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Overview: We assessed the role of age and disease activity as new factors contributing to establish the risk of progressive multifocal leucoencephalopathy in multiple sclerosis patients treated with natalizumab in 36 University Hospitals in Europe. We performed the study in 1,307 multiple sclerosis patients (70.8% anti-John Cunninghan virus positive antibodies) treated with natalizumab for a median time of 3.28 years. Epidemiological, clinical, and laboratory variables were collected. Lipid-specific IgM oligoclonal band status was available in 277 patients. Factors associated with progressive multifocal leucoencephalopathy onset were explored by uni- and multivariate logistic regression. Results: Thirty-five patients developed progressive multifocal leucoencephalopathy. The multivariate analysis identified anti-John Cunninghan virus antibody indices and relapse rate as the best predictors for the onset of this serious opportunistic infection in the whole cohort. They allowed to stratify progressive multifocal leucoencephalopathy risk before natalizumab initiation in individual patients [area under the curve (AUC) = 0.85]. The risk ranged from <1/3,300 in patients with anti-John Cunninghan virus antibody indices <0.9 and relapse rate >0.5, to 1/50 in the opposite case. In patients with lipid-specific IgM oligoclonal bands assessment, age at natalizumab onset, anti-John Cunninghan virus antibody indices, and lipid-specific IgM oligoclonal band status predicted progressive multifocal leucoencephalopathy risk (AUC = 0.92). The absence of lipid-specific IgM oligoclonal bands was the best individual predictor (OR = 40.94). The individual risk ranged from <1/10,000 in patients younger than 45 years at natalizumab initiation, who showed anti John Cunningham virus antibody indices <0.9 and lipid-specific IgM oligoclonal bands to 1/33 in the opposite case. Conclusions: In a perspective of personalized medicine, disease activity, anti-lipid specific IgM oligoclonal bands, anti

[Abstract]: Thermal simulations are a commonly used tool for energy efficiency analysis of buildings. Regional meteorological station networks are a prime source of weather data inputs, required for building thermal simulations. However, local measurements from weather stations are not always available, and when available, access to data may be expensive. This paper analysed a novel use of a numerical weather prediction mesoscale model, the Global Forecast System (GFS) sflux model, as a source of input data for transient thermal simulations. Two interpolation techniques (nearest neighbour and universal kriging) were used to generate local weather datasets from GFS outputs at 27 locations spread over an area of 29,574 km2 in Galicia (northwest Spain). The performance of the GFS estimations was tested against weather measurements obtained from a government weather agency. A representative building with the most common features was selected for running thermal simulations in the TRNSYS environment, focused on heating demands, with estimated weather data as the input. The results highlighted that GFS-interpolated datasets consistently performed better than using measured data from the nearest weather station. GFS was found to be an appropriate weather source for building simulations and was able to provide good-quality, free and global-scale local weather inputs., Xunta de Galicia; TOPACIO IN852A 2018/37

[Abstract]: The building sector offers huge potential for energy savings, which helps to achieve environmental objectives and social benefits. A good approach to determine both the energy consumption of new buildings and the energetic refurbishment of existing buildings is through thermal simulation. This paper studies how building energy simulation calibration can be improved using interpolated weather data to determine on-site meteorological parameters at the building location. The lack of precise meteorological data in the exact location of buildings means that data from nearby stations is generally used, not knowing how far the error spreads in the results of heating demands and loads. The novelty of this paper lies in the analysis of error propagation to the results of demands and loads of thermal simulation, as well as in the method used to reduce these errors by TPS interpolation. As an interesting conclusion, the average (CV(RMSE)) obtained in the simulation of the studied building, placed successively in each one of the 70 meteorological station locations, decreases from 74% when using the nearest neighborhood to each site to 26% using the TPS interpolation technique. The error in the building simulations is almost three times lower using the studied method.

Este trabajo presenta un método simplificado de modelado térmico de depósitos de almacenamiento de energía térmica con materiales de cambio de fase (PCMs) en su interior. El planteamiento desarrollado es muy similar en forma y metodología al concepto de “factor de bypass”, ampliamente utilizado en el campo de la climatización para estimar las condiciones de salida de un aire tratado cuando atraviesa una batería de acondicionamiento. El método propuesto es capaz de reproducir la respuesta térmica de un depósito con PCMs de forma robusta y relativamente precisa, sólo empleando los parámetros más relevantes del almacenamiento (diez en total). Parámetros de entrada muy fundamentales y bien conocidos, ya en las primeras etapas de diseño, donde el impacto potencial de las decisiones tomadas en el ahorro de energía es el mayor, y en donde no es habitual un conocimiento perfecto de todas las particularidades del depósito con PCM (conocimiento preciso de la geometría del depósito e intercambiador, propiedades termofísicas del PCM en función de su temperatura, etc.). Para estas etapas preliminares de diseño de la instalación, el método es especialmente útil y adecuado. Los resultados obtenidos de la simulación del modelo simplificado muestran un elevado grado de convergencia y exactitud con 21 ensayos experimentales de carga y descarga térmica de dos depósitos con PCMs, observándose discrepancias en el total de energía acumulada o extraída del depósito desde valores próximos al 1% hasta como máximo un 13% aproximadamente, según ensayo y material de cambio de fase. Los dos almacenamientos latentes, usaban cada uno de ellos un PCM diferente (orgánico e inorgánico),así como un tipo de intercambiador de calor diferente (de diferente geometría, configuración y material).