Auscultación de presas y su aplicación al estudio de gradientes hidráulicos

Las características morfológicas de la Presa de Las Fitas, presa de materiales sueltos con núcleo de arcilla. Así como las propias del terreno en que se ubica (compuesto por un sustrato terciario dominado por argilitas y limolitas con paquetes intercalados de arenisca de pequeño espesor), obliga a tener controladas en todo momento las filtraciones de agua a través del cimiento de la presa. Para ello se disponen de una serie de elementos de auscultación que de modo continuo están recogiendo información, tanto del cuerpo de presa como de sus interacciones con distintos elementos del contorno. En base a los datos de necesario control para prevenir y salvaguardar la integridad de la presa, se diseñaron unas secciones tipo con la distinta instrumentación que se requería. Del mismo modo, aguas abajo y fuera del cuerpo de presa, se disponen aforadores que permiten controlar las filtraciones a través de la cerrada. Con el análisis de los datos de esta instrumentación, especialmente en la fase de puesta en carga, se obtienen una serie de conclusiones que van a condicionar la futura explotación del embalse

Sostenibilidad del proyecto de regulación y modernización del canal de Terreu

El sistema de Riegos del Alto Aragón, con fuentes de suministro muy distantes de la zona regable, obliga a largas conducciones que dificultan el suministro de agua a las demandas reales de las zonas, tanto en lo referente al caudal como a la regulación del mismo. El Canal de Terreu con una longitud total de 49,220 Km pertenece al Sistema de Riegos del Alto Aragón, tiene su origen en el Canal del Cinca del que deriva su caudal actual, dominando una superficie de 25.101 hectáreas de regadío con una dotación de 0,67 l/s/Ha. En la gestión interna del Sistema de Riegos del Alto Aragón es común la regulación con embalses de los principales canales, tal y como sucede en la cola del canal del Cinca (Embalse de Valdabra) o en el Canal del Flumen (Embalse del Torrollón

Early detection of peritoneal dialysis complications through convolutional neural networks

La diálisis peritoneal es una alternativa para pacientes con insuficiencia renal crónica, que requiere el análisis periódico del líquido resultante para la detección precoz de complicaciones. Dicho análisis implica la evaluación directa del líquido bajo microscopio y una posterior prueba bioquímica. Como alternativa, el líquido podría analizarse a través de una fotografía (evaluación indirecta) lo que permitiría detectar precozmente una posible complicación, sin que el paciente deba acercarse a un centro de nefrología, mejorando sustancialmente su calidad de vida. En [Comas et al., XX Congreso Argentino de Bioingeniería, pp. 477–486 (2015)] se estudió preliminarmente la detección de muestras patológicas del líquido a partir de fotografías, utilizando descriptores de color y el clasificador k-vecinos más próximos. En el presente trabajo, se presenta un método basado en redes neuronales convolucionales, partiendo de la Alexnet y utilizando transfer learning. La fase de clasificación se implementó con un perceptrón multicapa, clasificando las fotografías entre “normal” y “patológica”, con el resultado de la prueba bioquímica como Gold-standard. Se obtuvo una tasa de error de 5,79%, una FPR de 4,21% y una FNR de 7,37%, con gran estabilidad, reflejada en bajas desviaciones estándar en la estimación de las medidas de error. El método propuesto es más robusto que el enfoque previo, sin requerir ningún tipo de preprocesamiento, ni extracción de características, siendo un buen punto de partida para el desarrollo de una herramienta automática con adecuada capacidad de soporte al diagnóstico.Peritoneal dialysis is an alternative for patients with chronic renal failure requiring periodic analysis of the resulting liquid for the early detection of complications, which involves a direct evaluation of the liquid under a microscope and a biochemical test. Alternatively, the liquid could be analyzed through a photograph (indirect evaluation), enabling the early detection of complications, without requiring the patient going to a nephrology center, improving their life quality. In [Comas et al., XX Congreso Argentino de Bioingeniería, pp. 477–486 (2015)], detection of pathological samples of the liquid from photographs was preliminary studied using color descriptors and k-nearest neighbors as classifier. In the present paper, a method based on convolutional neural networks is presented, starting from Alexnet and using transfer learning. The classification phase was implemented with a multilayer perceptron, classifying the photographs between “normal” and “pathological”, using the biochemical test as Gold-standard. An error rate of 5.79%, a FPR of 4.21% and a FNR of 7.37% were obtained with great stability, reflected in low standard deviations in the estimation of the error measures. The proposed method is more robust than the previous approach, without requiring any preprocessing or feature extraction, being a good starting point for the development of an automatic tool with adequate diagnostic capacity.Fil: Comas, Diego Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Meschino, Gustavo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Ballarin, Virginia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Jerónimo Aguilera Díaz. Hospital Italiano; ArgentinaFil: Musso, Carlos. Hospital Italiano; ArgentinaFil: Rivera, Héctor. Hospital Italiano; ArgentinaFil: Plazzotta, Fernando. Hospital Italiano; ArgentinaFil: Algranati, Luis. Hospital Italiano; ArgentinaFil: Luna, Daniel. Hospital Italiano; Argentin

Impact of the implementation of best practice guidelines on nurse's evidence-based practice and on nurses' work environment: research protocol

Abstract Aim: To determine the impact of the Best Practice Spotlight Organization® initia- tive on nurses' perception of their work environment and their attitudes to evidence- based practice. Design: Quasi-experimental, multicentre study. The intervention is the participation in Best Prectice Spotilight Organizations to implement Best Practice Guidelines. Methods: The study will include seven centres in the interventional group and 10 in the non-equivalent control group, all of them belonging to the Spanish national health system. The Practice Environment Scale of the Nursing Work Index, and the Health Sciences Evidence-Based Practice Questionnaire will be administered to a sample of 1,572 nurses at the beginning of the programme and at 1 year. This 3-year study started in April 2018 and will continue until December 2021. Statistical analy- ses will be carried out using the SPSS 25.0. This project was approved by the Drug Research Ethics Committee of the Parc de Salut Mar and registered in Clinical Trials. Discussion: The study findings will show the current state of nurses' perception of their work environment and attitudes to evidence-based practice, and possible changes in these parameters due to the programme. Impact: The findings could provide a strong argument for health policymakers to scale up the Best Practice Spotlight Organization® initiative in the Spanish national health system

Aligned ovine diaphragmatic myoblasts overexpressing human connexin-43 seeded on poly (L-lactic acid) scaffolds for potential use in cardiac regeneration

Diaphragmatic myoblasts (DMs) are precursors of type-1 muscle cells displaying high exhaustion threshold on account that they contract and relax 20 times/min over a lifespan, making them potentially useful in cardiac regeneration strategies. Besides, it has been shown that biomaterials for stem cell delivery improve cell retention and viability in the target organ. In the present study, we aimed at developing a novel approach based on the use of poly (L-lactic acid) (PLLA) scaffolds seeded with DMs overexpressing connexin-43 (cx43), a gap junction protein that promotes inter-cell connectivity. DMs isolated from ovine diaphragm biopsies were characterized by immunohistochemistry and ability to differentiate into myotubes (MTs) and transduced with a lentiviral vector encoding cx43. After confirming cx43 expression (RT-qPCR and Western blot) and its effect on inter-cell connectivity (fluorescence recovery after photobleaching), DMs were grown on fiberaligned or random PLLA scaffolds. DMs were successfully isolated and characterized. Cx43 mRNA and protein were overexpressed and favored inter-cell connectivity. Alignment of the scaffold fibers not only aligned but also elongated the cells, increasing the contact surface between them. This novel approach is feasible and combines the advantages of bioresorbable scaffolds as delivery method and a cell type that on account of its features may be suitable for cardiac regeneration. Future studies on animal models of myocardial infarction are needed to establish its usefulness on scar reduction and cardiac function.Centro de Investigaciones Cardiovasculare

On the influence of model fragment properties on a machine learning-based approach for feature location

[EN] Context: Leveraging machine learning techniques to address feature location on models has been gaining attention. Machine learning techniques empower software product companies to take advantage of the knowledge and the experience to improve the performance of the feature location process. Most of the machine learning-based works for feature location on models report the machine learning techniques and the tuning parameters in detail. However, these works focus on the size and the distribution of the data sets, neglecting the properties of their contents. Objective: In this paper, we analyze the influence of three model fragment properties (density, multiplicity, and dispersion) on a machine learning-based approach for feature location. Method: The analysis of these properties is based on an industrial case provided by CAF, a worldwide provider of railway solutions. The test cases were evaluated through a machine learning technique that uses different subsets of a knowledge base to learn how to locate unknown features. Results: Results show that the density and dispersion properties have a direct impact on the results. In our case study, the model fragments with extra-small density values achieve results with up to 43% more precision, 41% more recall, 42% more F-measure, and 0.53 more Matthews Correlation Coefficient (MCC) than the model fragments with other density values. On the other hand, the model fragments with extra-small and small dispersion values achieve results with up to 53% more precision, 52% more recall, 52% more F-measure, and 0.57 more MCC than the model fragments with other dispersion values. Conclusions: The analysis of the results shows that both density and dispersion properties significantly influence the results. These results can serve not only to improve the reports by means of the model fragment properties, but also to be able to compare machine learning-based feature location approaches fairly improving the feature location results.This work has been partially supported by the Ministry of Economy and Competitiveness (MINECO), Spain through the Spanish National R+D+i Plan and ERDF funds under the Project ALPS (RTI2018096411-B-I00). We also thank the ITEA3 15010 REVaMP2 Project and ACIF/2018/171.Ballarin, M.; Marcén, AC.; Pelechano Ferragud, V.; Cetina, C. (2021). On the influence of model fragment properties on a machine learning-based approach for feature location. Information and Software Technology. 129:1-19. https://doi.org/10.1016/j.infsof.2020.106430S119129Marcén, A. C., Lapeña, R., Pastor, Ó., & Cetina, C. (2020). Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case. Journal of Systems and Software, 163, 110519. doi:10.1016/j.jss.2020.110519Pérez, F., Font, J., Arcega, L., & Cetina, C. (2019). Collaborative feature location in models through automatic query expansion. Automated Software Engineering, 26(1), 161-202. doi:10.1007/s10515-019-00251-9ZHUANG, X., ENGEL, B. A., LOZANO-GARCIA, D. F., FERNÁNDEZ, R. N., & JOHANNSEN, C. J. (1994). Optimization of training data required for neuro-classification. International Journal of Remote Sensing, 15(16), 3271-3277. doi:10.1080/01431169408954326Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1335-1343. doi:10.1109/tgrs.2004.827257Foody, G. M., Mathur, A., Sanchez-Hernandez, C., & Boyd, D. S. (2006). Training set size requirements for the classification of a specific class. Remote Sensing of Environment, 104(1), 1-14. doi:10.1016/j.rse.2006.03.004Weiss, G. M., & Provost, F. (2003). Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction. Journal of Artificial Intelligence Research, 19, 315-354. doi:10.1613/jair.1199Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249-259. doi:10.1016/j.neunet.2018.07.011Arcuri, A., & Fraser, G. (2013). Parameter tuning or default values? An empirical investigation in search-based software engineering. Empirical Software Engineering, 18(3), 594-623. doi:10.1007/s10664-013-9249-9Lapeña, R., Font, J., Pastor, Ó., & Cetina, C. (2017). Analyzing the impact of natural language processing over feature location in models. ACM SIGPLAN Notices, 52(12), 63-76. doi:10.1145/3170492.3136052Shabtai, A., Moskovitch, R., Elovici, Y., & Glezer, C. (2009). Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey. Information Security Technical Report, 14(1), 16-29. doi:10.1016/j.istr.2009.03.003Song, Q., Jia, Z., Shepperd, M., Ying, S., & Liu, J. (2011). A General Software Defect-Proneness Prediction Framework. IEEE Transactions on Software Engineering, 37(3), 356-370. doi:10.1109/tse.2010.90Cao, Z., Tian, Y., Le, T.-D. B., & Lo, D. (2018). Rule-based specification mining leveraging learning to rank. Automated Software Engineering, 25(3), 501-530. doi:10.1007/s10515-018-0231-zArcuri, A., & Briand, L. (2012). A Hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering. Software Testing, Verification and Reliability, 24(3), 219-250. doi:10.1002/stvr.1486García, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences, 180(10), 2044-2064. doi:10.1016/j.ins.2009.12.010Falessi, D., Di Penta, M., Canfora, G., & Cantone, G. (2016). Estimating the number of remaining links in traceability recovery. Empirical Software Engineering, 22(3), 996-1027. doi:10.1007/s10664-016-9460-6Jialei Wang, Peilin Zhao, Hoi, S. C. H., & Rong Jin. (2014). Online Feature Selection and Its Applications. IEEE Transactions on Knowledge and Data Engineering, 26(3), 698-710. doi:10.1109/tkde.2013.3

Interval type-2 fuzzy predicates for brain magnetic resonance image segmentation

The analysis of structural changes in the brain through Magnetic Resonance Imaging (MRI) provides useful information for diagnosis and clinical treatment of patients with pathologies like Alzheimer disease and dementia. While complexity achieved by the MRI equipment is high, quantification of structures and tissues has not been entirely solved. In the present paper, MRI segmentation is discussed using a new classification method called Type-2 Label-based Fuzzy Predicate Classification (T2-LFPC). From labeled data (pixels of different tissues selected by medical experts) a random partition is defined and the obtained subsets are analyzed discovering groups with similar properties called class prototypes. Using theses prototypes, interval type-2 membership functions and fuzzy predicates are defined. Parameters regarding the fuzzy predicates are optimized. Fuzzy predicates are applied on unlabeled pixels performing the segmentation and volumes occupied for the tissues into the intracranial cavity are computed. Results are compared to those of known methods. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist. Results show that the performance of the proposed method is highly acceptable as a contribution for this requirement. Advantages of this approach are presented throughout this paper.The analysis of structural changes in the brain through Magnetic Resonance Imaging (MRI) provides useful information for diagnosis and clinical treatment of patients with pathologies like Alzheimer disease and dementia. While complexity achieved by the MRI equipment is high, quantification of structures and tissues has not been entirely solved. In the present paper, MRI segmentation is discussed using a new classification method called Type-2 Label-based Fuzzy Predicate Classification (T2-LFPC). From labeled data (pixels of different tissues selected by medical experts) a random partition is defined and the obtained subsets are analyzed discovering groups with similar properties called class prototypes. Using theses prototypes, interval type-2 membership functions and fuzzy predicates are defined. Parameters regarding the fuzzy predicates are optimized. Fuzzy predicates are applied on unlabeled pixels performing the segmentation and volumes occupied for the tissues into the intracranial cavity are computed. Results are compared to those of known methods. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist. Results show that the performance of the proposed method is highly acceptable as a contribution for this requirement. Advantages of this approach are presented throughout this paper.Fil: Comas, Diego Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas En Electronica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas En Electronica.; ArgentinaFil: Meschino, Gustavo Javier. Universidad FASTA ; ArgentinaFil: Costantino, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas En Electronica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas En Electronica.; ArgentinaFil: Capiel, Carlos. Instituto Radiologico Mar del Plata; Argentina. Universidad FASTA ; ArgentinaFil: Ballarin, Virginia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas En Electronica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas En Electronica.; Argentina. Universidad FASTA ; Argentin

Tissue discrimination in magnetic resonance imaging of the rotator cuff

Evaluation and diagnosis of diseases of the muscles within the rotator cuff can be done using different modalities, being the Magnetic Resonance the method more widely used. There are criteria to evaluate the degree of fat infiltration and muscle atrophy, but these have low accuracy and show great variability inter and intra observer. In this paper, an analysis of the texture features of the rotator cuff muscles is performed to classify them and other tissues. A general supervised classification approach was used, combining forward-search as feature selection method with kNN as classification rule. Sections of Magnetic Resonance Images of the tissues of interest were selected by specialist doctors and they were considered as Gold Standard. Accuracies obtained were of 93% for T1-weighted images and 92% for T2-weighted images. As an immediate future work, the combination of both sequences of images will be considered, expecting to improve the results, as well as the use of other sequences of Magnetic Resonance Images. This work represents an initial point for the classification and quantification of fat infiltration and muscle atrophy degree. From this initial point, it is expected to make an accurate and objective system which will result in benefits for future research and for patients' health.Fil: Meschino, Gustavo Javier. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Departamento de Ingeniería Eléctrica. Laboratorio de Bioingeniería; Argentina. Universidad FASTA "Santo Tomas de Aquino"; ArgentinaFil: Comas, Diego Sebastián. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Gonzalez, Mariela Azul. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Departamento de Ingeniería Eléctrica. Laboratorio de Bioingeniería; Argentina. Universidad FASTA "Santo Tomas de Aquino"; ArgentinaFil: Capiel, Carlos Alfredo. Universidad FASTA "Santo Tomas de Aquino"; ArgentinaFil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina. Universidad FASTA "Santo Tomas de Aquino"; Argentin