Síndrome de Reiter exacerbado por indometacina

A 26-year-old man, with a personal history of drug abuse and positive serology for HIV, had Reiter's syndrome for six years. He experienced progressive worsening of his cutaneous lesions after initiation of indomethacin therapy. The skin lesions were almost completely resolved after the discontinuance of the drug and its reintroduction resulted in a similar deterioration. To our knowledge, indomethacin has not been reported to aggravate Reiter's syndrome. This case study documents anti-inflammatory drugs as possible causal factors for triggering Reiter's syndrome. Possible implicated mechanisms are also discussed

A review of the role of spatial resolution in energy systems modelling:Lessons learned and applicability to the North Sea region

The importance of spatial resolution for energy modelling has increased in the last years. Incorporating more spatial resolution in energy models presents wide benefits, but it is not straightforward, as it might compromise their computational performance. This paper aims to provide a comprehensive review of spatial resolution in energy models, including benefits, challenges and future research avenues. The paper is divided in four parts: first, it reviews and analyses the applications of geographic information systems (GIS) for energy modelling in the literature. GIS analyses are found to be relevant to analyse how meteorology affects renewable production, to assess infrastructure needs, design and routing, and to analyse resource allocation, among others. Second, it analyses a selection of large scale energy modelling tools, in terms of how they can include spatial data, which resolution they have and to what extent this resolution can be modified. Out of the 34 energy models reviewed, 16 permit to include regional coverage, while 13 of them permit to include a tailor-made spatial resolution, showing that current available modelling tools permit regional analysis in large scale frameworks. The third part presents a collection of practices used in the literature to include spatial resolution in energy models, ranging from aggregated methods where the spatial granularity is non-existent to sophisticated clustering methods. Out of the spatial data clustering methods available in the literature, k-means and max-p have been successfully used in energy related applications showing promising results. K-means permits to cluster large amounts of spatial data at a low computational cost, while max-p ensures contiguity and homogeneity in the resulting clusters. The fourth part aims to apply the findings and lessons learned throughout the paper to the North Sea region. This region combines large amounts of planned deployment of variable renewable energy sources with multiple spatial claims and geographical constraints, and therefore it is ideal as a case study. We propose a complete modelling framework for the region in order to fill two knowledge gaps identified in the literature: the lack of offshore integrated system modelling, and the lack of spatial analysis while defining the offshore regions of the modelling framework

H(z)H(z) diagnostics on the nature of dark energy

The two dominant components of the cosmic budget today, pressureles matter and dark energy, may or may not be interacting with each other. Currently, both possibilities appear compatible with observational data. We propose several criteria based on the history of the Hubble factor that can help discern whether they are interacting and whether dark energy is phantom or quintessence in nature.Comment: 22 pages, 7 figures. Accepted for publication in IJMP

A Fuzzy k-Nearest Neighbors Classifier to Deal with Imperfect Data

© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Soft Computing. To access the final edited and published work see https://doi.org/10.1007/s00500-017-2567-xThe k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regression and classification. To classify a new instance, the kNN method computes its k nearest neighbors and generates a class value from them. Usually, this method requires that the information available in the datasets be precise and accurate, except for the existence of missing values. However, data imperfection is inevitable when dealing with real-world scenarios. In this paper, we present the kNNimp classifier, a k-nearest neighbors method to perform classification from datasets with imperfect value. The importance of each neighbor in the output decision is based on relative distance and its degree of imperfection. Furthermore, by using external parameters, the classifier enables us to define the maximum allowed imperfection, and to decide if the final output could be derived solely from the greatest weight class (the best class) or from the best class and a weighted combination of the closest classes to the best one. To test the proposed method, we performed several experiments with both synthetic and realworld datasets with imperfect data. The results, validated through statistical tests, show that the kNNimp classifier is robust when working with imperfect data and maintains a good performance when compared with other methods in the literature, applied to datasets with or without imperfection

Pancreatite Aguda Necrosante — Caso Clínico

Os autores apresentam um caso clínico de Pancreatite Aguda Hemorrágica, de provável etiologia idiopática, numa criança de 10 meses, que se manifestou inicialmente por vómitos, e alteração do estado de consciência e posteriormente por abdómen agudo. O diagnóstico foi feito durante a laparotomia. Houve boa evolução clínica. Como complicações, refere-se o aparecimento de dois pseudoquistos pancreáticos que regrediram progressiva e espontaneamente

Notas corológicas del macrofitobentos de Andalucía (España). VIII

La flora de macroalgas marinas de Andalucía se recoge en los catálogos de Flores- Moya et al. (1995a, 1995b) y Conde et al. (1996a), y las adiciones posteriores de Conde et al. (1996b), Báez et al. (2001) y Altamirano et al. (2006, 2008). En este trabajo se presentan 9 citas nuevas para Andalucía (señaladas con un asterisco), 19 citas nuevas para la provincia de Huelva, 9 citas nuevas y una confirmación para la provincia de Málaga y 25 citas nuevas para la provincia de Granada. Mediante esta contribución el catálogo de Chlorophyceae de Andalucía consta de 90 taxones, mientras que los de Phaeophyceae y Rhodophyceae contienen 109 y 356 taxones, respectivamente. Todo el material recolectado se conservó en formol al 4% en agua de mar para su posterior determinación en el laboratorio. Como medio de montaje y preservación de las muestras microscópicas se ha utilizado sirope de maíz al 25% en agua destilada con unas gotas de formol al 4%. Los ejemplares identificados se han depositado en el herbario de la Universidad de Málaga (MGC Phyc). Para la ordenación taxonómica se ha seguido a Guiry & Guiry (2009