Estudio epidemiológico sobre la prevalencia de uso de medicinas alternativas y complementarias por la población general y un grupo de médicos y estudiantes de medicina de la Comunidad de Madrid

Gómez Gascón, Tomás, codir.La medicina complementaria y alternativa (CAM) tiene un uso creciente en los países occidentales durante los últimos 20 años, con cifras en población general entre el 30 y el 90%. En España no hay estudios de población sobre el uso de estas terapias en la población general, aunque algunos estudios sobre pacientes y otros factores indirectos indican una alta utilización. Objetivos. Estimar la prevalencia de utilización de CAM y productos alternativos por la población general y un grupo de médicos de plantilla, médicos residentes y estudiantes de Medicina de la Comunidad de Madrid, así como conocer sus características socioculturales, los problemas de salud y razones que motivaron el uso, el gasto estimado por paciente y sus opiniones acerca de las CAM. Material y métodos: Estudio transversal de prevalencia mediante encuesta autoadministrada a cuatro muestras aleatorias significativas: una de población general perteneciente a las once áreas de Atención Primaria de Madrid (n=897), otra de médicos de plantilla (n=324) y residentes (n=111) de dos hospitales y áreas de Atención Primaria, y la última de estudiantes de dos facultades y hospitales universitarios (n=45). Resultados. Se recibieron 288 cuestionarios de población general (porcentaje de respuesta del 14,4%), 165 de médicos de plantilla (51%), 96 de residentes (86,5%) y 45 estudiantes (100%), El 66% de la población general había consumido productos alternativos y un 56,3% habían utilizado CAM en alguna ocasión, fundamentalmente terapias de relajación y de masaje, por problemas músculo-esqueléticos y de salud mental. Más del 75% tuvo percepción de mejoría y alto grado de satisfacción. La principal razón por la que acudieron a CAM fue la búsqueda de mejoría no alcanzada con la medicina convencional. El consejo de familiares o conocidos fue el principal medio por el que conocieron las terapias, y más de la mitad habían gastado menos de 50 euros mensuales en terapias y productos. Más del 80% opinó que las CAM deberían incluirse en el sistema sanitario público y los profesionales sanitarios recibir formación en ellas. En el análisis multivariado ser mujer, pertenecer al Área 5 de Atención Primaria y haber visitado a sanitarios convencionales más de 9 veces en el último año fueron los factores independientes asociados al uso de CAM. Un 42,1% de los no usuarios de CAM había consumido productos alterntativos y el 83,5% acudiría a CAM en caso de necesidad. Respecto a los médicos y estudiantes, habían consumido productos alternativos entre el 25 y el 45%, y acudido a CAM entre el 16 y el 36%, por las mismas patologías y a las mismas terapias que la población general, y su percepción de mejoría y grado de satisfacción fueron elevados aunque en menor grado que la población general. El gasto mensual en CAM y el medio por el que conocieron las terapias fueron los mismos que la población general. Entre el 48% y el 85% opinaron que las CAM debían incluirse en el sistema sanitario público y los profesionales sanitarios debían formarse en ellas. Entre los no usuarios de CAM, entre un 40% y un 73% habían consumido productos alternativos y del 68% al 76% declararon que acudirían a CAM si lo necesitaran en algún momento de su vida. Al comparar las cuatros subpoblaciones, la población general acudía a CAM entre 2 y 4 veces más que el resto, y a más terapias, y consumía productos alternativos entre 2 y 3 veces más. Conclusiones. El consumo de CAM y productos alternativos entre la población general es elevado, y mayor que entre los médicos y estudiantes. En todos ellos el nivel de satisfacción con las CAM es elevado y un alto porcentaje considera adecuado incluirlas dentro del sistema sanitario público y en la formación de los profesionales sanitarios

Clutch size in great tits (Parus major) in orange-groves of Valencia and in the holm oak forest of Monte Poblet (Tarragona)

La estación de nidificación del Carbonero Común (Parus major) en la franja mediterránea ibérica comienza en abril y finaliza en julio. No obstante, la fecha media de puesta es diferente en distintas localidades. El tamaño medio de la puesta gira en torno a los 7 huevos por nido en las localidades estudiadas. Este valor es diferente del tamaño de la puesta al norte de los Pirineos y en regiones más occidentales de la Península Ibérica.In the Iberian Mediterranean area, the Great Tit's breeding season starts in April and finishes in July, but the mean laying date differs in different areas. The mean clutch size is about 7 eggs in the study sites. This values is significantly lower than mean clutch size both in more northern localities and in Salamanca (western Spain)

On the multiplicity of the zero-age main-sequence O star Herschel 36

We present the analysis of high-resolution optical spectroscopic observations of the zero-age main-sequence O star Herschel 36 spanning six years. This star is definitely a multiple system, with at least three components detected in its spectrum. Based on our radial-velocity (RV) study, we propose a picture of a close massive binary and a more distant companion, most probably in wide orbit about each other. The orbital solution for the binary, whose components we identify as O9 V and B0.5 V, is characterized by a period of 1.5415 +/- 0.0006 days. With a spectral type O7.5 V, the third body is the most luminous component of the system and also presents RV variations with a period close to 498 days. Some possible hypotheses to explain the variability are briefly addressed and further observations are suggested.Comment: 6 pages, 2 figure

Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization

[EN] In this paper, we present an approach to multi-document summarization based on Siamese Hierarchical Attention Neural Networks. The attention mechanism of Hierarchical Attention Networks, provides a score to each sentence in function of its relevance in the classification process. For the summarization process, only the scores of sentences are used to rank them and select the most salient sentences. In this work we explore the adaptability of this model to the problem of multi-document summarization (typically very long documents where the straightforward application of neural networks tends to fail). The experiments were carried out using the CNN/DailyMail as training corpus, and the DUC-2007 as test corpus. Despite the difference between training set (CNN/DailyMail) and test set (DUC-2007) characteristics, the results show the adequacy of this approach to multi-document summarization.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R). Work of Jose-Angel Gonzalez is also financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Julien Delonca; Sanchís Arnal, E.; García-Granada, F.; Segarra Soriano, E. (2019). Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization. PROCESAMIENTO DEL LENGUAJE NATURAL. (63):111-118. https://doi.org/10.26342/2019-63-12S1111186

Environmental Geology of the Tinto river using concept mapping technique.

En esta ponencia se presenta un mapa conceptual sobre las condiciones geoambientales del río Tinto (Huelva), uno de los ecosistemas más singulares de nuestro planeta debido a la extrema acidez y a las elevadas concentraciones de sulfatos y metales pesados que transportan sus aguas. El mapa conceptual se ha elaborado con la herramienta FreeMind (software libre basado en Java) y aborda conceptos troncales, como el origen y las fuentes de contaminación hídrica, que derivan de forma progresiva y jerárquica hacia aspectos más específicos y complejos, como el proceso de generación de aguas ácidas, el impacto ambiental sobre el medio físico, biológico y socioeconómico, y la composición química y mineralógica de los sedimentos y suelos afectados.In this paper, we present a concept map for organizing and representing knowledge about geo-environmental conditions of the Tinto River (Huelva), a unique ecosystem on Earth due to the extreme acidity and high concentrations of sulphates and heavy metals dissolved in the water. The concept map was drawn with the software FreeMind (a mind-mapping tool based on Java) showing how that specific topics, such as processes of acid mine drainage formation, environmental impacts, and chemical and mineralogical composition of sediments and soils affected, stemming from the main concepts (origin, sources and effects of water pollution)

Attentional Extractive Summarization

[EN] In this work, a general theoretical framework for extractive summarization is proposed¿the Attentional Extractive Summarization framework. Although abstractive approaches are generally used in text summarization today, extractive methods can be especially suitable for some applications, and they can help with other tasks such as Text Classification, Question Answering, and Information Extraction. The proposed approach is based on the interpretation of the attention mechanisms of hierarchical neural networks, which compute document-level representations of documents and summaries from sentence-level representations, which, in turn, are computed from word-level representations. The models proposed under this framework are able to automatically learn relationships among document and summary sentences, without requiring Oracle systems to compute the reference labels for each sentence before the training phase. These relationships are obtained as a result of a binary classification process, the goal of which is to distinguish correct summaries for documents. Two different systems, formalized under the proposed framework, were evaluated on the CNN/DailyMail and the NewsRoom corpora, which are some of the reference corpora in the most relevant works on text summarization. The results obtained during the evaluation support the adequacy of our proposal and suggest that there is still room for the improvement of our attentional framework.This work is partially supported by MCIN/AEI/10.13039/501100011033 and by the European Union "NextGenerationEU/PRTR" under grants PDC2021-120846-C44 (AMIC-PoC-UPV) and PID2021-126061OB-C41 (BEWORD-UPV).González-Barba, JÁ.; Segarra Soriano, E.; García-Granada, F.; Sanchís Arnal, E.; Hurtado Oliver, LF. (2023). Attentional Extractive Summarization. Applied Sciences. 13(3):1-22. https://doi.org/10.3390/app1303145812213

Extractive summarization using siamese hierarchical transformer encoders

[EN] In this paper, we present an extractive approach to document summarization, the Siamese Hierarchical Transformer Encoders system, that is based on the use of siamese neural networks and the transformer encoders which are extended in a hierarchical way. The system, trained for binary classification, is able to assign attention scores to each sentence in the document. These scores are used to select the most relevant sentences to build the summary. The main novelty of our proposal is the use of self-attention mechanisms at sentence level for document summarization, instead of using only attentions at word level. The experimentation carried out using the CNN/DailyMail summarization corpus shows promising results in-line with the state-of-the-art.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R). Work of Jose Angel Gonzalez is also financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Segarra Soriano, E.; García-Granada, F.; Sanchís Arnal, E.; Hurtado Oliver, LF. (2020). Extractive summarization using siamese hierarchical transformer encoders. Journal of Intelligent & Fuzzy Systems. 39(2):2409-2419. https://doi.org/10.3233/JIFS-179901S24092419392Begum N. , Fattah M. and Ren F. , Automatic text summarization using support vector machine, 5 (2009), 1987–1996.González, J.-Á., Segarra, E., García-Granada, F., Sanchis, E., & Hurtado, L.-F. (2019). Siamese hierarchical attention networks for extractive summarization. Journal of Intelligent & Fuzzy Systems, 36(5), 4599-4607. doi:10.3233/jifs-179011Lloret, E., & Palomar, M. (2011). Text summarisation in progress: a literature review. Artificial Intelligence Review, 37(1), 1-41. doi:10.1007/s10462-011-9216-zLouis, A., & Nenkova, A. (2013). Automatically Assessing Machine Summary Content Without a Gold Standard. Computational Linguistics, 39(2), 267-300. doi:10.1162/coli_a_00123Tur G. and De Mori R. , Spoken language understanding: Systems for extracting semantic information from speech. John Wiley & Sons, 2011

Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks

[EN] In this paper, we present an approach to Spanish talk shows summarization. Our approach is based on the use of Siamese Neural Networks on the transcription of the show audios. Specifically, we propose to use Hierarchical Attention Networks to select the most relevant sentences for each speaker about a given topic in the show, in order to summarize his opinion about the topic. We train these networks in a siamese way to determine whether a summary is appropriate or not. Previous evaluation of this approach on summarization task of English newspapers achieved performances similar to other state-of-the-art systems. In the absence of enough transcribed or recognized speech data to train our system for talk show summarization in Spanish, we acquire a large corpus of document-summary pairs from Spanish newspapers and we use it to train our system. We choose this newspapers domain due to its high similarity with the topics addressed in talk shows. A preliminary evaluation of our summarization system on Spanish TV programs shows the adequacy of the proposal.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R). Work of Jose-Angel Gonzalez is financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Hurtado Oliver, LF.; Segarra Soriano, E.; García-Granada, F.; Sanchís Arnal, E. (2019). Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks. Applied Sciences. 9(18):1-13. https://doi.org/10.3390/app9183836S113918Carbonell, J., & Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’98. doi:10.1145/290941.291025Erkan, G., & Radev, D. R. (2004). LexRank: Graph-based Lexical Centrality as Salience in Text Summarization. Journal of Artificial Intelligence Research, 22, 457-479. doi:10.1613/jair.1523Lloret, E., & Palomar, M. (2011). Text summarisation in progress: a literature review. Artificial Intelligence Review, 37(1), 1-41. doi:10.1007/s10462-011-9216-zSee, A., Liu, P. J., & Manning, C. D. (2017). Get To The Point: Summarization with Pointer-Generator Networks. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). doi:10.18653/v1/p17-1099Narayan, S., Cohen, S. B., & Lapata, M. (2018). Ranking Sentences for Extractive Summarization with Reinforcement Learning. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). doi:10.18653/v1/n18-1158González, J.-Á., Segarra, E., García-Granada, F., Sanchis, E., & Hurtado, L.-F. (2019). Siamese hierarchical attention networks for extractive summarization. Journal of Intelligent & Fuzzy Systems, 36(5), 4599-4607. doi:10.3233/jifs-179011Furui, S., Kikuchi, T., Shinnaka, Y., & Hori, C. (2004). Speech-to-Text and Speech-to-Speech Summarization of Spontaneous Speech. IEEE Transactions on Speech and Audio Processing, 12(4), 401-408. doi:10.1109/tsa.2004.828699Shih-Hung Liu, Kuan-Yu Chen, Chen, B., Hsin-Min Wang, Hsu-Chun Yen, & Wen-Lian Hsu. (2015). Combining Relevance Language Modeling and Clarity Measure for Extractive Speech Summarization. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(6), 957-969. doi:10.1109/taslp.2015.2414820Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical Attention Networks for Document Classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. doi:10.18653/v1/n16-1174Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017). Supervised Learning of Universal Sentence Representations from Natural Language Inference Data. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. doi:10.18653/v1/d17-1070Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407. doi:10.1002/(sici)1097-4571(199009)41:63.0.co;2-