The practice of collective singing: benefits from the socioaffective and emotional perspectives

El canto es considerado un elemento dinamizador del proceso evolutivo. Su valor transversal trasciende los aspectos específicamente musicales, implicando la práctica de actos beneficiosos para el ser humano y favoreciendo su salud. Las agrupaciones corales constituyen un espacio idóneo en el que cada componente aspira a lograr su superación, excelencia y felicidad contribuyendo al fin último de cantar. La presente investigación, basada en un diseño mixto no experimental, descriptivo y correlacional con modalidad transeccional, centra su interés en comprobar que la práctica del canto colectivo aporta al ser humano beneficios en las perspectivas socioafectiva y emocional. Los cinco capítulos en que se estructura el trabajo, recogen la fundamentación teórica del tema abordado, los rasgos descriptivos del estudio empírico efectuado, la sucesión de los resultados obtenidos y las conclusiones más significativas. Desde la consideración de las distintas dimensiones de estudio establecidas, y con independencia de su edad, sexo y agrupación coral de pertenencia, se observa como la práctica del canto colectivo impacta positivamente sobre el desarrollo socioafectivo y emocional del coralista.Singing is considered a dynamic element of the evolutionary process. Its transversal value transcends specifically musical aspects, involving the practice of acts beneficial to humans and promoting their health. Choral groups constitute an ideal space in which each component aims to achieve its overcoming, excellence and happiness contributing to the ultimate goal of singing. This research, based on a mixed non-experimental, descriptive and correlational design with a transectional modality, focuses its interest in verifying that the practice of collective singing brings benefits to the human being in socio-emotional and emotional perspectives. The five chapters in which the work is structured, include the theoretical foundation of the subject addressed, the descriptive features of the empirical study carried out, the succession of the results obtained and the most significant conclusions. From the consideration of the different dimensions of study established, and regardless of their age, sex and coral group membership, it is observed how the practice of collective singing positively impacts the socioaffective and emotional development of the coralist

El análisis de la influencia del canto colectivo en el bienestar físico y emocional del coralista como elemento relevante en el desarrollo de la Sociedad del Conocimiento

El canto colectivo consiste en la interpretación coordinada de una pieza musical vocal, realizada colectivamente con una determinada función social y/o musical. El presente estudio tiene como propósito analizar los beneficios que su práctica puede aportar a los y las coralistas desde una doble perspectiva: el bienestar físico y el bienestar emocional, alentando de este modo la promoción de un conocimiento compartido transferible a diferentes ámbitos de la vida. El proceso analítico de la información cuantitativa, corresponde a un diseño no experimental, descriptivo y correlacional con modalidad transeccional. Los resultados del estudio ofrecen una respuesta clara y contundente acerca de la percepción de estos beneficios en su doble vertiente planteada

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

[EN] Background and Objective: Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer. It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard. This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. Methods: A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. Results: The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. Conclusions: An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2019/1, and by Carlos III Institute of Health under the project DTS15/00080.Perez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Fuster Bagetto, A.; Pollan, M.; Pérez-Gómez, B.; Salas-Trejo, D.... (2020). A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. Computer Methods and Programs in Biomedicine. 195:123-132. https://doi.org/10.1016/j.cmpb.2020.105668S123132195Kuhl, C. K. (2015). The Changing World of Breast Cancer. Investigative Radiology, 50(9), 615-628. doi:10.1097/rli.0000000000000166Boyd, N. F., Rommens, J. M., Vogt, K., Lee, V., Hopper, J. L., Yaffe, M. J., & Paterson, A. D. (2005). Mammographic breast density as an intermediate phenotype for breast cancer. The Lancet Oncology, 6(10), 798-808. doi:10.1016/s1470-2045(05)70390-9Assi, V., Warwick, J., Cuzick, J., & Duffy, S. W. (2011). Clinical and epidemiological issues in mammographic density. Nature Reviews Clinical Oncology, 9(1), 33-40. doi:10.1038/nrclinonc.2011.173Oliver, A., Freixenet, J., Marti, R., Pont, J., Perez, E., Denton, E. R. E., & Zwiggelaar, R. (2008). A Novel Breast Tissue Density Classification Methodology. IEEE Transactions on Information Technology in Biomedicine, 12(1), 55-65. doi:10.1109/titb.2007.903514Pérez-Benito, F. J., Signol, F., Pérez-Cortés, J.-C., Pollán, M., Pérez-Gómez, B., Salas-Trejo, D., … LLobet, R. (2019). Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts. Computer Methods and Programs in Biomedicine, 177, 123-132. doi:10.1016/j.cmpb.2019.05.022Ciatto, S., Houssami, N., Apruzzese, A., Bassetti, E., Brancato, B., Carozzi, F., … Scorsolini, A. (2005). Categorizing breast mammographic density: intra- and interobserver reproducibility of BI-RADS density categories. The Breast, 14(4), 269-275. doi:10.1016/j.breast.2004.12.004Skaane, P. (2009). Studies comparing screen-film mammography and full-field digital mammography in breast cancer screening: Updated review. Acta Radiologica, 50(1), 3-14. doi:10.1080/02841850802563269Van der Waal, D., den Heeten, G. J., Pijnappel, R. M., Schuur, K. H., Timmers, J. M. H., Verbeek, A. L. M., & Broeders, M. J. M. (2015). Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting. PLOS ONE, 10(9), e0136667. doi:10.1371/journal.pone.0136667Kim, S. H., Lee, E. H., Jun, J. K., Kim, Y. M., Chang, Y.-W., … Lee, J. H. (2019). Interpretive Performance and Inter-Observer Agreement on Digital Mammography Test Sets. Korean Journal of Radiology, 20(2), 218. doi:10.3348/kjr.2018.0193Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2017). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246. doi:10.1093/bib/bbx044LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., … Kingsbury, B. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 29(6), 82-97. doi:10.1109/msp.2012.2205597Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters, 119, 3-11. doi:10.1016/j.patrec.2018.02.010Helmstaedter, M., Briggman, K. L., Turaga, S. C., Jain, V., Seung, H. S., & Denk, W. (2013). Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature, 500(7461), 168-174. doi:10.1038/nature12346Lee, K., Turner, N., Macrina, T., Wu, J., Lu, R., & Seung, H. S. (2019). Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy. Current Opinion in Neurobiology, 55, 188-198. doi:10.1016/j.conb.2019.04.001Leung, M. K. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014). Deep learning of the tissue-regulated splicing code. Bioinformatics, 30(12), i121-i129. doi:10.1093/bioinformatics/btu277Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., … Troyanskaya, O. G. (2019). Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nature Genetics, 51(6), 973-980. doi:10.1038/s41588-019-0420-0Kallenberg, M., Petersen, K., Nielsen, M., Ng, A. Y., Diao, P., Igel, C., … Lillholm, M. (2016). Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring. IEEE Transactions on Medical Imaging, 35(5), 1322-1331. doi:10.1109/tmi.2016.2532122Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.726791P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun, Overfeat: integrated recognition, localization and detection using convolutional networks, arXiv:1312.6229 (2013).Dice, L. R. (1945). Measures of the Amount of Ecologic Association Between Species. Ecology, 26(3), 297-302. doi:10.2307/1932409Pollán, M., Llobet, R., Miranda-García, J., Antón, J., Casals, M., Martínez, I., … Salas-Trejo, D. (2013). Validation of DM-Scan, a computer-assisted tool to assess mammographic density in full-field digital mammograms. SpringerPlus, 2(1). doi:10.1186/2193-1801-2-242Llobet, R., Pollán, M., Antón, J., Miranda-García, J., Casals, M., Martínez, I., … Pérez-Cortés, J.-C. (2014). Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction. Computer Methods and Programs in Biomedicine, 116(2), 105-115. doi:10.1016/j.cmpb.2014.01.021He, L., Ren, X., Gao, Q., Zhao, X., Yao, B., & Chao, Y. (2017). The connected-component labeling problem: A review of state-of-the-art algorithms. Pattern Recognition, 70, 25-43. doi:10.1016/j.patcog.2017.04.018Wu, K., Otoo, E., & Suzuki, K. (2008). Optimizing two-pass connected-component labeling algorithms. Pattern Analysis and Applications, 12(2), 117-135. doi:10.1007/s10044-008-0109-yShen, R., Yan, K., Xiao, F., Chang, J., Jiang, C., & Zhou, K. (2018). Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection. Journal of Digital Imaging, 31(5), 680-691. doi:10.1007/s10278-018-0068-9Yin, K., Yan, S., Song, C., & Zheng, B. (2018). A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms. International Journal of Computer Assisted Radiology and Surgery, 14(2), 237-248. doi:10.1007/s11548-018-1867-7James, J. . (2004). The current status of digital mammography. Clinical Radiology, 59(1), 1-10. doi:10.1016/j.crad.2003.08.011Sáez, C., Robles, M., & García-Gómez, J. M. (2016). Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research, 26(1), 312-336. doi:10.1177/0962280214545122Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666. doi:10.1016/j.patrec.2009.09.011Lee, J., & Nishikawa, R. M. (2018). Automated mammographic breast density estimation using a fully convolutional network. Medical Physics, 45(3), 1178-1190. doi:10.1002/mp.12763D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, arXiv:1412.6980 (2014).Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., & Barzilay, R. (2019). Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Radiology, 290(1), 52-58. doi:10.1148/radiol.2018180694Bengio, Y., Courville, A., & Vincent, P. (2013). Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828. doi:10.1109/tpami.2013.50Wu, G., Kim, M., Wang, Q., Munsell, B. C., & Shen, D. (2016). Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning. IEEE Transactions on Biomedical Engineering, 63(7), 1505-1516. doi:10.1109/tbme.2015.2496253T.P. Matthews, S. Singh, B. Mombourquette, J. Su, M.P. Shah, S. Pedemonte, A. Long, D. Maffit, J. Gurney, R.M. Hoil, et al., A multi-site study of a breast density deep learning model for full-field digital mammography and digital breast tomosynthesis exams, arXiv:2001.08383 (2020)

Analysis of multipactor RF breakdown in a waveguide containing a transversely magnetized ferrite

In this paper, the multipactor RF breakdown in a parallel-plate waveguide partially filled with a ferrite slab magnetized normal to the metallic plates is studied. An external magnetic field is applied along the vertical direction between the plates in order to magnetize the ferrite. Numerical simulations using an in-house 3D code are carried out to obtain the multipactor RF voltage threshold in this kind of structures. The presented results show that the multipactor RF voltage threshold at certain frequencies becomes considerably lower than for the corresponding classical metallic parallel-plate waveguide with the same vacuum gap.This work was supported by the European Space Agency (ESA) under Novel Investigation in Multipactor Effect in Ferrite and other Dielectrics used in high power RF Space Hardware Contract AO 1-7551/13/NL/GLC, and partially by the Spanish Government (under coordinated R&D projects TEC2013-47037-C5-R and TEC2014-55463-C3-3-P)

Experimental validation of multipactor effect for ferrite materials used in L- and S-band nonreciprocal microwave components

This paper reports on the experimental measurement of power threshold levels for the multipactor effect between samples of ferrite material typically used in the practical implementation of L- and S-band circulators and isolators. For this purposes, a new family of wideband, nonreciprocal rectangular waveguide structures loaded with ferrites has been designed with a full-wave electromagnetic simulation tool. The design also includes the required magnetostatic field biasing circuits. The multipactor breakdown power levels have also been predicted with an accurate electron tracking code using measured values for the secondary electron yield (SEY) coefficient. The measured results agree well with simulations, thereby fully validating the experimental campaign.This work was supported by European Space Agency (ESA) through research project "Novel Investigation in Multipactor Effect in Ferrite and other Dielectrics used in High Power RF Space Hardware" (ref. AO 1-7551/13/NL/GLC), and by MINECO (Spanish Government) under R&D projects TEC2016-75934-C4-1-R, TEC2016-75934-C4-2-R and the ERDF co-funded project TEC2014-55463-C3-3-P

Novel multipactor studies in RF satellite payloads: Single-carrier digital modulated signals and ferrite materials

In this work it is reviewed the most novel advances in the multipactor RF breakdown risk assessment devoted to RF satellite microwave passive devices employed in space telecommunication systems. On one side, it is studied the effect of transmitting a single-carrier digital modulated signal in the multipactor RF voltage threshold in a coaxial line. On the other hand, an analysis of the multipactor phenomenon in a parallel-plate waveguide containing a magnetized ferrite slab it is presented

Hábitos sedentarios en adolescentes escolarizados de Cantabria

El aumento del tiempo dedicado a actividades sedentarias se ha relacionado en los últimos años con el aumento de la prevalencia del sobrepeso y la obesidad en la infancia y la adolescencia. En este trabajo estudiamos los hábitos sedentarios de adolescentes escolarizados en centros de educación pública de la Comunidad Autónoma de Cantabria, participantes en el Proyecto «Promoción de Hábitos Saludables en Adolescentes desde el Ámbito Educativo» llevado a cabo durante 2011. Participaron 1101 adolescentes con edades comprendidas entre los 10 y los 17 años, escolarizados en 16 centros. Todos los participantes cumplimentaron un cuestionario sobre sus hábitos de vida. Más del 85% de los participantes cumplen las recomendaciones de visionado de televisión en días de colegio, sin embargo dicho porcentaje disminuye en fin de semana. Esta actividad es superior en los chicos, en el fin de semana, e inferior en el grupo de edad de 10-11años. En cuanto al uso del ordenador, de la videoconsola, y de Internet por ocio, encontramos diferencias en función del sexo, la edad y el día de la semana. El tiempo dedicado a actividades sedentarias aumenta con la edad y durante el fin de semana. Asimismo, existe un patrón de hábitos sedentarios diferente entre ambos sexos. Debemos conocer estas diferencias de estilo de vida para poder realizar intervenciones eficaces en la promoción de hábitos saludables en los adolescentes

Autoimagen en las dos primeras fases de la adolescencia y factores relacionados

Se trata de la descripción de la imagen corporal en un amplio grupo de alumnos escolarizados en Cantabria (n=1179 adolescentes), de 10 a 17 años de edad (adolescencia temprana e intermedia) dentro de un estudio más amplio encaminado a evidenciar un estilo de vida saludable en estos adolescentes, llevado a cabo por profesores de universidad y profesores de educación física de los centros educativos. Los principales hallazgos consisten en que los adolescentes tienen, en general, una buena imagen de sí mismos y, aunque no reconocen la elevada prevalencia de sobrepeso y obesidad, desean adelgazar y el grado de satisfacción que tienen con su imagen corporal va empeorando conforme avanza la adolescencia, signifi cativamente más en las del sexo femenino. Esta insatisfacción debe ser tenida en cuenta en el abordaje de los adolescentes con obesidad

Influence of the Cumulative Incidence of COVID-19 Cases on the Mental Health of the Spanish Out-of-Hospital Professionals

This study aimed to analyze the psychological affectation of health professionals (HPs) of Spanish Emergency Medical Services (EMSs) according to the cumulative incidence (CI) of COVID19 cases in the regions in which they worked. A cross-sectional descriptive study was designed, including all HPs working in any EMS of the Spanish geography between 1 February 2021 and 30 April 2021. Their level of stress, anxiety and depression (DASS-21) and the perception of self-efficacy (GSES) were the study’s main results. A 2-factor analysis of covariance was used to determine if the CI regions of COVID-19 cases determined the psychological impact on each of the studied variables. A total of 1710 HPs were included. A third presented psychological impairment classified as severe. The interaction of CI regions with the studied variables did not influence their levels of stress, anxiety, depression or self-efficacy. Women, younger HPs or those with less EMS work experience, emergency medical technicians (EMT), workers who had to modify their working conditions or those who lived with minors or dependents suffered a greater impact from the COVID-19 pandemic in certain regions. These HPs have shown high levels of stress, anxiety, depression and medium levels of self-efficacy, with similar data in the different geographical areas. Psychological support is essential to mitigate their suffering and teach them to react to adverse events.This research was funded by Fundación ASISA and Sociedad Española de Urgencias y Emergencias (SEMES)