Search

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein–ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol−1, equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol−1. For the first time, a setup is presented for overall high precision and high accuracy relative protein–ligand alchemical free energy calculations based on open-source software., Relative ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design.

[EN] Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this article, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis., This work was supported by Generalitat Valenciana through the "Instituto Valenciano de Competitividad Empresarial-IVACE'' under Grant IMDEEA/2020/69.

[EN] The data quality assessment has many dimensions, from those so obvious as the data completeness and consistency to other less evident such as the correctness or the ability to represent the target population. In general, it is possible to classify them as those produced by an external effect, and those that are inherent in the data itself. This work will be focused on those inherent to data, such as the temporal and the multisource variability applied to healthcare data repositories. Every process is usually improved over time, and that has a direct impact on the data distribution. Similarly, how a process is executed in different sources may vary due to many factors, such as the diverse interpretation of standard protocols by human beings or different previous experiences of experts. Artificial Intelligence has become one of the most widely extended technological paradigms in almost all the scientific and industrial fields. Advances not only in models but also in hardware have led to their use in almost all areas of science. Although the solved problems using this technology often have the drawback of not being interpretable, or at least not as much as other classical mathematical or statistical techniques. This motivated the emergence of the "explainable artificial intelligence" concept, that study methods to quantify and visualize the training process of models based on machine learning. On the other hand, real systems may often be represented by large networks (graphs), and one of the most relevant features in such networks is the community or clustering structure. Since sociology, biology, or clinical situations could usually be modeled using graphs, community detection algorithms are becoming more and more extended in a biomedical field. In the present doctoral thesis, contributions have been made in the three above mentioned areas. On the one hand, temporal and multisource variability assessment methods based on information geometry were used to detect variability in data distribution that may hinder data reuse and, hence, the conclusions which can be extracted from them. This methodology's usability was proved by a temporal variability analysis to detect data anomalies in the electronic health records of a hospital over 7 years. Besides, it showed that this methodology could have a positive impact if it applied previously to any study. To this end, firstly, we extracted the variables that highest influenced the intensity of headache in migraine patients using machine learning techniques. One of the principal characteristics of machine learning algorithms is its capability of fitting the training set. In those datasets with a small number of observations, the model can be biased by the training sample. The observed variability, after the application of the mentioned methodology and considering as sources the registries of migraine patients with different headache intensity, served as evidence for the truthfulness of the extracted features. Secondly, such an approach was applied to measure the variability among the gray-level histograms of digital mammographies. We demonstrated that the acquisition device produced the observed variability, and after defining an image preprocessing step, the performance of a deep learning model, which modeled a marker of breast cancer risk estimation, increased. Given a dataset containing the answers to a survey formed by psychometric scales, or in other words, questionnaires to measure psychologic factors, such as depression, cope, etcetera, two deep learning architectures that used the data structure were defined. Firstly, we designed a deep learning architecture using the conceptual structure of such psychometric scales. This architecture was trained to model the happiness degree of the participants, improved the performance compared to classical statistical approaches. A second architecture, automatically designed using community detection in graphs, was not only a contribution, [ES] El análisis de la calidad de los datos abarca muchas dimensiones, desde aquellas tan obvias como la completitud y la coherencia, hasta otras menos evidentes como la correctitud o la capacidad de representar a la población objetivo. En general, es posible clasificar estas dimensiones como las producidas por un efecto externo y las que son inherentes a los propios datos. Este trabajo se centrará en la evaluación de aquellas inherentes a los datos en repositorios de datos sanitarios, como son la variabilidad temporal y multi-fuente. Los procesos suelen evolucionar con el tiempo, y esto tiene un impacto directo en la distribución de los datos. Análogamente, la subjetividad humana puede influir en la forma en la que un mismo proceso, se ejecuta en diferentes fuentes de datos, influyendo en su cuantificación o recogida. La inteligencia artificial se ha convertido en uno de los paradigmas tecnológicos más extendidos en casi todos los campos científicos e industriales. Los avances, no sólo en los modelos sino también en el hardware, han llevado a su uso en casi todas las áreas de la ciencia. Es cierto que, los problemas resueltos mediante esta tecnología, suelen tener el inconveniente de no ser interpretables, o al menos, no tanto como otras técnicas de matemáticas o de estadística clásica. Esta falta de interpretabilidad, motivó la aparición del concepto de "inteligencia artificial explicable", que estudia métodos para cuantificar y visualizar el proceso de entrenamiento de modelos basados en aprendizaje automático. Por otra parte, los sistemas reales pueden representarse a menudo mediante grandes redes (grafos), y una de las características más relevantes de esas redes, es la estructura de comunidades. Dado que la sociología, la biología o las situaciones clínicas, usualmente pueden modelarse mediante grafos, los algoritmos de detección de comunidades se están extendiendo cada vez más en el ámbito biomédico. En la presente tesis doctoral, se han hecho contribuciones en los tres campos anteriormente mencionados. Por una parte, se han utilizado métodos de evaluación de variabilidad temporal y multi-fuente, basados en geometría de la información, para detectar la variabilidad en la distribución de los datos que pueda dificultar la reutilización de los mismos y, por tanto, las conclusiones que se puedan extraer. Esta metodología demostró ser útil tras ser aplicada a los registros electrónicos sanitarios de un hospital a lo largo de 7 años, donde se detectaron varias anomalías. Además, se demostró el impacto positivo que este análisis podría añadir a cualquier estudio. Para ello, en primer lugar, se utilizaron técnicas de aprendizaje automático para extraer las características más relevantes, a la hora de clasificar la intensidad del dolor de cabeza en pacientes con migraña. Una de las propiedades de los algoritmos de aprendizaje automático es su capacidad de adaptación a los datos de entrenamiento, en bases de datos en los que el número de observaciones es pequeño, el estimador puede estar sesgado por la muestra de entrenamiento. La variabilidad observada, tras la utilización de la metodología y considerando como fuentes, los registros de los pacientes con diferente intensidad del dolor, sirvió como evidencia de la veracidad de las características extraídas. En segundo lugar, se aplicó para medir la variabilidad entre los histogramas de los niveles de gris de mamografías digitales. Se demostró que esta variabilidad estaba producida por el dispositivo de adquisición, y tras la definición de un preproceso de imagen, se mejoró el rendimiento de un modelo de aprendizaje profundo, capaz de estimar un marcador de imagen del riesgo de desarrollar cáncer de mama. Dada una base de datos que recogía las respuestas de una encuesta formada por escalas psicométricas, o lo que es lo mismo cuestionarios que sirven para medir un factor psicológico, tales como depresión, resiliencia, etc., se definieron nuevas arquitecturas de aprendizaje profundo utilizando la estructura de los datos. En primer lugar, se dise˜no una arquitectura, utilizando la estructura conceptual de las citadas escalas psicom´etricas. Dicha arquitectura, que trataba de modelar el grado de felicidad de los participantes, tras ser entrenada, mejor o la precisión en comparación con otros modelos basados en estadística clásica. Una segunda aproximación, en la que la arquitectura se diseño de manera automática empleando detección de comunidades en grafos, no solo fue una contribución de por sí por la automatización del proceso, sino que, además, obtuvo resultados comparables a su predecesora., [CA] L'anàlisi de la qualitat de les dades comprén moltes dimensions, des d'aquelles tan òbvies com la completesa i la coherència, fins a altres menys evidents com la correctitud o la capacitat de representar a la població objectiu. En general, és possible classificar estes dimensions com les produïdes per un efecte extern i les que són inherents a les pròpies dades. Este treball se centrarà en l'avaluació d'aquelles inherents a les dades en reposadors de dades sanitaris, com són la variabilitat temporal i multi-font. Els processos solen evolucionar amb el temps i açò té un impacte directe en la distribució de les dades. Anàlogament, la subjectivitat humana pot influir en la forma en què un mateix procés, s'executa en diferents fonts de dades, influint en la seua quantificació o arreplega. La intel·ligència artificial s'ha convertit en un dels paradigmes tecnològics més estesos en quasi tots els camps científics i industrials. Els avanços, no sols en els models sinó també en el maquinari, han portat al seu ús en quasi totes les àrees de la ciència. És cert que els problemes resolts per mitjà d'esta tecnologia, solen tindre l'inconvenient de no ser interpretables, o almenys, no tant com altres tècniques de matemàtiques o d'estadística clàssica. Esta falta d'interpretabilitat, va motivar l'aparició del concepte de "inteligencia artificial explicable", que estudia mètodes per a quantificar i visualitzar el procés d'entrenament de models basats en aprenentatge automàtic. D'altra banda, els sistemes reals poden representar-se sovint per mitjà de grans xarxes (grafs) i una de les característiques més rellevants d'eixes xarxes, és l'estructura de comunitats. Atés que la sociologia, la biologia o les situacions clíniques, poden modelar-se usualment per mitjà de grafs, els algoritmes de detecció de comunitats s'estan estenent cada vegada més en l'àmbit biomèdic. En la present tesi doctoral, s'han fet contribucions en els tres camps anteriorment mencionats. D'una banda, s'han utilitzat mètodes d'avaluació de variabilitat temporal i multi-font, basats en geometria de la informació, per a detectar la variabilitat en la distribució de les dades que puga dificultar la reutilització dels mateixos i, per tant, les conclusions que es puguen extraure. Esta metodologia va demostrar ser útil després de ser aplicada als registres electrònics sanitaris d'un hospital al llarg de 7 anys, on es van detectar diverses anomalies. A més, es va demostrar l'impacte positiu que esta anàlisi podria afegir a qualsevol estudi. Per a això, en primer lloc, es van utilitzar tècniques d'aprenentatge automàtic per a extraure les característiques més rellevants, a l'hora de classificar la intensitat del mal de cap en pacients amb migranya. Una de les propietats dels algoritmes d'aprenentatge automàtic és la seua capacitat d'adaptació a les dades d'entrenament, en bases de dades en què el nombre d'observacions és xicotet, l'estimador pot estar esbiaixat per la mostra d'entrenament. La variabilitat observada després de la utilització de la metodologia, i considerant com a fonts els registres dels pacients amb diferent intensitat del dolor, va servir com a evidència de la veracitat de les característiques extretes. En segon lloc, es va aplicar per a mesurar la variabilitat entre els histogrames dels nivells de gris de mamografies digitals. Es va demostrar que esta variabilitat estava produïda pel dispositiu d'adquisició i després de la definició d'un preprocés d'imatge, es va millorar el rendiment d'un model d'aprenentatge profund, capaç d'estimar un marcador d'imatge del risc de desenrotllar càncer de mama. Donada una base de dades que arreplegava les respostes d'una enquesta formada per escales psicomètriques, o el que és el mateix qüestionaris que servixen per a mesurar un factor psicològic, com ara depressió, resiliència, etc., es van definir noves arquitectures d'aprenentatge profund utilitzant l’estructura de les dades. En primer lloc, es disseny`a una arquitectura, utilitzant l’estructura conceptual de les esmentades escales psicom`etriques. La dita arquitectura, que tractava de modelar el grau de felicitat dels participants, despr´es de ser entrenada, va millorar la precisió en comparació amb altres models basats en estad´ıstica cl`assica. Una segona aproximació, en la que l’arquitectura es va dissenyar de manera autoàtica emprant detecció de comunitats en grafs, no sols va ser una contribució de per si per l’automatització del procés, sinó que, a més, va obtindre resultats comparables a la seua predecessora., También me gustaría mencionar al Instituto Tecnológico de la Informáica, en especial al grupo de investigación Percepción, Reconocimiento, Aprendizaje e Inteligencia Artificial, no solo por darme la oportunidad de seguir creciendo en el mundo de la ciencia, sino también, por apoyarme en la consecución de mis objetivos personales

[EN] Deep neural networks (DNNs) have emerged as a state-of-the-art tool in very different research fields due to its adaptive power to the decision space since they do not presuppose any linear relationship between data. Some of the main disadvantages of these trending models are that the choice of the network underlying architecture profoundly influences the performance of the model and that the architecture design requires prior knowledge of the field of study. The use of questionnaires is hugely extended in social/behavioral sciences. The main contribution of this work is to automate the process of a DNN architecture design by using an agglomerative hierarchical algorithm that mimics the conceptual structure of such surveys. Although the train had regression purposes, it is easily convertible to deal with classification tasks. Our proposed methodology will be tested with a database containing socio-demographic data and the responses to five psychometric Likert scales related to the prediction of happiness. These scales have been already used to design a DNN architecture based on the subdimension of the scales. We show that our new network configurations outperform the previous existing DNN architectures., The authors thank the support of the project Analysis, quality, and variability of medical data funded by Universitat Politècnica de València. JMGG and JAC acknowledge the support of the H2020 project CrowdHealth (Collective Wisdom Driving Public Health Policies - 727560) funded by the European Comission. JMGG acknowledge and to the In Advance project (Patient-Centred Pathways of Early Palliative Care, Supportive Ecosystems and Appraisal Standard - 825750) funded by the European Comission, too.

We measure sub-7-fs laser pulses using commercially available BaTiO3 second-harmonic nanoparticles and the dispersion-scan technique. Scattering plays a key role in the retrieval algorithm to obtain the same results as in Beta-Barium-Borate crystals.

[EN] Background and Objective: Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer. It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard. This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. Methods: A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. Results: The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. Conclusions: An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work., This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2019/1, and by Carlos III Institute of Health under the project DTS15/00080.

[EN] Background Migraine is a heterogeneous condition with multiple clinical manifestations. Machine learning algorithms permit the identification of population groups, providing analytical advantages over other modeling techniques. Objective The aim of this study was to analyze critical features that permit the differentiation of subgroups of patients with migraine according to the intensity and frequency of attacks by using machine learning algorithms. Methods Sixty-seven women with migraine participated. Clinical features of migraine, related disability (Migraine Disability Assessment Scale), anxiety/depressive levels (Hospital Anxiety and Depression Scale), anxiety state/trait levels (State-Trait Anxiety Inventory), and pressure pain thresholds (PPTs) over the temporalis, neck, second metacarpal, and tibialis anterior were collected. Physical examination included the flexion-rotation test, cervical range of cervical motion, forward head position while sitting and standing, passive accessory intervertebral movements (PAIVMs) with headache reproduction, and joint positioning sense error. Subgrouping was based on machine learning algorithms by using the nearest neighbors algorithm, multisource variability assessment, and random forest model. Results For migraine intensity, group 2 (women with a regular migraine headache intensity score of 7 on an 11-point Numeric Pain Rating Scale [where 0 = no pain and 10 = maximum pain]) were younger and had lower joint positioning sense error in cervical rotation, greater cervical mobility in rotation and flexion, lower flexion-rotation test scores, positive PAIVMs reproducing migraine, normal PPTs over the tibialis anterior, shorter migraine history, and lower cranio-vertebral angles while standing than the remaining migraine intensity subgroups. The most discriminative variable was the flexion-rotation test score of the symptomatic side. For migraine frequency, no model was able to identify differences between groups (ie, patients with episodic or chronic migraine). Conclusions A subgroup of women with migraine who had common migraine intensity was identified with machine learning algorithms.

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

[EN] Objective To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years. Materials and methods Temporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index. Results Four Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay. Discussion TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities¿ relocation and increment of citizens (findings 1, 3¿4), the impact of strategies (findings 2¿3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse. Conclusions The effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data., F.J.P.B, C.S., J.M.G.G. and J.A.C. were funded Universitat Politecnica de Valencia, project "ANALISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MEDICOS". www.upv.es. J.M.G.G.is also partially supported by: Ministerio de Economia y Competitividad of Spain through MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R); and European Commission projects H2020-SC1-2016-CNECT Project (No. 727560) and H2020-SC1-BHC-2018-2020 (No. 825750). The funders did not play any role in the study design, data collection and analysis, decision to publish, nor preparation of the manuscript.

[ES] La visión artificial es uno de los campos en mayor crecimiento en la actualidad que, junto con otras tecnologías como la Biometría o el Big Data, se ha convertido en el foco de interés de numerosas investigaciones y es considerada como una de las tecnologías del futuro. Este amplio campo abarca diversos métodos entre los que se encuentra el procesamiento y análisis de imágenes digitales. El éxito del análisis de imágenes y otras tareas de procesamiento de alto nivel, como pueden ser el reconocimiento de patrones o la visión 3D, dependerá en gran medida de la buena calidad de las imágenes de partida. Hoy en día existen multitud de factores que dañan las imágenes dificultando la obtención de imágenes de calidad óptima, esto ha convertido el (pre-) procesamiento digital de imágenes en un paso fundamental previo a la aplicación de cualquier otra tarea de procesado. Los factores más comunes son el ruido y las malas condiciones de adquisición: los artefactos provocados por el ruido dificultan la interpretación adecuada de la imagen y la adquisición en condiciones de iluminación o exposición deficientes, como escenas dinámicas, causan pérdida de información de la imagen que puede ser clave para ciertas tareas de procesamiento. Los pasos de (pre-)procesamiento de imágenes conocidos como suavizado y realce se aplican comúnmente para solventar estos problemas: El suavizado tiene por objeto reducir el ruido mientras que el realce se centra en mejorar o recuperar la información imprecisa o dañada. Con estos métodos conseguimos reparar información de los detalles y bordes de la imagen con una nitidez insuficiente o un contenido borroso que impide el (post-)procesamiento óptimo de la imagen. Existen numerosos métodos que suavizan el ruido de una imagen, sin embargo, en muchos casos el proceso de filtrado provoca emborronamiento en los bordes y detalles de la imagen. De igual manera podemos encontrar una enorme cantidad de técnicas de realce que intentan combatir las pérdidas de información, sin embargo, estas técnicas no contemplan la existencia de ruido en la imagen que procesan: ante una imagen ruidosa, cualquier técnica de realce provocará también un aumento del ruido. Aunque la idea intuitiva para solucionar este último caso será el previo filtrado y posterior realce, este enfoque ha demostrado no ser óptimo: el filtrado podrá eliminar información que, a su vez, podría no ser recuperable en el siguiente paso de realce. En la presente tesis doctoral se propone un modelo basado en teoría de grafos para el procesamiento de imágenes en color. En este modelo, se construye un grafo para cada píxel de tal manera que sus propiedades permiten caracterizar y clasificar dicho pixel. Como veremos, el modelo propuesto es robusto y capaz de adaptarse a una gran variedad de aplicaciones. En particular, aplicamos el modelo para crear nuevas soluciones a los dos problemas fundamentales del procesamiento de imágenes: suavizado y realce. Se ha estudiado el modelo en profundidad en función del umbral, parámetro clave que asegura la correcta clasificación de los píxeles de la imagen. Además, también se han estudiado las posibles características y posibilidades del modelo que nos han permitido sacarle el máximo partido en cada una de las posibles aplicaciones. Basado en este modelo se ha diseñado un filtro adaptativo capaz de eliminar ruido gaussiano de una imagen sin difuminar los bordes ni perder información de los detalles. Además, también ha permitido desarrollar un método capaz de realzar los bordes y detalles de una imagen al mismo tiempo que se suaviza el ruido presente en la misma. Esta aplicación simultánea consigue combinar dos operaciones opuestas por definición y superar así los inconvenientes presentados por el enfoque en dos etapas., [CA] La visió artificial és un dels camps en major creixement en l'actualitat que, junt amb altres tecnlogies com la Biometria o el Big Data, s'ha convertit en el focus d'interés de nombroses investigacions i és considerada com una de les tecnologies del futur. Aquest ampli camp comprén diversos m`etodes entre els quals es troba el processament digital d'imatges i anàlisis d'imatges digitals. L'èxit de l'anàlisis d'imatges i altres tasques de processament d'alt nivell, com poden ser el reconeixement de patrons o la visió 3D, dependrà en gran manera de la bona qualitat de les imatges de partida. Avui dia existeixen multitud de factors que danyen les imatges dificultant l'obtenció d'imatges de qualitat òptima, açò ha convertit el (pre-) processament digital d'imatges en un pas fonamental previa la l'aplicació de qualsevol altra tasca de processament. Els factors més comuns són el soroll i les males condicions d'adquisició: els artefactes provocats pel soroll dificulten la inter- pretació adequada de la imatge i l'adquisició en condicions d'il·luminació o exposició deficients, com a escenes dinàmiques, causen pèrdua d'informació de la imatge que pot ser clau per a certes tasques de processament. Els passos de (pre-) processament d'imatges coneguts com suavitzat i realç s'apliquen comunament per a resoldre aquests problemes: El suavitzat té com a objecte reduir el soroll mentres que el real se centra a millorar o recuperar la informació imprecisa o danyada. Amb aquests mètodes aconseguim reparar informació dels detalls i bords de la imatge amb una nitidesa insuficient o un contingut borrós que impedeix el (post-)processament òptim de la imatge. Existeixen nombrosos mètodes que suavitzen el soroll d'una imatge, no obstant això, en molts casos el procés de filtrat provoca emborronamiento en els bords i detalls de la imatge. De la mateixa manera podem trobar una enorme quantitat de tècniques de realç que intenten combatre les pèrdues d'informació, no obstant això, aquestes tècniques no contemplen l'existència de soroll en la imatge que processen: davant d'una image sorollosa, qualsevol tècnica de realç provocarà també un augment del soroll. Encara que la idea intuïtiva per a solucionar aquest últim cas seria el previ filtrat i posterior realç, aquest enfocament ha demostrat no ser òptim: el filtrat podria eliminar informació que, al seu torn, podria no ser recuperable en el seguënt pas de realç. En la present Tesi doctoral es proposa un model basat en teoria de grafs per al processament d'imatges en color. En aquest model, es construïx un graf per a cada píxel de tal manera que les seues propietats permeten caracteritzar i classificar el píxel en quëstió. Com veurem, el model proposat és robust i capaç d'adaptar-se a una gran varietat d'aplicacions. En particular, apliquem el model per a crear noves solucions als dos problemes fonamentals del processament d'imatges: suavitzat i realç. S'ha estudiat el model en profunditat en funció del llindar, paràmetre clau que assegura la correcta classificació dels píxels de la imatge. A més, també s'han estudiat les possibles característiques i possibilitats del model que ens han permés traure-li el màxim partit en cadascuna de les possibles aplicacions. Basat en aquest model s'ha dissenyat un filtre adaptatiu capaç d'eliminar soroll gaussià d'una imatge sense difuminar els bords ni perdre informació dels detalls. A més, també ha permés desenvolupar un mètode capaç de realçar els bords i detalls d'una imatge al mateix temps que se suavitza el soroll present en la mateixa. Aquesta aplicació simultània aconseguix combinar dues operacions oposades per definició i superar així els inconvenients presentats per l'enfocament en dues etapes., [EN] Computer vision is one of the fastest growing fields at present which, along with other technologies such as Biometrics or Big Data, has become the focus of interest of many research projects and it is considered one of the technologies of the future. This broad field includes a plethora of digital image processing and analysis tasks. To guarantee the success of image analysis and other high-level processing tasks as 3D imaging or pattern recognition, it is critical to improve the quality of the raw images acquired. Nowadays all images are affected by different factors that hinder the achievement of optimal image quality, making digital image processing a fundamental step prior to the application of any other practical application. The most common of these factors are noise and poor acquisition conditions: noise artefacts hamper proper image interpretation of the image; and acquisition in poor lighting or exposure conditions, such as dynamic scenes, causes loss of image information that can be key for certain processing tasks. Image (pre-) processing steps known as smoothing and sharpening are commonly applied to overcome these inconveniences: Smoothing is aimed at reducing noise and sharpening at improving or recovering imprecise or damaged information of image details and edges with insufficient sharpness or blurred content that prevents optimal image (post-)processing. There are many methods for smoothing the noise in an image, however in many cases the filtering process causes blurring at the edges and details of the image. Besides, there are also many sharpening techniques, which try to combat the loss of information due to blurring of image texture and need to contemplate the existence of noise in the image they process. When dealing with a noisy image, any sharpening technique may amplify the noise. Although the intuitive idea to solve this last case would be the previous filtering and later sharpening, this approach has proved not to be optimal: the filtering could remove information that, in turn, may not be recoverable in the later sharpening step. In the present PhD dissertation we propose a model based on graph theory for color image processing from a vector approach. In this model, a graph is built for each pixel in such a way that its features allow to characterize and classify the pixel. As we will show, the model we proposed is robust and versatile: potentially able to adapt to a variety of applications. In particular, we apply the model to create new solutions for the two fundamentals problems in image processing: smoothing and sharpening. To approach high performance image smoothing we use the proposed model to determine if a pixel belongs to a at region or not, taking into account the need to achieve a high-precision classification even in the presence of noise. Thus, we build an adaptive soft-switching filter by employing the pixel classification to combine the outputs from a filter with high smoothing capability and a softer one to smooth edge/detail regions. Further, another application of our model allows to use pixels characterization to successfully perform a simultaneous smoothing and sharpening of color images. In this way, we address one of the classical challenges within the image processing field. We compare all the image processing techniques proposed with other state-of-the-art methods to show that they are competitive both from an objective (numerical) and visual evaluation point of view.

[EN] Background The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage. Methods A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients. Results The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208. Conclusion Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study., This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2018/1, and by Carlos III Institute of Health under the project DTS15/00080

Study Design Multicenter, parallel randomized clinical trial. Background Myofascial trigger points (MTrPs) are implicated in shoulder pain and functional limitations. An intervention intended to treat MTrPs is dry needling. Objectives To investigate the effectiveness of dry needling in addition to evidence-based personalized physical therapy treatment in the treatment of shoulder pain. Methods One hundred twenty patients with nonspecific shoulder pain were randomly allocated into 2 parallel groups: (1) personalized, evidencebased physical therapy treatment; and (2) trigger point dry needling in addition to personalized, evidence-based physical therapy treatment. Patients were assessed at baseline, posttreatment, and 3-month follow-up. The primary outcome measure was pain assessed by a visual analog scale at 3 months, and secondary variables were joint range-of-motion limitations, Constant-Murley score for pain and function, and number of active MTrPs. Clinical efficacy was assessed using intention-to-treat analysis. Results Of the 120 enrolled patients, 63 were randomly assigned to the control group and 57 to the intervention group. There were no significant differences in outcome between the 2 treatment groups. Both groups showed improvement over time. Conclusion Dry needling did not offer benefits in addition to personalized, evidencebased physical therapy treatment for patients with nonspecific shoulder pain. Level of Evidence Therapy, level 1b. Registered February 11, 2009 at www.isrctn.com (ISRCTN30907460). J Orthop Sports Phys Ther 2017;47(1):11-20. Epub 9 Dec 2016. doi:10.2519/jospt.2017.6698.

Background and Objective: Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventional methodologies are based on linear relationships, such as the commonly used Multivariate Linear Regression (MLR), which may suffer from the lack of representative capacity to the varied psychological features. Using Deep Neural Networks (DNN), we define a Happiness Degree Predictor (H-DP) based on the answers to five psychometric standardized questionnaires. Methods: A Data-Structure driven architecture for DNNs (D-SDNN) is proposed for defining a HDP in which the network architecture enables the conceptual interpretation of psychological factors associated to happiness. Four different neural network configurations have been tested, varying the number of neurons and the presence or absence of bias in the hidden layers. Two metrics for evaluating the influence of conceptual dimensions have been defined and computed: one quantifies the influence weight of the conceptual dimension in absolute terms and the other one pinpoints the direction (positive or negative) of the influence. Materials: A cross-sectional survey targeting non-institutionalized adult population residing in Spain was completed by 823 cases. The total of 111 elements of the survey are grouped by socio-demographic data and by five psychometric scales (Brief COPE Inventory, EPQR-A, GHQ-28, MOS-SSS and SDHS) measuring several psychological factors acting one as the outcome (SDHS) and the four others as predictors. Results: Our D-SDNN approach provided a better outcome (MSE: 1.46 · 10 − 2 ) than MLR (MSE: 2.30 · 10 − 2 ), hence improving by 37% the predictive accuracy, and allowing to simulate the conceptual structure. Conclusions: We observe a better performance of Deep Neural Networks (DNN) with respect to traditional methodologies. This demonstrates its capability to capture the conceptual structure for predicting happiness degree through psychological variables assessed by standardized questionnaires. It also permits to estimate the influence of each factor on the outcome without assuming a linear relationship.

[EN] In this work we introduce an image characterization of pixels based on local graphs that allows to distinguish different local regions around a pixel. This separation also permits us to develop a method for determining the role of each pixel in a neighborhood of any other, either for smoothing or for sharpening. Two methods for simultaneously conducting both processes are provided. Our solution overcomes the drawbacks of the classic two steps sequential smoothing and sharpening process: enhancing details while reducing noise and not losing critical information. The parameters of the methods are adjusted in two different ways: through observers visual quality optimization and with an objective optimization criterion. The results show that our methods outperform other recent state-of-the-art ones., We thank F. Russo for providing the implementation of the Fuzzy method and V. Ratmer, and Y.Y. Zeevi for providing the implementation of the FAB method. Cristina Jordan acknowledges the support of grant TEC2016-79884-C2-2-R. Samuel Morillas acknowledges the support of grant MTM2015-64373-P (MINECO/FEDER, Spain, UE).

We demonstrate the measurement and retrieval of sub-7-fs laser pulses from a broadband Ti:Sa oscillator using ensembles of commercially available second-harmonic nanoparticles (NPs) and dispersion scan (d-scan). Scattering adds an incoherent component to the d-scan trace, and our retrieval procedure allows for correctly weighting the coherent and incoherent contributions to the trace. Comparison with a thin crystal [beta barium borate (BBO)] gave consistent pulse durations (6.56±0.03 fs for BBO and 6.5±0.1 fs for the NPs). These results pave the way to using ensembles of NPs for the measurement of ultrashort light pulses with ultra-broadband spectra at room temperature with standard spectrometers, in any spectral range.

[EN] A model based on local graphs to classify pixels coming from at or detail regions of an image is presented. For each pixel a local graph is dened. Its structure will depend on the similarity between neighbouring pixels. Its features allow us to classify each image pixel as belonging to one type of region or the other. This classication is an essential pre-processing technique for many Computer Vision tools, such as smoothingor sharpening of digital color images., [ES] Presentamos un modelo basado en grafos locales para clasificar los píxeles atendiendo a si pertenecen a zonas homogéneas o a zonas de detalle de una imagen. Para cada píxel se define un grafo cuya estructura dependerá de la similitud entre los píxeles adyacentes. Sus características permiten clasificar cada píxel de la imagen como perteneciente a un tipo de zona u a otra. Esta clasificación constituye un pre-procesado de la imagen primordial para múltiples ramas de Visión Artificial, como puede ser el suavizado o el realce de imágenes digitales., Samuel Morillas: MTM2015-64373-P (MINECO/FEDER);Cristina Jordán: MECD TEC2016-79884-C2-2-R y Samuel Morillas, Cristina Jordán y J. Alberto Conejero: Proyecto de Innovación Docente del Depto. de Matemática Aplicada de la Universitat Politècnica de València, PID-DMA2016: Prácticas de Teoría de Grafos aplicadas al procesamiento de imágenes.

BackgroundRotator cuff tendinopathy and subacromial impingement syndrome present complex patomechanical situations, frequent difficulties in clinical diagnosis and lack of effectiveness in treatment. Based on clinical experience, we have therefore considered the existence of another pathological entity as the possible origin of pain and dysfunction. The hypothesis of this study is to relate subacromial impingement syndrome (SIS) with myofascial pain syndrome (MPS), since myofascial trigger points (MTrPs) cause pain, functional limitation, lack of coordination and alterations in quality of movement, even prior to a tendinopathy. MTrPs can coexist with any degenerative subacromial condition. If they are not taken into consideration, they could perpetuate and aggravate the problem, hindering diagnosis and making the applied treatments ineffective.The aims and methods of this study are related with providing evidence of the relationship that may exist between this condition and MPS in the diagnosis and treatment of rotator cuff tendonitis and/or SIS.Method/designA descriptive transversal study will be made to find the correlation between the diagnosis of SIS and rotator cuff tendonitis, positive provocation test responses, the existence of active MTrPs and the results obtained with ultrasonography (US) and Magnetic Renonance Imaging (MRI). A randomized double blinded clinical trial will be carried out in experimental conditions: A Protocolized treatment based on active and passive joint repositioning, stabilization exercises, stretching of the periarticular shoulder muscles and postural reeducation. B. The previously described protocolized treatment, with the addition of dry needling applied to active MTrPs with the purpose of isolating the efficacy of dry needling in treatment.DiscussionThis study aims to provide a new vision of shoulder pain, from the perspective of MPS. This syndrome can, by itself, account for shoulder pain and dysfunction, although it can coexist with real conditions involving the tendons.Trail RegistrationISRCTN Number: 30907460

systole, and in over 80% of cases these intramural vessels exhibit anatomic abnormalities with medial/intimal proliferation and a narrowed lumen.4 Thus a patient with IHSS may suffer from various factors that further reduce oxygen supply in the face of markedly heightened demand. Whether the LAD in such patients is more prone to spasm is unknown. Certainly, abrupt closure of the diseased vessel is not an uncommon occurrence (4”;# to 657, incidence) after balloon dilatation5 or directional atherectomy.” If this should occur in the LAD in a patient with IHSS, profound hemodynamic decompensation, as in our patient, may result and preclude the employment of the usual pharmacologic agents used to counteract spasm or lessen ischemia (for example, nitrates or calcium-entry blockers), because of their potential hypotensive effects. Rapid reestablishment of coronary flow mechanically, as in our case, may not be sufficient to reverse the spiraling hemodynamic consequences. In summary, with the proportionately huge amount of myocardium supplied, the LAD in a patient with IHSS may be considered a “left main equivalent.” Catheter interventional t.echniques in this vessel should be undertaken with great care, if at all.

The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking congeneric series based on deep 3D-convolutional neural networks. Furthermore we prove its effectiveness by blindly testing it on datasets provided by Janssen, Pfizer and Biogen totalling over 3246 ligands and 13 targets as well as several well-known openly available sets, representing one the largest evaluations ever performed. We also performed online learning simulations of lead optimization using the approach in a predictive manner obtaining significant advantage over experimental choice. We believe that the evaluation performed in this study is strong evidence of the usefulness of a modern deep learning model in lead optimization pipelines against more expensive simulation-based alternatives. The authors thank Acellera Ltd. for funding. G. D. F. acknowledges support from MINECO (BIO2014-53095-P), MICINN (PTQ-17-09079) and FEDER. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 675451 (CompBioMed project).