Electronic Raman Scattering in Twistronic Few-Layer Graphene

We study electronic contribution to the Raman scattering signals of two-, three- and four-layer graphene with layers at one of the interfaces twisted by a small angle with respect to each other. We find that the Raman spectra of these systems feature two peaks produced by van Hove singularities in moir\'{e} minibands of twistronic graphene, one related to direct hybridization of Dirac states, and the other resulting from band folding caused by moir\'{e} superlattice. The positions of both peaks strongly depend on the twist angle, so that their detection can be used for non-invasive characterization of the twist, even in hBN-encapsulated structures.Comment: 7 pages (including 4 figures) + 10 pages (3 figures) supplemen

Performance of ZigBee-based wireless sensor nodes for real-time monitoring of fruits logistics

Progress in fruit logistics requires an increasing number of measurements to be performed in refrigerated chambers and during transport. Wireless sensor networks (WSN) are a promising solution in this field. This paper explores the potential of wireless sensor technology for monitoring fruit storage and transport conditions. It focuses in particular on ZigBee technology with special regard to two different commercial modules (Xbow and Xbee). The main contributions of the paper relate to the analysis of battery life under cooling conditions and the evaluation of the reliability of communications and measurements. Psychrometric equations were used for quick assessment of changes in the absolute water content of air, allowing estimation of future water loss, and detection of condensation on the product

Impact parameters related to bruising in selected fruits.

Impact testing with an instrumented free-fallingmass (50.4 g) device was applied to three varities of pears and two varieties of apples, forincreasing ripeness stages and impact energy (2 to 20 cm drops). Impact parameters were studied in relation to bruise and to ripeness, establishing relations between them and with the different characteristics of the fruits

A data generator for covid-19 patients’ care requirements inside hospitals

[EN] A Spanish version of the article is provided (see section before references). This paper presents the generation of a plausible data set related to the needs of COVID-19 patients with severe or critical symptoms. Possible illness’ stages were proposed within the context of medical knowledge as of January 2021. The parameters chosen in this data set were customized to fit the population data of the Valencia region (Spain) with approximately 2.5 million inhabitants. They were based on the evolution of the pandemic between September 2020 and March 2021, a period that included two complete waves of the pandemic. Contrary to expectation and despite the European and national transparency laws (BOE-A2013-12887, 2013; European Parliament and Council of the European Union, 2019), the actual COVID-19 pandemic-related data, at least in Spain, took considerable time to be updated and made available (usually a week or more). Moreover, some relevant data necessary to develop and validate hospital bed management models were not publicly accessible. This was either because these data were not collected, because public agencies failed to make them public (despite having them indexed in their databases), the data were processed within indicators and not shown as raw data, or they simply published the data in a format that was difficult to process (e.g., PDF image documents versus CSV tables). Despite the potential of hospital information systems, there were still data that were not adequately captured within these systems. Moreover, the data collected in a hospital depends on the strategies and practices specific to that hospital or health system. This limits the generalization of "real" data, and it encourages working with "realistic" or plausible data that are clean of interactions with local variables or decisions (Gunal, 2012; Marin-Garcia et al., 2020). Besides, one can parameterize the model and define the data structure that would be necessary to run the model without delaying till the real data become available. Conversely, plausible data sets can be generated from publicly available information and, later, when real data become available, the accuracy of the model can be evaluated (Garcia-Sabater and Maheut, 2021). This work opens lines of future research, both theoretical and practical. From a theoretical point of view, it would be interesting to develop machine learning tools that, by analyzing specific data samples in real hospitals, can identify the parameters necessary for the automatic prototyping of generators adapted to each hospital. Regarding the lines of research applied, it is evident that the formalism proposed for the generation of sound patients is not limited to patients affected by SARS-CoV-2 infection. The generation of heterogeneous patients can represent the needs of a specific population and serve as a basis for studying complex health service delivery systems.[ES] En este trabajo se presenta cómo se ha generado un conjunto de datos verosímiles relacionados con las necesidades de pacientes covid-19 con síntomas severe or critical. Se considerarán las etapas posibles con los conocimientos médicos a fecha de enero de 2021. Los parámetros elegidos en este data set están personalizados para adecuarse a los valores poblacionales de la región de Valencia (Spain), unos 2.5 Millones de habitantes y la evolución de la pandemia entre los meses de septiembre 2020 y marzo 2021, un periodo de tiempo que contemple dos olas completas de pandemia.En contra de lo que cabría esperar, a pesar de la ley de transparencia europea y nacional (BOE-A-2013-12887, 2013; Parlamento Europeo y del Consejo de la Unión Europea, 2019), los datos reales relacionados con la pandemia covid-19, al menos en España, tardan mucho en actualizarse y estar disponibles (normalmente una semana o más días). Además, algunos datos relevantes para trabajar los modelos de gestión de camas de hospital no están accesibles públicamente. Bien porque no se hayan recogido esos datos, o porque los organismos públicos no los ofrecen (a pesar de tenerlos indexados en sus bases de datos), o los ofrecen camuflados en indicadores procesados y no muestran los datos en bruto, o simplemente los publican en un formato de difícil reutilización (por ejemplo, en documentos PDF en lugar de en tablas CSV). A pesar de que los sistemas de información de los hospitales son bastante potentes, siguen existiendo datos que ni siquiera están recogidos adecuadamente en el sistema de información de salud.Por otra parte, los datos recogidos en un hospital dependen de las estrategias y practicas propias de ese hospital o sistema de salud. Este efecto limita la generalización de los datos “reales” y es necesario trabajar con datos “realistas” o verosímiles que están limpios de interacciones con variables o decisiones locales (Gunal, 2012; Marin-Garcia et al., 2020). Por un lado, se puede parametrizar el modelo y definir la estructura de datos que sería necesaria para ejecutar el modelo con datos reales. Por otro lado, se pueden generar conjuntos de datos verosímiles a partir de la información pública disponible y, posteriormente, cuando se disponga de los datos reales evaluar la bondad del modelo (Garcia-Sabater & Maheut, 2021).Marin-Garcia, JA.; Ruiz, A.; Julien, M.; Garcia-Sabater, JP. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management. 12(1):76-115. https://doi.org/10.4995/wpom.1533276115121Alexander, G. L. (2007). The nurse-patient trajectory framework. Medinfo. MEDINFO, 12(Pt 2), 910- 914.Belciug, S., Bejinariu, S. I., & Costin, H. (2020). An artificial immune system approach for a multicompartment queuing model for improving medical resources and inpatient bed occupancy in pandemics. Advances in Electrical and Computer Engineering, 20(3). https://doi.org/10.4316/AECE.2020.03003BOE-A-2013-12887. (2013). Ley 19/2013, de 9 de diciembre, de transparencia acceso a la información pública y buen gobierno. 1-32.Brochard, L. (2003). Mechanical ventilation: Invasive versus noninvasive. European Respiratory Journal, Supplement, 22(47), 31s-37s. https://doi.org/10.1183/09031936.03.00050403Buckley, D., & Gillham, M. (2007). Invasive Respiratory Support. In Cardiothoracic Critical Care (pp. 419-436). Elsevier Inc. https://doi.org/10.1016/B978-075067572-7.50032-1Casas-Rojo, J. M., Antón-Santos, J. M., Millán-Núñez-Cortés, J., Lumbreras-Bermejo, C., RamosRincón, J. M., Roy-Vallejo, E., Artero-Mora, A., Arnalich-Fernández, F., García-Bruñén, J. M., Vargas-Núñez, J. A., Freire-Castro, S. J., Manzano-Espinosa, L., Perales-Fraile, I., CresteloViéitez, A., Puchades-Gimeno, F., Rodilla-Sala, E., Solís-Marquínez, M. N., Bonet-Tur, D., Fidalgo-Moreno, M. P., … Gómez-Huelgas, R. (2020). Clinical characteristics of patients hospitalized with COVID-19 in Spain: Results from the SEMI-COVID-19 Registry. Revista Clinica Espanola, 220(8), 480-494. https://doi.org/10.1016/j.rce.2020.07.003Castelnuovo, F., Marchese, V., Cristini, G., Crosato, V., Pennati, F., Renisi, G., Izzo, I., Paraninfo, G., Van Hauwermeiren, E., & Castelli, F. (2020). Discharge ward during the sars-cov-2 pandemic: An effective way to increase patient turnover when human resources are scarce. Infezioni in Medicina, 28(4), 539-544. https://doi.org/10.1007/s15010-020-01522-4Celeux, G., Lavergne, C., Vernaz, Y., Celeux, G., Lavergne, C., Vernaz, Y., Material, A., & Censored, D. (2006). Assessing Material Aging from Doubly Censored Data : Weibull Distribution vs . Poisson Process To cite this version : HAL Id : inria-00072799 Assessing material aging from doubly censored data : Weibull distribution vs . Poisson process apport. [Research Report] RR-3857, INRIA. 2000. inria-00072799.Claudio, D., Cosgriff, V., Nino, V., & Valladares, L. (2021). An Agile Standardized Work Procedure for Cleaning the Operating Room. Journal of Industrial Engineering and Management, 14, in press. https://doi.org/https://doi.org/jiem.3440CNE -Centro Nacional de Epidemiología. (2020). Información científico-técnica, enfermedad por coronavirus, COVID-19 (actualizado 20201112).Comtois, D. (2021). summarytools: Tools to Quickly and Neatly Summarize Data.Corbin, J. M., & Strauss, A. L. (1988). Unending Work and Care: Managing Chronic Illness at Home. Jossey-Bass Inc.Daniel, P., Mecklenburg, M., Massiah, C., Joseph, M. A., Wilson, C., Parmar, P., Rosengarten, S., Maini, R., Kim, J., Oomen, A., & Zehtabchi, S. (2021). Non-invasive positive pressure ventilation versus endotracheal intubation in treatment of COVID-19 patients requiring ventilatory support. American Journal of Emergency Medicine, 43, 103-108. https://doi.org/10.1016/j.ajem.2021.01.068Dominguez-Lara, S. A. (2018). Odds-ratios and their interpretation as effect size in research. In Educacion Medica (Vol. 19, Issue 1, pp. 65-66). Fundacion Educacion Medica. https://doi.org/10.1016/j.edumed.2017.01.008ECDP. (2020). Guidance for discharge and ending isolation in the context of widespread community transmission of COVID-19-first update Scope of this document. In European Centre for Disease Prevention (Issue April, pp. 1-8).Epstein, R. H., & Dexter, F. (2020). A Predictive Model for Patient Census and Ventilator Requirements at Individual Hospitals During the Coronavirus Disease 2019 (COVID-19) Pandemic: A Preliminary Technical Report. Cureus. https://doi.org/10.7759/cureus.8501European center for disease prevention and control. (2020). Coronavirus disease 2019 (COVID-19) pandemic: increased transmission in the EU/EEA and the UK - seventh update. 2019 (March).Fowler, R., Hatchette, T., Salvadori, M., Baclic, O., Volling, C., Murthy, S., Emeriaud, G., Money, D., Brooks, J., Decou, M., & Ofner, M. (2020). Clinical management of patients with COVID-19: Second interim guidance. Canadian Critical Care Society and Association of Medical Microbiology and Infectious Disease (AMMI) Canada, 1-67.Garcia-Sabater, J. P., & Maheut, J. (2021). Introducción al Modelado Matematico, Nota Técnica. RiuNet. Repositorio Institucional UPV. https://doi.org/http://hdl.handle.net/10251/158555Garcia-Sabater, J. P., Maheut, J., Ruiz, A., & Garcia-Sabater, J. J. (2020). A framework for capacity and operations planning in services organizations employing workers with intellectual disabilities. Sustainability (Switzerland), 12(22), 1-17. https://doi.org/10.3390/su12229713Generalitat Valenciana. Conselleria de Sanitat Universal i Salut Pública. (2019). Memoria de gestión conselleria de sanitat universal i salut pública 2019. 14493-14496.Generalitat Valenciana. (2018). Memoria de Gestión de la Conselleria de Sanitat Universal i Salut Pública.Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C., Hui, D. S. C., Du, B., Li, L., Zeng, G., Yuen, K.-Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., … Zhong, N. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine, 382(18), 1708-1720. https://doi.org/10.1056/NEJMoa2002032Gunal, M. M. (2012). A guide for building hospital simulation models. Health Systems, 1(1), 17-25. https://doi.org/10.1057/hs.2012.8Hair, J. F., Black, W. C., Babin, B., & Anderson, R. E. (2009). Multivariate data analysis (7th edition). Prentice Hall. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., … Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223), 497- 506. https://doi.org/10.1016/S0140-6736(20)30183-5Lagarda-Leyva, E. A., & Ruiz, A. (2019). A Systems Thinking Model to Support Long-Term Bearability of the Healthcare System: The Case of the Province of Quebec. Sustainability, 11(24), 7028. https://doi.org/10.3390/su11247028Manninen, K. (2020). Typical progress of covid-19. Marin-Garcia, J. A. (2015). Publishing in two phases for focused research by means of "research collaborations." WPOM-Working Papers on Operations Management, 6(2), 76. https://doi.org/10.4995/wpom.v6i2.4459Marin-Garcia, J. A., Bonavia, T., & Losilla, J.-M. (2020). Changes in the Association between European Workers' Employment Conditions and Employee Well-Being in 2005, 2010 and 2015. International Journal of Environmental Research and Public Health, 17(3), 1048. https://doi.org/10.3390/ijerph17031048Marin-Garcia, J. A., Garcia-Sabater, J. P., Ruiz, A., Maheut, J., & Garcia-Sabater, J. J. (2020). Operations Management at the service of health care management: Example of a proposal for action research to plan and schedule health resources in scenarios derived from the COVID-19 outbreak. Journal of Industrial Engineering and Management, 13(2), 213. https://doi.org/10.3926/jiem.3190Marin-Garcia, J. A., Vidal-Carreras, P. I., Garcia Sabater, J. J., & Escribano-Martinez, J. (2019). Protocol: Value Stream Maping in Healthcare. A systematic literature review. WPOM-Working Papers on Operations Management, 10(2), 36. https://doi.org/10.4995/wpom.v10i2.12297Ministerio De Sanidad, Servicios Sociales e Igualdad. (2017). Hábitos de Vida Informe Anual del Sistema Nacional de salud 2016 (INFORMES,). MINISTERIO DE SANIDAD, SERVICIOS SOCIALES E IGUALDAD.Mun, J. (2008). Appendix C. Understanding and Choosing the Right Probability Distributions. Advanced Analytical Models: Over 800 Models and 300 Applications from the Basel II Accord to Wall Street and Beyond, 899-917. https://doi.org/10.1002/9781119197096.app03Nino, V., Gomez, K., Martinez, K., & Claudio, D. (2021). Improving the registration process in a healthcare facility with lean principles. Journal of Industrial Engineering and Management, 14, in press. https://doi.org/https://doi.org/jiem.3432Olivieri, A., Palù, G., & Sebastiani, G. (2021). COVID-19 cumulative incidence, intensive care, and mortality in Italian regions compared to selected European countries. International Journal of Infectious Diseases, 102. https://doi.org/10.1016/j.ijid.2020.10.070Parlamento Europeo y del Consejo de la Unión Europea. (2019). Directiva(UE) 2019/1024 DEL PARLAMENTO EUROPEO Y DEL CONSEJO de la Unión Europea de 20 de junio de 2019 relativa a los datos abiertos y la reutilización de la información del sector público (versión refundida). 172/56-172/78.Petermann-Rocha, F., Hanlon, P., Gray, S. R., Welsh, P., Gill, J. M. R., Foster, H., Katikireddi, S. V., Lyall, D., Mackay, D. F., O'Donnell, C. A., Sattar, N., Nicholl, B. I., Pell, J. P., Jani, B. D., Ho, F. K., Mair, F. S., & Celis-Morales, C. (2020). Comparison of two different frailty measurements and risk of hospitalisation or death from COVID-19: findings from UK Biobank. BMC Medicine, 18(1). https://doi.org/10.1186/s12916-020-01822-4Pinaire, J., Azé, J., Bringay, S., & Landais, P. (2017). Patient healthcare trajectory. An essential monitoring tool: a systematic review. Health Information Science and Systems, 5(1), 1-18. https://doi.org/10.1007/s13755-017-0020-2Plaza, J. (2021). Informe Científico-Divulgativo: Un Año De Coronavirus Sars-Cov-2. Ministerio de Ciencia e Innovación.Popat, B., & Jones, A. T. (2012). Invasive and non-invasive mechanical ventilation. In Medicine (United Kingdom) (Vol. 40, Issue 6, pp. 298-304). Elsevier Ltd. https://doi.org/10.1016/j.mpmed.2012.03.010Posso, M., Comas, M., Román, M., Domingo, L., Louro, J., González, C., Sala, M., Anglès, A., Cirera, I., Cots, F., Frías, V.-M., Gea, J., Güerri-Fernández, R., Masclans, J. R., Noguès, X., Vázquez, O., Villar-García, J., Horcajada, J. P., Pascual, J., & Castells, X. (2020). Comorbidities and Mortality in Patients With COVID-19 Aged 60 Years and Older in a University Hospital in Spain. Archivos de Bronconeumología, 56(11), 756-758. https://doi.org/10.1016/j.arbres.2020.06.012R Core Team. (2020). R: A Language and Environment for Statistical Computing. Revelle, W. (2021). psych: Procedures for Psychological, Psychometric, and Personality Research.Roa-Martínez, S. M., Vidotti, S. A. B., & Santana, R. C. (2017). Estructura propuesta del artículo de datos como publicación científica. Revista Espanola de Documentacion Cientifica, 40(1), 1-12. https://doi.org/10.3989/redc.2017.1.1375Romeo Casabona, C. M., & Urruela Mora, A. (2020). Informe Del Ministerio De Sanidad Sobre Los Aspectos Éticos En Situaciones De Pandemia: El Sars-Cov-2. 12.RStudio Team. (2020). RStudio: Integrated Development for R. RStudio, PBC. Rubio-Rivas, M., Corbella, X., Mora-Luján, J. M., Loureiro-Amigo, J., López Sampalo, A., Yera Bergua, C., Esteve Atiénzar, P. J., Díez García, L. F., Gonzalez Ferrer, R., Plaza Canteli, S., Pérez Piñeiro, A., Cortés Rodríguez, B., Jorquer Vidal, L., Pérez Catalán, I., León Téllez, M., Martín Oterino, J. Á., Martín González, M. C., Serrano Carrillo de Albornoz, J. L., García Sardon, E., … GómezHuelgas, R. (2020). Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID19. Journal of Clinical Medicine, 9(11), 3488. https://doi.org/10.3390/jcm9113488Ruckdeschel, P., Kohl, M., Stabla, T., & Camphausen, F. (2006). S4 Classes for Distributions. R News, 6(2), 2-6. Ruza, F. (2008). Cuidados Intensivos Pediatricos. 6(6), 336. Schauberger, P., & Walker, A. (2020). openxlsx: Read, Write and Edit xlsx Files.Stang, A., Stang, M., & Jöckel, K. H. (2020). Estimated use of intensive care beds due to COVID-19 in Germany over time. Deutsches Arzteblatt International, 117(19). https://doi.org/10.3238/arztebl.2020.0329Unroe, M., Kahn, J. M., Carson, S. S., Govert, J. A., Martinu, T., Sathy, S. J., Clay, A. S., Chia, J., Gray, A., Tulsky, J. A., & Cox, C. E. (2010). One-year trajectories of care and resource utilization for recipients of prolonged mechanical ventilation: A cohort study. Annals of Internal Medicine, 153(3), 167-175. https://doi.org/10.7326/0003-4819-153-3-201008030-00007Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (Fourth). Springer. https://doi.org/10.1007/978-0-387-21706-2Wang, Y., Wang, Y., Chen, Y., & Qin, Q. (2020). Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. In Journal of Medical Virology (Vol. 92, Issue 6, pp. 568-576). John Wiley and Sons Inc. https://doi.org/10.1002/jmv.25748Wickham, H. (2007). Reshaping Data with the {reshape} Package. Journal of Statistical Software, 21(12), 1-20. https://doi.org/10.18637/jss.v021.i12Wickham, H. (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. https://doi.org/10.18637/jss.v040.i01Wiersema, U. F. (2007). Noninvasive Respiratory Support. In Cardiothoracic Critical Care (pp. 410- 418). Elsevier Inc. https://doi.org/10.1016/B978-075067572-7.50031-XWinck, J. C., & Scala, R. (2021). Non-invasive respiratory support paths in hospitalized patients with COVID-19: proposal of an algorithm. Pulmonology. https://doi.org/10.1016/j.pulmoe.2020.12.005Wong, G. N., Weiner, Z. J., Tkachenko, A. V., Elbanna, A., Maslov, S., & Goldenfeld, N. (2020). Modeling COVID-19 dynamics in Illinois under non-pharmaceutical interventions. In medRxiv. https://doi.org/10.1101/2020.06.03.20120691Wu, H., Godfrey, A. J. R., Govindaraju, K., & Pirikahu, S. (2020). ExtDist: Extending the Range of Functions for Probability Distributions.Xia, W., & Sun, J. (2013). Simulation guided value stream mapping and lean improvement: A case study of a tubular machining facility. Journal of Industrial Engineering and Management, 6(2), 456-476. https://doi.org/10.3926/jiem.532Xu, X. W., Wu, X. X., Jiang, X. G., Xu, K. J., Ying, L. J., Ma, C. L., Li, S. B., Wang, H. Y., Zhang, S., Gao, H. N., Sheng, J. F., Cai, H. L., Qiu, Y. Q., & Li, L. J. (2020). Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: Retrospective case series. The BMJ, 368. https://doi.org/10.1136/bmj.m606Zheng, Z., Peng, F., Xu, B., Zhao, J., Liu, H., Peng, J., Li, Q., Jiang, C., Zhou, Y., Liu, S., Ye, C., Zhang, P., Xing, Y., Guo, H., & Tang, W. (2020). Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. In Journal of Infection (Vol. 81, Issue 2, pp. e16- e25). W.B. Saunders Ltd. https://doi.org/10.1016/j.jinf.2020.04.02

Mitochondria and the NLRP3 Inflammasome in Alcoholic and Nonalcoholic Steatohepatitis

Alcoholic (ASH) and nonalcoholic steatohepatitis (NASH) are advanced stages of fatty liver disease and two of the most prevalent forms of chronic liver disease. ASH and NASH are associated with significant risk of further progression to cirrhosis and hepatocellular carcinoma (HCC), the most common type of liver cancer, and a major cause of cancer-related mortality. Despite extensive research and progress in the last decades to elucidate the mechanisms of the development of ASH and NASH, the pathogenesis of both diseases is still poorly understood. Mitochondrial damage and activation of inflammasome complexes have a role in inducing and sustaining liver damage. Mitochondrial dysfunction produces inflammatory factors that activate the inflammasome complexes. NLRP3 inflammasome (nucleotide-binding oligomerization domain-like receptor protein 3) is a multiprotein complex that activates caspase 1 and the release of pro-inflammatory cytokines, including interleukin-1? (IL-1?) and interleukin-18 (IL-18), and contributes to inflammatory pyroptotic cell death. The present review, which is part of the issue "Mitochondria in Liver Pathobiology", provides an overview of the role of mitochondrial dysfunction and NLRP3 activation in ASH and NASH

Global analysis of electromagnetic moments in odd near doubly magic nuclei

We use the nuclear DFT approach to determine nuclear electric quadrupole and magnetic dipole moments in all one-particle and one-hole neighbors of eight doubly magic nuclei. We align angular momenta along the intrinsic axial-symmetry axis with broken time-reversal symmetry, which allows us to explore fully the self-consistent charge, spin, and current polarizations. Spectroscopic moments are determined for symmetry-restored wave functions and compared with available experimental data. We find that the obtained polarizations do not call for using quadrupole- or dipole-moment operators with effective charges or effective g-factors.Comment: 15 LaTeX pages, 9 figure

Antiagregación y anticoagulación en síndromes coronarios agudos: niveles de evidencia

Management of acute coronary syndromes (ACS) has moved rapidly in parallel with our understanding of the pathophysiological basis of the disease. In the eighties, the demonstration of the pivotal role of coronary thrombosis in the etiology of a ACS led to administration of aspirin and unfractionated heparin. In recent years, new medical and invasive therapies have been developed: anti-platelets (thienopyridines and glycoprotein Ilb/IlIa inhibitors), antithrombins (low molecularweight heparins) and most recently, factor Xa inhibitors (pentasaccharides). As new treatments are rapidly added, clinicians are constantly challenged to incorporate new information and guidelines into their practices in a timely fashion

Winds in Star Clusters Drive Kolmogorov Turbulence

Intermediate and massive stars drive fast and powerful isotropic winds that interact with the winds of nearby stars in star clusters and the surrounding interstellar medium (ISM). Wind-ISM collisions generate astrospheres around these stars that contain hot $T\sim 10^7$ K gas that adiabatically expands. As individual bubbles expand and collide they become unstable, potentially driving turbulence in star clusters. In this paper we use hydrodynamic simulations to model a densely populated young star cluster within a homogeneous cloud to study stellar wind collisions with the surrounding ISM. We model a mass-segregated cluster of 20 B-type young main sequence stars with masses ranging from 3--17 $M_{\odot}$. We evolve the winds for $\sim$11 kyrs and show that wind-ISM collisions and over-lapping wind-blown bubbles around B-stars mixes the hot gas and ISM material generating Kolmogorov-like turbulence on small scales early in its evolution. We discuss how turbulence driven by stellar winds may impact the subsequent generation of star formation in the clusterComment: 12 pages, 5 figures, Accepted for publication in ApJ

Evaluating Wikipedia as a source of information for disease understanding

The increasing availability of biological data is improving our understanding of diseases and providing new insight into their underlying relationships. Thanks to the improvements on both text mining techniques and computational capacity, the combination of biological data with semantic information obtained from medical publications has proven to be a very promising path. However, the limitations in the access to these data and their lack of structure pose challenges to this approach. In this document we propose the use of Wikipedia - the free online encyclopedia - as a source of accessible textual information for disease understanding research. To check its validity, we compare its performance in the determination of relationships between diseases with that of PubMed, one of the most consulted data sources of medical texts. The obtained results suggest that the information extracted from Wikipedia is as relevant as that obtained from PubMed abstracts (i.e. the free access portion of its articles), although further research is proposed to verify its reliability for medical studies.Comment: 6 pages, 5 figures, 5 tables, published at IEEE CBMS 2018, 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS