Quantitative analysis of complex nanocomposites based on straight skeletonization

Bones are complex nanocomposites composed mainly by hydroxyapatite nanocrystals. Different factors characterize its morphology: composition, length, orientation, roughness. To increase our understanding of the tissue morphology at this fundamental lever of organization, a new method based on the straight skelonization of the images obtained by electronic microscopy is proposed. The method detects and measures the length and angularity of any straight edge of over the image. The technique resolved several test patterns independent of size and angle of rotation. Several samples obtained from different substrates were analyzed with the method. The results were consistent with those values obtained from conventional methods. Although still limited as a laboratory application, shape analysis has the potential to provide insight into the mechanisms of crystal growing and may provide a basis for specifications or guidelines for the manufacturing of biomaterial for bone tissue engineering. Our proposed automated computational method for the analysis and quantification digital images of bone tissue at microscale provide a rapid and accurate of the mechanical properties of the tissue.Fil: Tahoces, Pablo G.. Universidad de Santiago de Compostela; EspañaFil: Messina, Paula Verónica. Universidad Nacional del Sur; ArgentinaFil: Ruso, Juan Manuel. Universidad de Santiago de Compostela; Españ

Deep learning method for aortic root detection

Background: Computed tomography angiography (CTA) is a preferred imaging technique for a wide range of vascular diseases. However, extensive manual analysis is required to detect and identify several anatomical landmarks for clinical application. This study demonstrates the feasibility of a fully automatic method for detecting the aortic root, which is a key anatomical landmark in this type of procedure. The approach is based on the use of deep learning techniques that attempt to mimic expert behavior. Methods: A total of 69 CTA scans (39 for training and 30 for validation) with different pathology types were selected to train the network. Furthermore, a total of 71 CTA scans were selected independently and applied as the test set to assess their performance. Results: The accuracy was evaluated by comparing the locations marked by the method with benchmark locations (which were manually marked by two experts). The interobserver error was 4.6 ± 2.3 mm. On an average, the differences between the locations marked by the two experts and those detected by the computer were 6.6 ± 3.0 mm and 6.8 ± 3.3 mm, respectively, when calculated using the test set. Conclusions: From an analysis of these results, we can conclude that the proposed method based on pre-trained CNN models can accurately detect the aortic root in CTA images without prior segmentationThis work was partially financed by Consellería de Cultura, Educación e Universidade (reference 2019–2021, ED431C 2018/19)S

Bootstrap-based procedures for inference in nonparametric ROC regression analysis

Before the use of a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is an essential step. The receiver operating characteristic (ROC) curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy needs to be also assessed. In this paper, attention is focused on an estimator for the covariate-specific ROC curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalized additive models for the ROC curve (ROC-GAM). The main aim of the paper is to offer new inferential procedures for testing the effect of co- variates over the conditional ROC curve within the ROC-GAM context. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the ROC curve; and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computed-aided diagnostic (CAD) system for the automatic detection of tumour masses in breast cancer is analysed

Aplicación web de gestión de alquiler de aulas, cursos de formación y consultoría

El objetivo principal es desarrollar la página web para dicha empresa, necesaria para dar a conocer el producto que ellos ofrecen y para que los alumnos de los cursos que se imparten puedan llevar un seguimiento mayor y tener la información más cercana de éstos, y puedan participar con opiniones en el blog o por medio de las redes sociales, y en un segundo plano, realizar otro tipo de tareas correspondientes para la empresa. En este documento se describirá la experiencia en la práctica de empresa, centrándose en el desarrollo de la página web mediante tecnologías CMS, en este caso Joomla, y las respectivas extensiones que se ha utilizado de ésta CMS. En un primer lugar se describirá el entorno de desarrollo como pudiera ser las tecnologías web de hoy en día, focalizando a las tecnologías o lenguajes que utiliza Joomla. Seguidamente se pasará a describir la especificación de requisitos que se compondrá de diferentes puntos y explicará entre otras cosas las funcionalidades que posee o los requerimientos funcionales y no funcionales que tendrá la aplicación web. A partir de ahí, nos centraremos en el diseño de la web (la explicación de las diferentes capas que posee esta aplicación) y la implementación de ésta. Por último habrá un apartado de conclusiones y otro de referencias.Tahoces Marroco, D. (2013). Aplicación web de gestión de alquiler de aulas, cursos de formación y consultoría. http://hdl.handle.net/10251/18314.Archivo delegad

Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis

Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analyse

La nueva oportunidad de la hipoteca inversa

The reverse mortgage is a credit or loan guaranteed by a mortgage that falls on the applicant?s habitual residence, granted at once, or through periodic benefits, to a person who must be over a certain age or prove a degree of disability or dependency, and is not due until the time of death. In view of the doubts that the future of the public pension is raising and the concentration of savings in Spanish property, the reverse mortgage is once again being put on the table as a viable alternative to complement a public pension with little revaluation, in view of the increase in life expectancy and expenses after retirement

Barrier height prediction by machine learning correction of semiempirical calculations

Different machine learning (ML) models are proposed in the present work to predict DFT-quality barrier heights (BHs) from semiempirical quantum-mechanical (SQM) calculations. The ML models include multi-task deep neural network, gradient boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors (MAEs) are similar or slightly better than previous models considering the same number of data points. Unlike other ML models employed to predict BHs, entropic effects are included, which enables the prediction of rate constants at different temperatures. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in Combustion Chemistry or in Astrochemistry. Finally, our results show that 70% of the bespoke predictors are amongst the features with the highest impact on model output. This custom-made set of predictors could be employed by future delta-ML models to improve the quantitative prediction of other reaction properties