FLORA LIQUÉNICA EPIFÍTICA DE CÁDIZ. l. LOS ALCORNOCALES DE LAS SIERRAS DE ALGECIRAS

This first contribution to the lichen epiphytic flora of the province of Cádiz includes ecological data and comments on literature records from this and nearby territories (portuguese Algarve and the spanish province of Huelva). The species distribution approximation is drawn based on bioclimate of several stations. The catalogue includes records of interest, either because they are new for the spanish lichen flora or because of their chorological relevance. among them: Buellia jorgei G. Samp., Caloplaca pollini (Massal.) Jatta, Fuscidea cyathoides (Ach.) V. Wirth & Vezda, Lecanactis patellarioides (Nyl.) Vainio, Lecanora balearicaRealizamos una primera aportaci6n al conocimiento de la flora epifítica de la provincia de Cádiz. Se incluyen datos ecológicos y se comentan las citas bibliográficas de éste y otros territorios próximos (Algarve portugués y provincia española de Huelva). Presentamos una aproximación a la distribución de las especies en función de las variables bioclimáticas de las distintas estaciones. Entre los táxones del catálogo se incluyen algunos interesantes por ser novedad para la flora española o por su significación corológica, entre ellos destacamos: Buellia jorgei G. Samp., Caloplaca pollinii (Massal.) Jatta, Fuscidea cyathoides (Ach.) V. Wirth & Vézda, Lecanactis patellarioides (Nyl.) Vainio, Lecanora balearica Crespo & Llimona, Maronea constans (Nyl.) Hepp., Parmotrema austrosinense (Zahlbr.) Hale, Parmotrema hypoleucinum (Steiner) Hale, Parmotrema stupeum (Taylor) Hale, Pertusaria caesioalba (Flotow) Nyl., Pertusaria dalmatita Erichsen, Pertusaria ficorum Zahlbr., Pertusaria lecanorodes Erichsen, Pertusaria maximiliana Klem., Pyrrhospora quernea (Dickson) Koerber, Rinodina pruinella Bagl. y Sorothelia confluens Koerber. De todos los táxones catalogados se menciona los pliegos MAF lich testigo

Valoració dels estudiants de l’assignatura pràcticum del grau en infermeria en les unitats de salut maternal i salut sexual i reproductiva

Estudi vinculat al projecte: ACOPI-UB - Aprenentatge de Competències Professionals en Infermeria. GINDOC-UB/145[cat] Introducció. Estudi sobre la valoració dels estudiants que han cursat l'assignatura Pràcticum i realitzat la formació clínica, en les unitats de salut maternal i salut sexual i reproductiva en els centres assistencials de Barcelona ciutat. Objectiu. Identificar el grau de satisfacció dels estudiants en relació a l'assignatura pràcticum del Grau en Infermeria. Mètode. Disseny. Estudi descriptiu i transversal. Subjectes i emplaçament. N=170 estudiants de pràcticum de l'Escola d'Infermeria de la UB, de quatre cursos acadèmics. Unitats d'hospitalització de ginecologia, obstetrícia, urgències, cures obstètriques intensives de l'HCB i seu Maternitat, i atenció a la salut sexual i reproductiva (ASSIR) d'atenció primària de Barcelona ciutat. Recollida de dades. Qüestionari que recollia entre altres variables: grau d'assoliment dels objectius, grau de satisfacció activitats d'aprenentatge programades i la unitat de pràctiques. Els ítems s’avaluaven mitjançant una escala tipus Lickert (1-10). Anàlisi estadística programa PASW.22. Resultats. Es van obtenir 87 enquestes (51,2%). Principals resultats: grau d'assoliment dels objectius (mitjana) de 8,70 ± 0,94 (6-10), grau de satisfacció dels seminaris (mitjana) 8,05 ± 1,65 (1-10), grau de satisfacció del diari reflexiu (mitjana) 7,43 ± 1,65 (1-10), grau de satisfacció del procés de cures (mitjana) 7,15 ± 1,75 (1-10) i la unitat de pràctiques és adequada per a la consecució dels objectius (mitjana) 8,46 ± 1,33 (5-10). Conclusió. A partir dels resultats plantegem revisar les activitats programades per millorar el procés d'aprenentatge en les pràctiques clíniques.[spa] Introducción. Estudio sobre la valoración de los estudiantes que han cursado la asignatura Prácticum y realizado la formación clínica, en las unidades de salud maternal y salud sexual y reproductiva en los centros asistenciales de Barcelona ciudad. Objetivo. Identificar el grado de satisfacción de los estudiantes en relación a la asignatura Prácticum del Grado en Enfermería. Método. Diseño. Estudio descriptivo y transversal. Sujetos y emplazamiento. N = 170 estudiantes de prácticum de la Escuela de Enfermería de la UB, de cuatro cursos académicos. Unidades de hospitalización de ginecología, obstetricia, urgencias, cuidados obstétricos intensivos del HCB y sede Maternidad, y atención a la salud sexual y reproductiva (ASSIR) de atención primaria de Barcelona ciudad. Recogida de datos. Cuestionario que recogía entre otras variables: grado de consecución de los objetivos, grado de satisfacción de las actividades de aprendizaje programadas y la unidad de prácticas. Los ítems se evaluaban mediante una escala tipo Likert (1-10). Análisis estadístico programa PASW.22. Resultados. Se obtuvieron 87 encuestas (51,2%). Principales resultados: grado de consecución de los objetivos (media) de 8,70 ± 0,94 (6-10), grado de satisfacción de los seminarios (media) 8,05 ± 1,65 (1-10), grado de satisfacción del diario reflexivo (media) 7,43 ± 1,65 (1-10), grado de satisfacción del proceso de cuidados (media) 7,15 ± 1,75 (1-10) y la unidad de prácticas es adecuada para la consecución de los objetivos (media) 8,46 ± 1,33 (5-10). Conclusión. A partir de los resultados planteamos revisar las actividades programadas para mejorar el proceso de aprendizaje en las prácticas clínicas.Programa de Millora i Innovació Docent (PMID). Universitat de Barcelona

A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers

[EN] Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 degrees C in the same day) stands out. This causes the stone fruit trees to flower prematurely and the low winter temperatures freeze the flower causing the loss of the crop. Farmers use anti-freeze techniques to prevent crop loss and the most widely used techniques are those that use water irrigation as they are cheaper than other techniques. However, these techniques waste too much water and it is a scarce resource, especially in this area. In this article, we propose a novel intelligent Internet of Things (IoT) monitoring system to optimize the use of water in these anti-frost techniques while minimizing crop loss. The intelligent component of the IoT system is designed using an approach based on a multivariate Long Short-Term Memory (LSTM) model, designed to predict low temperatures. We compare the proposed approach of multivariate model with the univariate counterpart version to figure out which model obtains better accuracy to predict low temperatures. An accurate prediction of low temperatures would translate into significant water savings, as anti-frost techniques would not be activated without being necessary. Our experimental results show that the proposed multivariate LSTM approach improves the univariate counterpart version, obtaining an average quadratic error no greater than 0.65 degrees C and a coefficient of determination R2 greater than 0.97. The proposed system has been deployed and is currently operating in a real environment obtained satisfactory performance.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302.Guillén-Navarro, MA.; Martínez-España, R.; Bueno-Crespo, A.; Morales-García, J.; Ayuso, B.; Cecilia-Canales, JM. (2020). A Decision Support System for Water Optimization in Anti-Frost Techniques by Sprinklers. Sensors. 20(24):1-15. https://doi.org/10.3390/s20247129S1152024Melgarejo-Moreno, J., López-Ortiz, M.-I., & Fernández-Aracil, P. (2019). Water distribution management in South-East Spain: A guaranteed system in a context of scarce resources. Science of The Total Environment, 648, 1384-1393. doi:10.1016/j.scitotenv.2018.08.263Ferrández-Pastor, F., García-Chamizo, J., Nieto-Hidalgo, M., & Mora-Martínez, J. (2018). Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context. Sensors, 18(6), 1731. doi:10.3390/s18061731Liaghat. (2010). A Review: The Role of Remote Sensing in Precision Agriculture. American Journal of Agricultural and Biological Sciences, 5(1), 50-55. doi:10.3844/ajabssp.2010.50.55Nelson, G. C., van der Mensbrugghe, D., Ahammad, H., Blanc, E., Calvin, K., Hasegawa, T., … Willenbockel, D. (2013). Agriculture and climate change in global scenarios: why don’t the models agree. Agricultural Economics, 45(1), 85-101. doi:10.1111/agec.12091Crookston, R. K. (2006). A Top 10 List of Developments and Issues Impacting Crop Management and Ecology During the Past 50 Years. Crop Science, 46(5), 2253-2262. doi:10.2135/cropsci2005.11.0416gasDutta, R., Morshed, A., Aryal, J., D’Este, C., & Das, A. (2014). Development of an intelligent environmental knowledge system for sustainable agricultural decision support. Environmental Modelling & Software, 52, 264-272. doi:10.1016/j.envsoft.2013.10.004Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018). Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918-929. doi:10.1016/j.jhydrol.2018.04.065Sahoo, S., Russo, T. A., Elliott, J., & Foster, I. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resources Research, 53(5), 3878-3895. doi:10.1002/2016wr019933Coopersmith, E. J., Minsker, B. S., Wenzel, C. E., & Gilmore, B. J. (2014). Machine learning assessments of soil drying for agricultural planning. Computers and Electronics in Agriculture, 104, 93-104. doi:10.1016/j.compag.2014.04.004Mohammadi, K., Shamshirband, S., Motamedi, S., Petković, D., Hashim, R., & Gocic, M. (2015). Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture, 117, 214-225. doi:10.1016/j.compag.2015.08.008Feng, Y., Peng, Y., Cui, N., Gong, D., & Zhang, K. (2017). Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture, 136, 71-78. doi:10.1016/j.compag.2017.01.027Jin, X.-B., Yu, X.-H., Wang, X.-Y., Bai, Y.-T., Su, T.-L., & Kong, J.-L. (2020). Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability, 12(4), 1433. doi:10.3390/su12041433Castañeda-Miranda, A., & Castaño-Meneses, V. M. (2020). Internet of things for smart farming and frost intelligent control in greenhouses. Computers and Electronics in Agriculture, 176, 105614. doi:10.1016/j.compag.2020.105614Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31-48. doi:10.1016/j.biosystemseng.2017.09.007Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X., & Guo, Y. (2019). State-of-the-Art Internet of Things in Protected Agriculture. Sensors, 19(8), 1833. doi:10.3390/s19081833Jawad, H., Nordin, R., Gharghan, S., Jawad, A., & Ismail, M. (2017). Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors, 17(8), 1781. doi:10.3390/s17081781Guillén‐Navarro, M. A., Martínez‐España, R., López, B., & Cecilia, J. M. (2019). A high‐performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience, 33(2). doi:10.1002/cpe.5299Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J.-C., & Cecilia, J. M. (2020). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840. doi:10.1007/s11227-020-03288-

Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning

[EN] The Internet of Things (IoT) is driving the digital revolution. AlSome palliative measures aremost all economic sectors are becoming "Smart" thanks to the analysis of data generated by IoT. This analysis is carried out by advance artificial intelligence (AI) techniques that provide insights never before imagined. The combination of both IoT and AI is giving rise to an emerging trend, called AIoT, which is opening up new paths to bring digitization into the new era. However, there is still a big gap between AI and IoT, which is basically in the computational power required by the former and the lack of computational resources offered by the latter. This is particularly true in rural IoT environments where the lack of connectivity (or low-bandwidth connections) and power supply forces the search for "efficient" alternatives to provide computational resources to IoT infrastructures without increasing power consumption. In this paper, we explore edge computing as a solution for bridging the gaps between AI and IoT in rural environment. We evaluate the training and inference stages of a deep-learning-based precision agriculture application for frost prediction in modern Nvidia Jetson AGX Xavier in terms of performance and power consumption. Our experimental results reveal that cloud approaches are still a long way off in terms of performance, but the inclusion of GPUs in edge devices offers new opportunities for those scenarios where connectivity is still a challenge.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 (AEI/FEDER, UE) and RTC-2017-6389-5.Guillén-Navarro, MA.; Llanes, A.; Imbernón, B.; Martínez-España, R.; Bueno-Crespo, A.; Cano, J.; Cecilia-Canales, JM. (2021). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing. 77:818-840. https://doi.org/10.1007/s11227-020-03288-w8188407

Valoración de los estudiantes de la asignatura Prácticum del grado de Enfermería en las unidades de Salud Maternal y Salud Sexual y Reproductiva

La asignatura Prácticum del grado en Enfermería la realiza el estudiante en el cuarto curso, octavo semestre, y consta de 30 ECTS. Las prácticas clínicas se distribuyen en dos períodos: uno de ellos en unidades especializadas de Obstetricia y Ginecología, Pediatría, Psiquiatría o Geriatría. En este trabajo se presenta la valoración de los estudiantes que han cursado la asignatura Prácticum y que han realizado la formación clínica en las unida-des de Salud Maternal y Salud Sexual y Reproductiva en los centros asistenciales de Barcelona ciudad

Specialized pro-resolving mediators prevents cardiac dysfunction by modulating Ca2+ handling and NRF2 axis

Trabajo presentado en el ESC Congress 2021 - The Digital Experience, celebrado en modalidad virtual el 27 de agosto de 2021

Assessment of oceanographic services for the monitoring of highly anthropised coastal lagoons: The Mar Menor case study

Ocean monitoring systems are designed for continuous monitoring to track their evolution and anticipate environmental issues. However, they are often based on IoT systems that offer little spatial coverage and are hard to maintain. Satellite remote sensing offers good geographical coverage but they also face several challenges to become a monitoring system. This paper introduces an easy-to-use software tool to crawl water-quality data from up to 6 satellite instruments from the ESA and NASA. Particularly, Chl-a data is deeply analyzed in terms of reliability and data coverage for a highly anthropised coastal lagoon (Mar Menor, Spain), where serious socio-environmental issues are happening. Our results show a good linear correlation between in situ data and SRS data, reaching values close to 0.9, and stating the relevance of organic matter inputs from ephemeral streams in Chl-a concentrations. Moreover, temporal granularity is increased from 5 to 1.5 days by combining SRS sources.Preprin

The effect of excess weight on circulating inflammatory cytokines in drug-naïve first-episode psychosis individuals

Background: Low-grade inflammation has been repeatedly associated with both excess weight and psychosis. However, no previous studies have addressed the direct effect of body mass index (BMI) on basal serum cytokines in individuals with first-episode psychosis (FEP). Objectives: The aim of this study is to analyze the effect of BMI on basal serum cytokine levels in FEP patients and control subjects, separating the total sample into two groups: normal-weight and overweight individuals. Methods: This is a prospective and open-label study. We selected 75 FEP patients and 75 healthy controls with similar characteristics to patients according to the following variables: sex, age, and cannabis and tobacco consumption. Both controls and patients were separated into two groups according to their BMI: subjects with a BMI under 25 were considered as normal weight and those with a BMI equal to or more than 25 were considered as overweight. Serum levels of 21 cytokines/chemokines were measured at baseline using the Human High Sensitivity T Cell Magnetic Bead Panel protocol from the Milliplex® Map Kit. We compared the basal serum levels of the 21 cytokines between control and patient groups according to their BMI. Results: In the normal-weight group, IL-8 was the only cytokine that was higher in patients than in the control group (p = 0.001), whereas in the overweight group, serum levels of two pro-inflammatory cytokines (IL-6, p = 0.000; IL-1?, p = 0.003), two chemokines (IL-8, p = 0.001; MIP-1?, p = 0.001), four Th-1 and Th-2 cytokines (IL-13, p = 0.009; IL-2, p = 0.001; IL-7, p = 0.001; IL-12p70, p = 0.010), and one Type-3 cytokine (IL-23, p = 0.010) were higher in patients than in controls. Conclusions: Most differences in the basal serum cytokine levels between patients and healthy volunteers were found in the overweight group. These findings suggest that excess weight can alter the homeostasis of the immune system and therefore may have an additive pro-inflammatory effect on the one produced by psychosis in the central nervous system.Funding: The present study was carried out at the Hospital Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support from MINECO SAF2013-46292-R, Instituto de Salud Carlos III, and Fundación Marqués de Valdecilla. No pharmaceutical company has participated in the study concept and design, data collection, analysis and interpretation of the results, and drafting of the manuscript. We thank the Valdecilla Biobank for blood sampling handling and storage. We also wish to thank the participants and their families for enrolling in this study. The study, designed and directed by B C-F, conformed to international standards for research ethics and was approved by the local institutional review board

Health‐related quality of life, social support, and caregiver burden between six and 120 months after heart transplantation: a Spanish multicenter cross‐sectional study

[Abstract] A multicenter cross-sectional study was conducted to determine the current heart transplant (HTx) outcomes in Spain. Clinical and functional status, health-related quality of life (HRQoL), social support, and caregiver burden were analyzed in 303 adult transplant recipients (77.9% males) living with one functioning graft. Mean age at time of HTx (SD) was 56.4 (11.4) years, and the reason for transplantation in all patients was congestive heart failure. All patients had received a first heart transplant 6 (± 1), 12 (± 2), 36 (± 6), 60 (± 10), or 120 (± 20) months previously. Participants completed the Kansas City Cardiomyopathy Questionnaire (KCCQ), the EQ-5D, the Duke-UNC Functional Social Support Questionnaire, and the Zarit Caregiver Burden Scale. Reasonable HRQoL, social support, and caregiver burden levels were found at all time points, although a slight decrease in HRQoL was recorded at 120 months (p ≤ 0.033). Multivariate regression analyses showed that complications, comorbidities, and hospitalizations were associated with HRQoL (EQ-5D: 48.4% of explained variance, F4,164 = 38.46, p < 0.001; KCCQ overall summary score: 45.0%, F3,198 = 54.073, p < 0.001). Patient functional capabilities and complications affected caregiver burden (p < 0.05). In conclusion, HTx patients reported reasonable levels of HRQoL with low caregiver burden. Clinical variables related to these outcomes included functional status, complications, and number of admissions

Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images

Introduction: Ovarian tumors are the most common diagnostic challenge for gynecologists and ultrasound examination has become the main technique for assessment of ovarian pathology and for preoperative distinction between malignant and benign ovarian tumors. However, ultrasonography is highly examiner-dependent and there may be an important variability between two different specialists when examining the same case. The objective of this work is the evaluation of different well-known Machine Learning (ML) systems to perform the automatic categorization of ovarian tumors from ultrasound images. Methods: We have used a real patient database whose input features have been extracted from 348 images, from the IOTA tumor images database, holding together with the class labels of the images. For each patient case and ultrasound image, its input features have been previously extracted using Fourier descriptors computed on the Region Of Interest (ROI). Then, four ML techniques are considered for performing the classification stage: K-Nearest Neighbors (KNN), Linear Discriminant (LD), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Results: According to our obtained results, the KNN classifier provides inaccurate predictions (less than 60% of accuracy) independently of the size of the local approximation, whereas the classifiers based on LD, SVM and ELM are robust in this biomedical classification (more than 85% of accuracy). Conclusions: ML methods can be efficiently used for developing the classification stage in computer-aided diagnosis systems of ovarian tumor from ultrasound images. These approaches are able to provide automatic classification with a high rate of accuracy. Future work should aim at enhancing the classifier design using ensemble techniques. Another ongoing work is to exploit different kind of features extracted from ultrasound images