Effect of crowding on the electron transfer process from plastocyanin and cytochrome c6 to photosystem I: a comparative study from cyanobacteria to green algae

Plastocyanin and cytochrome c 6, the alternate donor proteins to photosystem I, can be acidic, neutral or basic; the role of electrostatics in their interaction with photosystem I vary accordingly for cyanobacteria, algae and plants. The effect of different crowding agents on the kinetics of the reaction between plastocyanin or cytochrome c 6 and photosystem I from three different cyanobacteria, Synechocystis PCC 6803, Nostoc PCC 7119 and Arthrospira maxima, and a green alga, Monoraphidium braunii, has been investigated by laser flash photolysis, in order to elucidate how molecular crowding affects the interaction between the two donor proteins and photosystem I. The negative effect of viscosity on the interaction of the two donors with photosystem I for the three cyanobacterial systems is very similar, as studied by increasing sucrose concentration. Bovine serum albumin seems to alter the different systems in a specific way, probably by means of electrostatic interactions with the donor proteins. Ficoll and dextran behave in a parallel manner, favouring the interaction by an average factor of 2, although this effect is somewhat less pronounced in Nostoc. With regards to the eukaryotic system, a strong negative effect of viscosity is able to overcome the favourable effect of any crowding agent, maybe due to stronger donor/photosystem I electrostatic interactions or the structural nature of the eukaryotic photosystem I-enriched membrane particles.Spanish Ministry of Innovation and Science BFU2006-01361Andalusian Government PAI BIO-02

Nuevas tecnologías aplicadas a la educación : el sistema educativo a través de los medios de comunicacion

Las asignaturas en las que enmarcamos nuestro trabajo, por un lado Nuevas Tecnologías aplicadas a la Educacion es una materia a impartir en todas las especialidades de los Nuevos Planes del Título de Maestro; y por otro lado, la asignatura de Estudio de los Niveles Educativos, es una asignatura optativa del tercer curso de la Licenciatura en Pedagogia de la Universidad de Sevilla.Este trabajo trata de llevar a cabo un análisis de las noticias educativas que surgen en los medios de comunicación, y en concreto en los periódicosThe subjects in those that frame our work, on one hand New Technologies applied to the Education are a matter to impart in all the specialties of the New Plans of Teacher's Title; and on the other hand, the subject of Study of the Educational Levels, is an optional subject of the third course of the Licentiate in Pedagogy of the University of Seville.This work tries to carry out an analysis of the educational news that emerge in the media, and in short in the newspapers

Significado patológico de la imagen ultrasónica en pequeños animales

La ecografía es una técnica de diagnóstico por imagen segura, no invasiva y que no requiere una preparación excesiva del animal. Se utiliza para estudiar tejidos blandos, permitiendo la valoración del tamaño, forma, situación y estructura de los mismos. La ecografía o ultrasonografía se basa en los ultrasonidos generados en un dispositivo llamado transductor, compuesto por cristales piezoeléctricos. La Histopatología constituye un medio diagnóstico especializado, complementario de otras técnicas diagnósticas como la ecografía y que, en la mayoría de los casos, resulta esencial en cuanto a establecer un diagnóstico definitivo en muchos procesos patológicos. La toma de biopsias para realizar un estudio histopatológico requiere una acción agresiva “in vivo” sobre el animal pudiendo servirse de la técnica ecográfica para realizar la toma de muestras de los diferentes órganos internos. El objetivo de este trabajo es el establecimiento de una correlación directa entre la imagen ultrasónica de órganos normales y patológicos procedentes de animales eutanasiados o de extirpaciones quirúrgicas de rutina, con la imagen macroscópica e histopatológica de esos mismos órganos.Ultrasound is a diagnostic imaging technique safe, non invasive and does not require excessive grooming of the animal. Is used to study soft tissue, allowing the assessment of the size, shape, location and structure of the data. Ultrasound sonography, based on the ultrasound generated in a device called a transducer, comprising crystals piezoeléctricos. La constitutes a diagnostic Histopathology specialized complementary to other diagnostic techniques such as ultrasound and that, in most cases, it is essential as for a defi nitive diagnosis in many disease processes. The biopsy for histopathological study requires aggressive action “in vivo” on the animal can use ultrasound technique for sampling of the different organs internos. The aim of this study is to establish a direct correlation between the ultrasound image of normal and pathological organs from animals euthanized or routine surgical excision with macroscopic images and histopathologyf these same organs

A mixed distribution to fix the threshold for Peak-Over-Threshold wave height estimation

Modelling extreme values distributions, such as wave height time series where the higher waves are much less frequent than the lower ones, has been tackled from the point of view of the Peak-OverThreshold (POT) methodologies, where modelling is based on those values higher than a threshold. This threshold is usually predefned by the user, while the rest of values are ignored. In this paper, we propose a new method to estimate the distribution of the complete time series, including both extreme and regular values. This methodology assumes that extreme values time series can be modelled by a normal distribution in a combination of a uniform one. The resulting theoretical distribution is then used to fx the threshold for the POT methodology. The methodology is tested in nine real-world time series collected in the Gulf of Alaska, Puerto Rico and Gibraltar (Spain), which are provided by the National Data Buoy Center (USA) and Puertos del Estado (Spain). By using the Kolmogorov-Smirnov statistical test, the results confrm that the time series can be modelled with this type of mixed distribution. Based on this, the return values and the confdence intervals for wave height in diferent periods of time are also calculated

Aplicación de un modelo de calidad para evaluar experiencias e-learning en el Espacio Europeo Universitario

Durante los últimos años, las comunidades universitarias han dedicado grandes esfuerzos para integrar nuevas tecnologías a fin de mejorar sus procesos de aprendizaje. En este sentido, la mayoría de las universidades europeas han incorporado plataformas de e-learning que sirven de apoyo y complementan el modelo clásico de enseñanza. Sin embargo, la utilización de estas plataformas no siempre es suficiente para mejorar los procesos de enseñanza y aprendizaje. Por este motivo, se hacen necesarios métodos de diseño y evaluación de procesos educativos basados en plataformas de e-learning que tratarán de garantizar los objetivos de aprendizaje fijados. Este trabajo está orientado a evaluar este tipo de experiencias y, más específicamente, a proporcionar un procedimiento que utilice un modelo de calidad y que sirva de guía en la evaluación de experiencias educativas apoyadas en plataformas de e-learning.Nowadays, Universities and other higher education institutions invest a lot of resources to integrate Information & Communications Technologies (ICT) in their learning processes. In particular, e-learning platforms have been incorporated to support the traditional campus-based activities. However, the application of these platforms is not enough to improve learning and teaching processes. Innovative methods are required to design and evaluate ICT enhanced learning experiences that contribute to change the traditional face-toface teaching in this context. This work is focused on evaluating this kind of experiences and more specifically, on providing procedures to guide their evaluation when e-learning platforms are used.Durant els últims anys, les comunitats universitàries han dedicat grans esforços a integrar noves tecnologies per millorar els seus processos d'aprenentatge. En aquest sentit, la majoria de les universitats europees han incorporat plataformes d'e-learning que serveixen de suport i complementen el model clàssic d'ensenyament. Tanmateix, la utilització d'aquestes plataformes no sempre és suficient per millorar els processos d'ensenyament i aprenentatge. Tanmateix, amb la utilització d'aquestes plataformes no sempre n'hi ha prou per millorar els processos d'ensenyament i aprenentatge. Per aquest motiu, es fan necessaris mètodes de disseny i avaluació de processos educatius basats en plataformes d'e-learning que tractaran de garantir els objectius d'aprenentatge fixats. Aquest treball està orientat a avaluar aquest tipus d'experiències i, més específicament, a proporcionar un procediment que utilitzi un model de qualitat i que serveixi de guia en l'avaluació d'experiències educatives basades en plataformes d'e-learning

Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our objective was to identify those factors for HIV/HCV co-infected patients (to which clinicians have given careful consideration before treatment uptake) that have not being included among the prioritisation criteria. This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model. This machine learning methodology found that radial basis neural networks provided a very simple model in terms of the number of patient characteristics to be considered by the classifier (in this case, six), returning a good overall classification accuracy of 0.767 and a minimum sensitivity (for the classification of the minority class, untreated patients) of 0.550. Finally, the area under the ROC curve was 0.802, which proved to be exceptional. The parsimony of the model makes it especially attractive, using just eight connections. The independent variable "recent PWID" is compulsory due to its importance. The simplicity of the model means that it is possible to analyse the relationship between patient characteristics and the probability of belonging to the treated group

Hybridization of neural network models for the prediction of Extreme Significant Wave Height segments

This work proposes a hybrid methodology for the detection and prediction of Extreme Significant Wave Height (ESWH) periods in oceans. In a first step, wave height time series is approximated by a labeled sequence of segments, which is obtained using a genetic algorithm in combination with a likelihood-based segmentation (GA+LS). Then, an artificial neural network classifier with hybrid basis functions is trained with a multiobjetive evolutionary algorithm (MOEA) in order to predict the occurrence of future ESWH segments based on past values. The methodology is applied to a buoy in the Gulf of Alaska and another one in Puerto Rico. The results show that the GA+LS is able to segment and group the ESWH values, and the neural network models, obtained by the MOEA, make good predictions maintaining a balance between global accuracy and minimum sensitivity for the detection of ESWH events. Moreover, hybrid neural networks are shown to lead to better results than pure models

Error-Correcting Output Codes in the Framework of Deep Ordinal Classification

Automatic classification tasks on structured data have been revolutionized by Convolutional Neural Networks (CNNs), but the focus has been on binary and nominal classification tasks. Only recently, ordinal classification (where class labels present a natural ordering) has been tackled through the framework of CNNs. Also, ordinal classification datasets commonly present a high imbalance in the number of samples of each class, making it an even harder problem. Focus should be shifted from classic classification metrics towards per-class metrics (like AUC or Sensitivity) and rank agreement metrics (like Cohen’s Kappa or Spearman’s rank correlation coefficient). We present a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error-Correcting Output Codes (ECOC). We aim to show experimentally, using four different CNN architectures and two ordinal classification datasets, that the OBD+ECOC methodology significantly improves the mean results on the relevant ordinal and class-balancing metrics. The proposed method is able to outperform a nominal approach as well as already existing ordinal approaches, achieving a mean performance of RMSE=1.0797 for the Retinopathy dataset and RMSE=1.1237 for the Adience dataset averaged over 4 different architectures