A research-based methodology to teach Magnetostatics

En este trabajo se describe la aplicación de un ciclo de mejora en el aula en la asignatura Física II de primer curso del Grado en Ingeniería del Diseño Industrial y Desarrollo del Producto. El modelo metodológico propuesto es de tipo investigativo, donde se plantea una pregunta inicial a los alumnos y éstos proponen hipótesis que deberán testear. Las actividades diseñadas incluyen discusión en grupos, fichas de actividades o visionado de experimentos. La comparación de cuestionarios realizados antes y después del ciclo refleja una mejora importante en los niveles de conocimiento de los alumnos respecto a los contenidos del tema. En una encuesta final, los alumnos declararon unánimemente preferir esta metodología a la tradicional

Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation

Medical image segmentation is a challenging task, particularly due to inter- and intra-observer variability, even between medical experts. In this paper, we propose a novel model, called Probabilistic Inter-Observer and iNtra-Observer variation NetwOrk (Pionono). It captures the labeling behavior of each rater with a multidimensional probability distribution and integrates this information with the feature maps of the image to produce probabilistic segmentation predictions. The model is optimized by variational inference and can be trained end-to-end. It outperforms state-of-the-art models such as STAPLE, Probabilistic U-Net, and models based on confusion matrices. Additionally, Pionono predicts multiple coherent segmentation maps that mimic the rater's expert opinion, which provides additional valuable information for the diagnostic process. Experiments on real-world cancer segmentation datasets demonstrate the high accuracy and efficiency of Pionono, making it a powerful tool for medical image analysis.Comment: 13 pages, 5 figure

Deep Gaussian processes for biogeophysical parameter retrieval and model inversion

Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have provided useful and informative solutions to such RTM inversion problems. This is in large part due to the confidence intervals they provide, and their predictive accuracy. However, RTMs are very complex, highly nonlinear, and typically hierarchical models, so that very often a single (shallow) GP model cannot capture complex feature relations for inversion. This motivates the use of deeper hierarchical architectures, while still preserving the desirable properties of GPs. This paper introduces the use of deep Gaussian Processes (DGPs) for bio-geo-physical model inversion. Unlike shallow GP models, DGPs account for complicated (modular, hierarchical) processes, provide an efficient solution that scales well to big datasets, and improve prediction accuracy over their single layer counterpart. In the experimental section, we provide empirical evidence of performance for the estimation of surface temperature and dew point temperature from infrared sounding data, as well as for the prediction of chlorophyll content, inorganic suspended matter, and coloured dissolved matter from multispectral data acquired by the Sentinel-3 OLCI sensor. The presented methodology allows for more expressive forms of GPs in big remote sensing model inversion problems.European Research Council (ERC) 647423Spanish Ministry of Economy and Competitiveness TIN2015-64210-R DPI2016-77869-C2-2-RSpanish Excellence Network TEC2016-81900-REDTLa Caixa Banking Foundation (Barcelona, Spain) 100010434 LCF-BQ-ES17-1160001

Equation Córdoba: A Simplified Method for Estimation of Body Fat (ECORE-BF)

Background: Many methods for measuring body fat have been developed, but applications in clinical settings are limited. For this reason, researchers have tried to identify different formulas for its estimation but most of are hard to incorporate into daily work due to the variability in population and difficulty of use. The aim of this study was to develop and validate a new equation for the simplified estimation of body fat using the Clínica Universidad de Navarra – Body Adiposity Estimator (CUN-BAE) as a reference. Methods: This research was conducted in two phases. In the first, the new body fat estimation equation was developed. The developed equation was validated in the second phase. Pearson’s linear correlation, raw and adjusted linear regressions, the intraclass correlation coefficient, and Bland–Altman graphs were used. Results: The variables that best adjusted the body fat percentage were age, sex, and the Napierian logarithm of Body Mass Index (LnBMI), forming the Equation Córdoba for Estimation of Body Fat (ECORE-BF) model. In its validation, the model presented correlation values of 0.994, an intraclass correlation coefficient of 0.960, with the Bland–Altman graph indicating means differences of 1.82 with respect to the estimation with the CUN-BAE. Nevertheless, although the aim was to simplify the CUN-BAE, the main limitation of this study is that a gold standard, such as air displacement plethysmography (ADP) or dual-energy X-ray absorptiometry (DXA), was not used. Conclusions: The proposed equation (ECORE-BF) simplified the CUN-BAE and provided a precise method, respecting the principle of parsimony, for the calculation of body fat

Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images

In the last years, the weakly supervised paradigm of multiple instance learning (MIL) has become very popular in many different areas. A paradigmatic example is computational pathology, where the lack of patch-level labels for whole-slide images prevents the application of supervised models. Probabilistic MIL methods based on Gaussian Processes (GPs) have obtained promising results due to their excellent uncertainty estimation capabilities. However, these are general-purpose MIL methods that do not take into account one important fact: in (histopathological) images, the labels of neighboring patches are expected to be correlated. In this work, we extend a state-of-the-art GP-based MIL method, which is called VGPMIL-PR, to exploit such correlation. To do so, we develop a novel coupling term inspired by the statistical physics Ising model. We use variational inference to estimate all the model parameters. Interestingly, the VGPMIL-PR formulation is recovered when the weight that regulates the strength of the Ising term vanishes. The performance of the proposed method is assessed in two real-world problems of prostate cancer detection. We show that our model achieves better results than other state-of-the-art probabilistic MIL methods. We also provide different visualizations and analysis to gain insights into the influence of the novel Ising term. These insights are expected to facilitate the application of the proposed model to other research areas.European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No 860627 (CLARIFY Project)Spanish Ministry of Science and Innovation under project PID2019-105142RBC22University of Granada and FEDER/Junta de Andalucía under project B-TIC-324-UGR20 (Proyectos de I+D+i en el marco del Programa Operativo FEDER Andalucía)Margarita Salas postdoctoral fellowship (Spanish Ministry of Universities with Next Generation EU funds

Diagnostic Precision of Anthropometric Variables for the Detection of Hypertension in Children and Adolescents

Introduction: High blood pressure (HBP) is a health problem the prevalence of which has increased in young populations. Overweight and obesity in early ages have been directly related to its development. Due to the impact of HBP, it is necessary to provide tools that facilitate its early diagnosis, with useful anthropometric variables being those that assess obesity. The objective of this paper was to determine the diagnostic accuracy of anthropometric variables to detect HBP. Methods: A cross-sectional study was conducted on 265 students aged 6–16. The diagnosis of HBP was made following the criteria proposed by the Spanish Association of Pediatrics. Through different statistical methods, the association between anthropometric variables of general obesity with HBP was analyzed. Results: Waist circumference (WC) showed the best diagnostic capacity (area under the receiver operating characteristic curve = 0.729), with a sensitivity and specificity of 72.2% and 76%, respectively, for a cut-off point of 73.5 cm. In the adjusted multivariate analysis, an association was found between HBP and anthropometric variables: WC (odds ratio (OR) = 10.7), body mass index (OR = 7.5), waist-to-height ratio (OR = 5.5) and body fat percentage (OR = 5.3) (p < 0.05). Conclusions: The anthropometric variables studied showed a moderate predictive capacity for HBP, highlighting WC, which showed the strongest association with HBP in the infant and child population

Efecto del tamaño de agregado de arcilla en la oxidación de fenol en medio acuoso diluido.

Este trabajo presenta un estudio sobre el efecto del tamaño de agregado de arcilla en el proceso de pilarización en suspensión concentrada, con un precursor polimérico de AlCeFe. Se evalúan dos arcillas esmectíticas colombianas (Bentonita y M64), las cuales se separan por tamaños de agregado ≤2μm, ≤50μm y ≤150μm. Los resultados revelan el éxito de la pilarización de todos los sólidos, independientemente del tamaño de agregado empleado. Las pruebas catalíticas en la oxidación de fenol en medio acuoso diluido muestran, en todos los casos, 100% de conversión de fenol después de 2h de reacción, y eliminación de carbono orgánico total (TOC) entre 50 y 58% después de 4h de reacción

Evaluación del paisaje de la Comunidad de Madrid: de la protección a la gestión territorial

La Comunidad de Madrid ha estimado de interés contar con una caracterización, diagnóstico y evaluación de la calidad del paisaje que permita el establecimiento de criterios de protección y ordenación del territorio, conforme a lo establecido por la Ley 9/2001, de 17 de julio, del Suelo de la Comunidad de Madrid. Como resultado del estudio llevado a cabo, se dispone de una caracterización del patrimonio paisajístico de la Comunidad a escala 1:25.000, de un diagnóstico sintético de las tendencias del paisaje por grandes conjuntos paisajísticos, y de una primera valoración de cada paisaje, que puede constituir, junto a otras variables, un criterio operativo de ordenación territorial, concretamente para la definición de suelos no urbanizables de protección por su interés paisajístico. En el artículo se incide en los aspectos metodológicos de la tipología y caracterización del paisaje madrileño a una escala pertinente para la gestión del territorio y, sobre todo, en los criterios y resultados de valoración, y en su traslación a una propuesta de regulación de usos, de acuerdo con los valores y el estado del paisaje, y con los requerimientos de la legislación urbanística de la ComunidadThe region of Madrid has deemed it of interest to have a description, diagnosis and evaluation of the landscape quality to allow the establishment of protection and zoning criteria for the territory, according to the Land Act 9/2001 of July 17. As a result of the study performed, a landscape survey of the region is available at a scale of 1:25000, as well as a description of the trends in major landscapes and a basic evaluation of each landscape.This last resource may be, together with others, a working criterion for territorial planning, particularly for the zoning of areas not available for residential development due to their landscap