Sarcoma sinovial sobre grave traumatismo de pie

El sarcoma sinovial es una neoplasia mesenquimal maligna poco frecuente, apareciendo como una tumoración profunda de crecimiento lento, con nulo o escaso dolor y poca alteración funcional por lo que el diagnóstico suele ser tardío. El antecedente traumático de ha registrado en algún caso sin probar relación etiológica. Se presenta el caso clínico de un varón de 56 años de edad, que sufrió un grave traumatismo en los pies con fractura de ambos calcáneos y II, III y IV MTT pie derecho más luxación articulación metatarso falángica del primer dedo. Tras la intervención mediante enclavijado sin exposición de los focos de lesión, la evolución de las fracturas fue buena, observando a los diez meses una tumoración en el dorso del pie derecho diagnosticada como sarcoma sinovial por biopsia. Tras el estudio de extensión de la neoplasia de objetivó afectación multisitémica y a pesar de los tratamientos instaurados al paciente falleció tres meses después. Se discute la dificultad del diagnóstico diferencialSynovial sarcoma is an inusual mesenchymal neoplasm, appearing as a deep tumour of show growth, with none or rare pain and few functional disturbances. These features usually lead to a late diagnosis. Previous injury is present in some cases but there in no proven ethilogical relation. We present a male, age 56, who suffered a serious feet injury leading to fracture of both calcaneus and II, III and IV MTT right foot with dislocation MTT-falangic of first finger. After surgery with with closed Kirschner wires, the evolution was good. Ten months later a tumour appeared on the right foot, diagnosed as a synovial sarcoma after biopsy. The stating study showed multisystemic damage and the patient died three months in spite of treatment. We address the difficulty of the differential diagnose

Fixing the Solar Neutrino Parameters with Sterile Neutrinos

Neutrino mixing matrix appears to be close to bimaximal mixing, but for the solar mixing angle which is definitively smaller than forty five degrees. Whereas it seems quite easy to understand bimaximal mixing with the use of new global symmetries, as in models using $L_e - L_\mu - L_\tau$, understanding the about to eleven degrees of deviation in the observed solar angle seems less simple. We suggest that such a deviation could be due to a light sterile neutrino that mixes with the active sector. The mass scale needed to produce the effect has to be smaller than atmospheric scale, and it would introduce a new mass squared difference which should be smaller than the solar scale. We present a toy model that exemplifies these features.Comment: 19 pages, two figures. Discussion extended. References adde

Method for monitoring urea and ammonia in wine and must by flow injection-pervaporation

An easy to automate flow-injection-pervaporation method for monitoring urea and ammonia in must and wine was developed. The method is based on separation of the ammonia from the sample matrix by pervaporation followed by its reaction with salicylate, hypochlorite and nitroprusside to form a diazonium salt with maximum absorption at 647 nm. Conversion of urea into ammonia catalysed by urease was mandatory before pervaporation. After optimisation by either the univariate or multivariate approaches as required, the linear range was established (between 0 and 25 mg l-1) for both analytes. Then, the assessment of the proposed method versus a reference one for urea and ammonia was studied in terms of repeatability (0.52 mg l-1 and 0.43 mg l-1, respectively), reproducibility (1.34 mg l-1 and 1.21 mg l-1, respectively), detection and quantification limits (LOD=0.9 and 0.6 mg l-1, LQ=1.02 and 0.67 mg l-1, respectively) and traceability. The sample throughput was 16 samples h-1. The method can be applied to the monitoring of the target analytes in must and young wine in order to control their contents, preventing formation of ethyl carbamate

Numerical Analysis of Electrohydrodynamic Instability in Dielectric-liquid-gas Flows Subjected to Unipolar Injection

In this work, the electrohydrodynamic instability induced by a unipolar charge injection is extended from a single-phase dielectric liquid to a two-phase system that consists of a liquid-air interface. A volume-of-fluid model-based two-phase solver was developed with simplified Maxwell equations implemented in the open-source platform OpenFOAM. The numerically obtained critical value for the linear stability matches well with the theoretical values. To highlight the effect of the slip boundary at interface, the deformation of the interface is ignored. A bifurcation diagram with hysteresis loop linking the linear and finite-amplitude criteria, which is Uf=0.059, was obtained in this situation. It is concluded that the lack of viscous effect at interface leads to a significant increase in the flow intensity, which is the reason for the smaller instability threshold in two-phase system. The presence of interface also changes the flow structure and results in a shear distribution of electric force, which may play an important role in the interface deformation.National Natural Science Foundation of China 11802079, 12172110Ministerio de Ciencia, Innovación y Universidades PGC2018-099217-B-I00Ministerio de Economía y Competitividad CTQ2017-83602-C2-2-RJunta de Andalucía 2019/FQM-25

Valoración de los estilos de dirección de proyectos fin de carrera en ingeniería informática

Completamos aquí un estudio realizado sobre dirección de proyectos fin de carrera (PFC) en Ingeniería Informática. Entonces usamos una encuesta para determinar una tipología de estilos de dirección de PFC. Resumimos ahora los resultados de aquel estudio, ampliándolo con nuevos ratios para valorar los resultados de la aplicación de cada estilo en cuanto a calificación, duración del PFC y tiempo dedicado por el director, teniendo en cuenta el tipo de alumno. Estos resultados pueden ayudar a los tutores de PFC a encontrar distintas formas de dirección a utilizar.SUMMARY -- This paper complements a previous study we conducted on capstone projects in Computer Science Engineering. Then we used a survey to determine a typology of supervision styles for that projects. This article summarizes the results of that study, extending them with new ratios to assess the results of the application of each style in terms of grade obtained, duration and time spent by the director, considering the type of student. These results can help supervisors to find other ways of supervision

Municipal water demand forecasting: Tools for intervention time series

This article introduces some approaches to common issues arising in real cases of water demand prediction. Occurrences of negative data gathered by the network metering system and demand changes due to closure of valves or changes in consumer behavior are considered. Artificial neural networks (ANNs) have a principal role modeling both circumstances. First, we propose the use of ANNs as a tool to reconstruct any anomalous time series information. Next, we use what we call interrupted neural networks (I-NN) as an alternative to more classical intervention ARIMA models. Besides, the use of hybrid models that combine not only the modeling ability of ARIMA to cope with the time series linear part, but also to explain nonlinearities found in their residuals, is proposed. These models have shown promising results when tested on a real database and represent a boost to the use and the applicability of ANNs.This work has been supported by project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, and ACOMP/2010/146 of the Conselleria de Educacion of the Generalitat Valenciana. As well, the authors are grateful to "Aguas de Murcia" for the collaboration in this work and for the availability of the data.This work has been supported by project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, and ACOMP/2010/146 of the Conseller a de Educacion of the Generalitat Valenciana. As well, the authors are grateful to "Aguas de Murcia" for the collaboration in this work and for the availability of the data.Herrera Fernández, AM.; García-Díaz, JC.; Izquierdo Sebastián, J.; Pérez García, R. (2011). Municipal water demand forecasting: Tools for intervention time series. Stochastic Analysis and Applications. 29(6):998-1007. https://doi.org/10.1080/07362994.2011.610161S9981007296Zhou, S. ., McMahon, T. ., Walton, A., & Lewis, J. (2002). Forecasting operational demand for an urban water supply zone. Journal of Hydrology, 259(1-4), 189-202. doi:10.1016/s0022-1694(01)00582-0Bougadis, J., Adamowski, K., & Diduch, R. (2005). Short-term municipal water demand forecasting. Hydrological Processes, 19(1), 137-148. doi:10.1002/hyp.5763Jain, A., & Ormsbee, L. E. (2002). Short-term water demand forecast modeling techniques-CONVENTIONAL METHODS VERSUS AI. Journal - American Water Works Association, 94(7), 64-72. doi:10.1002/j.1551-8833.2002.tb09507.xPeña, D., Tiao, G. C., & Tsay, R. S. (Eds.). (2000). A Course in Time Series Analysis. Wiley Series in Probability and Statistics. doi:10.1002/9781118032978et al. 2000 . Mining Time Series of Meteorological Variables Using Rough Sets—A Case Study, Binding Environmental Sciences and Artificial Intelligent. BESAI 2000, Germany, 7:1–8.Herrera, M., Torgo, L., Izquierdo, J., & Pérez-García, R. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150. doi:10.1016/j.jhydrol.2010.04.005McLeod, A. I., & Vingilis, E. R. (2005). Power Computations for Intervention Analysis. Technometrics, 47(2), 174-181. doi:10.1198/004017005000000094Box, G. E. P., & Tiao, G. C. (1975). Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association, 70(349), 70-79. doi:10.1080/01621459.1975.10480264Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501-514. doi:10.1016/j.ejor.2003.08.037Zealand, C. M., Burn, D. H., & Simonovic, S. P. (1999). Short term streamflow forecasting using artificial neural networks. Journal of Hydrology, 214(1-4), 32-48. doi:10.1016/s0022-1694(98)00242-xWang, W., Gelder, P. H. A. J. M. V., Vrijling, J. K., & Ma, J. (2006). Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology, 324(1-4), 383-399. doi:10.1016/j.jhydrol.2005.09.032Kneale , P. , See , L. , and Smith , A. 2001 .Towards Defining Evaluation Measures for Neural Network Forecasting Models; Proceedings of the Sixth International Conference on GeoComputation, University of Queensland, Australia.Peña, D., & Rodríguez, J. (2002). A Powerful Portmanteau Test of Lack of Fit for Time Series. Journal of the American Statistical Association, 97(458), 601-610. doi:10.1198/016214502760047122Peña, D., & Rodríguez, J. (2006). The log of the determinant of the autocorrelation matrix for testing goodness of fit in time series. Journal of Statistical Planning and Inference, 136(8), 2706-2718. doi:10.1016/j.jspi.2004.10.026LJUNG, G. M., & BOX, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303. doi:10.1093/biomet/65.2.297MONTI, A. C. (1994). A proposal for a residual autocorrelation test in linear models. Biometrika, 81(4), 776-780. doi:10.1093/biomet/81.4.77