principales basado en el método descriptivo multivariado y análisis de múltiple correspondencia fue realizado para agrupar varias variables relacionadas con asociación significativa y contexto clínico común. Resultados: Cincuenta pacientes adultos con EA con una edad promedio de 46,7 ± 17,55 años fueron evaluados. Los dos diagnósticos más comunes fueron lupus eritematoso sistémico y esclerosis sistémica, con una frecuencia de 45% y 20% de los pacientes respectivamente. La principal causa de admisión en la UCI fue la infección seguido de actividad aguda de la EA, 36% y 24% respectivamente. La mortalidad durante la estancia en UCI fue del 24%. El tiempo de hospitalización antes de la admisión a la UCI, el choque, soporte vasopresor, ventilación mecánica, sepsis abdominal, Glasgow bajo y plasmaféresis fueron factores asociados con mortalidad. Dos fenotipos de variables fueron definidos relacionadas con tiempo en la UCI y medidas de soporte en UCI, las cuales fueron asociadas supervivencia y mortalidad. Conclusiones: La identificación de factores individuales y grupos de factores por medio del análisis de componentes principales permitirá la implementación de medidas terapéutica de manera temprana y agresiva en pacientes con EA en la UCI para evitar desenlaces fatales.

The impact of North Atlantic Oscillation on the atmospheric pollutant variability in the Porto region, Portugal

The North Atlantic Oscillation (NAO) is an important large-scale atmospheric circulation that influences the European countries climate. This study evaluated NAO impact in air quality in Porto Metropolitan Area (PMA), Portugal, for the period 2002-2006. NAO, air pollutants and meteorological data were statistically analyzed. All data were obtained from PMA Weather Station, PMA Air Quality Stations and NOAA analysis. Two statistical methods were applied in different time scale : principal component and correlation coefficient. Annual time scale, using multivariate analysis (PCA, principal component analysis), were applied in order to identified positive and significant association between air pollutants such as PM10, PM2.5, CO, NO and NO2, with NAO. On the other hand, the correlation coefficient using seasonal time scale were also applied to the same data. The results of PCA analysis present a general negative significant association between the total precipitation and NAO, in Factor 1 and 2 (explaining around 70% of the variance), presented in the years of 2002, 2004 and 2005. During the same years, some air pollutants (such as PM10, PM2.5, SO2, NOx and CO) present also a positive association with NAO. The O3 shows as well a positive association with NAP during 2002 and 2004, at 2nd Factor, explaining 30% of the variance. From the seasonal analysis using correlation coefficient, it was found significant correlation between PM10 (0.72., p<0.05, in 2002), PM2.5 (0 74, p<0.05, in 2004), and SO2 (0.78, p<0.01, in 2002) with NAO during March-December (no winter period) period. Significant associations between air pollutants and NAO were also verified in the winter period (December to April) mainly with ozone (2005, r=-0.55, p.<0.01). Once that human health and hospital morbidities may be affected by air pollution, the results suggest that NAO forecast can be an important tool to prevent them, in the Iberian Peninsula and specially Portugal.

Visual Object Recognition Using Deformable Models of Vehicles

This workshop paper reports recent developments to a vision system for traffic interpretation which relies extensively on the use of geometrical and scene context. Firstly, a new approach to pose refinement is reported, based on forces derived from prominent image derivatives found close to an initial hypothesis. Secondly, a parameterised vehicle model is reported, able to represent different vehicle classes. This general vehicle model has been fitted to sample data, and subjected to a Principal Component Analysis to create a deformable model of common car types having 6 parameters. We show that the new pose recovery technique is also able to operate on the PCA model, to allow the structure of an initial vehicle hypothesis to be adapted to fit the prevailing context. We report initial experiments with the model, which demonstrate significant improvements to pose recovery.

Arctic oscillation or North Atlantic oscillation?

The definition and interpretation of the Arctic oscillation (AO) are examined and compared with those of the North Atlantic oscillation (NAO). It is shown that the NAO reflects the correlations between the surface pressure variability at its centers of action, whereas this is not the case for the AO. The NAO pattern can be identified in a physically consistent way in principal component analysis applied to various fields in the Euro-Atlantic region. A similar identification is found in the Pacific region for the Pacific–North American (PNA) pattern, but no such identification is found here for the AO. The AO does reflect the tendency for the zonal winds at 35° and 55°N to anticorrelate in both the Atlantic and Pacific regions associated with the NAO and PNA. Because climatological features in the two ocean basins are at different latitudes, the zonally symmetric nature of the AO does not mean that it represents a simple modulation of the circumpolar flow. An increase in the AO or NAO implies strong, separated tropospheric jets in the Atlantic but a weakened Pacific jet. The PNA has strong related variability in the Pacific jet exit, but elsewhere the zonal wind is similar to that related to the NAO. The NAO-related zonal winds link strongly through to the stratosphere in the Atlantic sector. The PNA-related winds do so in the Pacific, but to a lesser extent. The results suggest that the NAO paradigm may be more physically relevant and robust for Northern Hemisphere variability than is the AO paradigm. However, this does not disqualify many of the physical mechanisms associated with annular modes for explaining the existence of the NAO.

Chemical characterisation of wild populations of Thymus from different climatic regions in southeast Spain

Thymus is taxonomically a very complex genus with a high frequency of hybridisation and introgression among sympatric species. The variation in accumulation of leaf-surface flavonoids was investigated in 71 wild populations of Thymus front different putative hybrid swarm areas in Andalucia, Spain. Twenty-two flavones, five flavanones, two dihydroflavonols, a flavonol and two unknowns were detected by HPLC-DAD combined with LC-APCI-MS analysis. The majority of compounds were flavones with a lutelin-type substitution of the B-ring, in contrast to previous reports on Macedonian taxa, which predominantly accumulate flavones with apigenin-type substitution of the B-ring. Anatomical and morphometric studies, supported by cluster analysis, identified pure Thymus hyemalis and Thymus baeticus populations, and a large number of putative hybrids. Flavonoid variation was closely related to morphological variation in all populations and is suspected to be a result of genetic polymorphism. Principal component analysis identified the presence of species-specific and geographically linked chemotypes and putative hybrids with mixed morphological and chemical characteristics. Qualitative and quantitative flavonoid accumulation appears to be genetically regulated, while external factors play a secondary role. Flavonoid profiles can thus provide diagnostic markers for the taxonomy of Thymus and are also useful in detecting hybridising taxa. (C) 2007 Elsevier Ltd. All rights reserved.

Comparison of small and large deformation rheological properties of wheat dough and gluten

The rheological properties of dough and gluten are important for end-use quality of flour but there is a lack of knowledge of the relationships between fundamental and empirical tests and how they relate to flour composition and gluten quality. Dough and gluten from six breadmaking wheat qualities were subjected to a range of rheological tests. Fundamental (small-deformation) rheological characterizations (dynamic oscillatory shear and creep recovery) were performed on gluten to avoid the nonlinear influence of the starch component, whereas large deformation tests were conducted on both dough and gluten. A number of variables from the various curves were considered and subjected to a principal component analysis (PCA) to get an overview of relationships between the various variables. The first component represented variability in protein quality, associated with elasticity and tenacity in large deformation (large positive loadings for resistance to extension and initial slope of dough and gluten extension curves recorded by the SMS/Kieffer dough and gluten extensibility rig, and the tenacity and strain hardening index of dough measured by the Dobraszczyk/Roberts dough inflation system), the elastic character of the hydrated gluten proteins (large positive loading for elastic modulus [G'], large negative loadings for tan delta and steady state compliance [J(e)(0)]), the presence of high molecular weight glutenin subunits (HMW-GS) 5+10 vs. 2+12, and a size distribution of glutenin polymers shifted toward the high-end range. The second principal component was associated with flour protein content. Certain rheological data were influenced by protein content in addition to protein quality (area under dough extension curves and dough inflation curves [W]). The approach made it possible to bridge the gap between fundamental rheological properties, empirical measurements of physical properties, protein composition, and size distribution. The interpretation of this study gave indications of the molecular basis for differences in breadmaking performance.

Colonic microbiota signatures across five northern European countries

The composition of the colonic microbiota of 91 northern Europeans was characterized by fluorescent in situ hybridization using 18 phylogenetic probes. On average 75% of the bacteria were identified, and large interindividual variations were observed. Clostridium coccoides and Clostridium leptum were the dominant groups (28.0% and 25.2%), followed by the Bacteroides (8.5%). According to principal component analysis, no significant grouping with respect to geographic origin, age, or gender was observed.

Generative topographic mapping applied to clustering and visualization of motor unit action potentials

The identification and visualization of clusters formed by motor unit action potentials (MUAPs) is an essential step in investigations seeking to explain the control of the neuromuscular system. This work introduces the generative topographic mapping (GTM), a novel machine learning tool, for clustering of MUAPs, and also it extends the GTM technique to provide a way of visualizing MUAPs. The performance of GTM was compared to that of three other clustering methods: the self-organizing map (SOM), a Gaussian mixture model (GMM), and the neural-gas network (NGN). The results, based on the study of experimental MUAPs, showed that the rate of success of both GTM and SOM outperformed that of GMM and NGN, and also that GTM may in practice be used as a principled alternative to the SOM in the study of MUAPs. A visualization tool, which we called GTM grid, was devised for visualization of MUAPs lying in a high-dimensional space. The visualization provided by the GTM grid was compared to that obtained from principal component analysis (PCA). (c) 2005 Elsevier Ireland Ltd. All rights reserved.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Neurofuzzy state identification using prefiltering

A new state estimator algorithm is based on a neurofuzzy network and the Kalman filter algorithm. The major contribution of the paper is recognition of a bias problem in the parameter estimation of the state-space model and the introduction of a simple, effective prefiltering method to achieve unbiased parameter estimates in the state-space model, which will then be applied for state estimation using the Kalman filtering algorithm. Fundamental to this method is a simple prefiltering procedure using a nonlinear principal component analysis method based on the neurofuzzy basis set. This prefiltering can be performed without prior system structure knowledge. Numerical examples demonstrate the effectiveness of the new approach.

Visual surveillance using deformable models of vehicles

This paper presents recent developments to a vision-based traffic surveillance system which relies extensively on the use of geometrical and scene context. Firstly, a highly parametrised 3-D model is reported, able to adopt the shape of a wide variety of different classes of vehicle (e.g. cars, vans, buses etc.), and its subsequent specialisation to a generic car class which accounts for commonly encountered types of car (including saloon, batchback and estate cars). Sample data collected from video images, by means of an interactive tool, have been subjected to principal component analysis (PCA) to define a deformable model having 6 degrees of freedom. Secondly, a new pose refinement technique using “active” models is described, able to recover both the pose of a rigid object, and the structure of a deformable model; an assessment of its performance is examined in comparison with previously reported “passive” model-based techniques in the context of traffic surveillance. The new method is more stable, and requires fewer iterations, especially when the number of free parameters increases, but shows somewhat poorer convergence. Typical applications for this work include robot surveillance and navigation tasks.

Climatic and oceanic associations with daily rainfall extremes over southern Africa

Changes in climate variability and, in particular, changes in extreme climate events are likely to be of far more significance for environmentally vulnerable regions than changes in the mean state. It is generally accepted that sea-surface temperatures (SSTs) play an important role in modulating rainfall variability. Consequently, SSTs can be prescribed in global and regional climate modelling in order to study the physical mechanisms behind rainfall and its extremes. Using a satellite-based daily rainfall historical data set, this paper describes the main patterns of rainfall variability over southern Africa, identifies the dates when extreme rainfall occurs within these patterns, and shows the effect of resolution in trying to identify the location and intensity of SST anomalies associated with these extremes in the Atlantic and southwest Indian Ocean. Derived from a Principal Component Analysis (PCA), the results also suggest that, for the spatial pattern accounting for the highest amount of variability, extremes extracted at a higher spatial resolution do give a clearer indication regarding the location and intensity of anomalous SST regions. As the amount of variability explained by each spatial pattern defined by the PCA decreases, it would appear that extremes extracted at a lower resolution give a clearer indication of anomalous SST regions.

Reduced space optimal interpolation of daily rain gauge precipitation in Switzerland

The coarse spacing of automatic rain gauges complicates near-real- time spatial analyses of precipitation. We test the possibility of improving such analyses by considering, in addition to the in situ measurements, the spatial covariance structure inferred from past observations with a denser network. To this end, a statistical reconstruction technique, reduced space optimal interpolation (RSOI), is applied over Switzerland, a region of complex topography. RSOI consists of two main parts. First, principal component analysis (PCA) is applied to obtain a reduced space representation of gridded high- resolution precipitation fields available for a multiyear calibration period in the past. Second, sparse real-time rain gauge observations are used to estimate the principal component scores and to reconstruct the precipitation field. In this way, climatological information at higher resolution than the near-real-time measurements is incorporated into the spatial analysis. PCA is found to efficiently reduce the dimensionality of the calibration fields, and RSOI is successful despite the difficulties associated with the statistical distribution of daily precipitation (skewness, dry days). Examples and a systematic evaluation show substantial added value over a simple interpolation technique that uses near-real-time observations only. The benefit is particularly strong for larger- scale precipitation and prominent topographic effects. Small-scale precipitation features are reconstructed at a skill comparable to that of the simple technique. Stratifying the reconstruction method by the types of weather type classifications yields little added skill. Apart from application in near real time, RSOI may also be valuable for enhancing instrumental precipitation analyses for the historic past when direct observations were sparse.