Natural-cycle in vitro fertilization (IVF) combined with in vitro maturation in infertile patients with polycystic ovarian syndrome (PCOS) requiring IVF

REPA is supported by CONACYT (329705) Mexico and Elphinstone scholarships and a fellowship from IBSA Foundation.Peer reviewedPublisher PD

Cómo adaptar un modelo de aprendizaje profundo a un nuevo dominio: el caso de la extracción de relaciones biomédicas

In this article, we study the relation extraction problem from Natural Language Processing (NLP) implementing a domain adaptation setting without external resources. We trained a Deep Learning (DL) model for Relation Extraction (RE), which extracts semantic relations in the biomedical domain. However, can the model be applied to different domains? The model should be adaptable to automatically extract relationships across different domains using the DL network. Completely training DL models in a short time is impractical because the models should quickly adapt to different datasets in several domains without delay. Therefore, adaptation is crucial for intelligent systems, where changing factors and unanticipated perturbations are common. In this study, we present a detailed analysis of the problem, as well as preliminary experimentation, results, and their evaluation.En este trabajo estudiamos el problema de extracción de relaciones del Procesamiento de Lenguaje Natural (PLN). Realizamos una configuración para la adaptación de dominio sin recursos externos. De esta forma, entrenamos un modelo con aprendizaje profundo (DL) para la extracción de relaciones (RE). El modelo permite extraer relaciones semánticas para el dominio biomédico. Sin embargo, ¿El modelo puede ser aplicado a diferentes dominios? El modelo debería adaptarse automáticamente para la extracción de relaciones entre diferentes dominios usando la red de DL. Entrenar completamente modelos DL en una escala de tiempo corta no es práctico, deseamos que los modelos se adapten rápidamente de diferentes conjuntos de datos con varios dominios y sin demora. Así, la adaptación es crucial para los sistemas inteligentes que operan en el mundo real, donde los factores cambiantes y las perturbaciones imprevistas son habituales. En este artículo, presentamos un análisis detallado del problema, una experimentación preliminar, resultados y la discusión acerca de los resultados

Object-Based Image Classification of Summer Crop with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.This research was partly financed by the TIN2011-22794 project of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds, the P2011-TIC-7508 project of the “Junta de Andalucía” (Spain) and the Kearney Foundation of Soil Science (USA). The research of Peña was co-financed by the Fulbright-MEC postdoctoral program, financed by the Spanish Ministry for Science and Innovation, and by the JAEDoc Program, supported by CSIC and FEDER funds. ASTER data were available to us through a NASA EOS scientific investigator affiliation.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).Peer Reviewe

Object-Based Image Classification of Summer Crops with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification task

Electronic structure of crystalline binary and ternary Cd-Te-O compounds

The electronic structure of crystalline CdTe, CdO, $\alpha$-TeO$_2$, CdTeO$_3$ and Cd$_3$TeO$_6$ is studied by means of first principles calculations. The band structure, total and partial density of states, and charge densities are presented. For $\alpha$-TeO$_2$ and CdTeO$_3$, Density Functional Theory within the Local Density Approximation (LDA) correctly describes the insulating character of these compounds. In the first four compounds, LDA underestimates the optical bandgap by roughly 1 eV. Based on this trend, we predict an optical bandgap of 1.7 eV for Cd$_3$TeO$_6$. This material shows an isolated conduction band with a low effective mass, thus explaining its semiconducting character observed recently. In all these oxides, the top valence bands are formed mainly from the O 2p electrons. On the other hand, the binding energy of the Cd 4d band, relative to the valence band maximum, in the ternary compounds is smaller than in CdTe and CdO.Comment: 13 pages, 15 figures, 2 tables. Accepted in Phys Rev

Problemas relacionados con el alcohol: perfil del varón de riesgo.

Estudio de población general realizado mediante una encuesta transversal sobre un universo de 1.365 varones. Su objetivo es dibujar un perfil personal de alto riesgo de padecer problemas relacionados con el alcohol. Un 9% de los entrevistados refiere haber sufrido al menos tres problemas relacionados con el alcohol en el año anterior al estudio. Los consumidores habituales excesivos presentan mayor probabilidad de sufrir tres problemas relacionados con el alcohol. Se confirma la figura del joven, soltero, de medio semiurbano, y perteneciente a la clase social baja como de riesgo especial para sufrir tres o más problemas relacionados con el alcohol, independientemente de su consumo etílico

Problemas relacionados con el alcohol: perfil del varón de riesgo.

Estudio de población general realizado mediante una encuesta transversal sobre un universo de 1.365 varones. Su objetivo es dibujar un perfil personal de alto riesgo de padecer problemas relacionados con el alcohol. Un 9% de los entrevistados refiere haber sufrido al menos tres problemas relacionados con el alcohol en el año anterior al estudio. Los consumidores habituales excesivos presentan mayor probabilidad de sufrir tres problemas relacionados con el alcohol. Se confirma la figura del joven, soltero, de medio semiurbano, y perteneciente a la clase social baja como de riesgo especial para sufrir tres o más problemas relacionados con el alcohol, independientemente de su consumo etílico

Taxonomic distribution of neoplasia among non-domestic felid species under managed care

As evidenced by numerous case reports from zoos, neoplasia in felids is common, but most reports are limited to Panthera species in North America or Europe. In order to obtain a wider epidemiologic understanding of neoplasia distribution, necropsy records at seven facilities (USA, Mexico, Colombia, Peru, and Brazil) were evaluated. In contrast to others, this study population (195 cases, 16 species), included many non-Panthera felids. Overall neoplasia prevalence was 28.2% (55/195). Panthera species had a higher prevalence of neoplasia than non-Panthera species (52.5%; vs. 13.0%). Lions (66.7%), jaguars (55.0%), and tigers (31.3%) had the highest species-specific prevalence of neoplasia. Neoplasms in Panthera species were more frequently malignant than in non-Panthera (86.1% vs. 55.6%). The systems most commonly a_ected were the reproductive, hematolymphoid, and respiratory. The range of management conditions and more varied genetic backgrounds support a robust taxonomic pattern and suggest that the reported propensity for neoplasia in jaguars may have a genetic basis at a taxonomic level higher than species, as lions and tigers also have high prevalence. Given the high prevalence of neoplasia and high likelihood of malignancy, routine medical exams in all nondomestic felids, but Panthera species in particular, should include thorough assessments of any clinical signs of neoplasia

Comparison between Suitable Priors for Additive Bayesian Networks

Additive Bayesian networks are types of graphical models that extend the usual Bayesian generalized linear model to multiple dependent variables through the factorisation of the joint probability distribution of the underlying variables. When fitting an ABN model, the choice of the prior of the parameters is of crucial importance. If an inadequate prior - like a too weakly informative one - is used, data separation and data sparsity lead to issues in the model selection process. In this work a simulation study between two weakly and a strongly informative priors is presented. As weakly informative prior we use a zero mean Gaussian prior with a large variance, currently implemented in the R-package abn. The second prior belongs to the Student's t-distribution, specifically designed for logistic regressions and, finally, the strongly informative prior is again Gaussian with mean equal to true parameter value and a small variance. We compare the impact of these priors on the accuracy of the learned additive Bayesian network in function of different parameters. We create a simulation study to illustrate Lindley's paradox based on the prior choice. We then conclude by highlighting the good performance of the informative Student's t-prior and the limited impact of the Lindley's paradox. Finally, suggestions for further developments are provided.Comment: 8 pages, 4 figure