Quantifying inter-hemispheric differences in Parkinson’s Disease using siamese networks

Classification of medical imaging is one of the most popular application of intelligent systems. A crucial step is to find the features that are relevant for the subsequent classification. One possibility is to compute features derived from the morphology of the target region in order to check its role in the pathology under study. It is also possible to extract relevant features to evaluate the similarity between different regions, in addition to compute morphology-related measures. However, it can be much more useful to model the differences between regions. In this paper, we propose a method based on the principles of siamese neural networks to extract informative features from differences between two brain regions. The output of this network generates a latent space that characterizes differences between the two hemispheres. This output vector is then fed into a linear SVM classifier. The usefulness of this method has been assessed with images from the Parkinson’s Progression Markers Initiative, demonstrating that differences between the dopaminergic regions of both hemispheres lead to a high performance when classifying controls vs Parkinson’s disease patients

Generation of Virtual Children for testing a Recommendation System for Interventions with Children with Dyslexia.

The LEEDUCA project has developed a recommendation system to generate intervention sessions tailored to children with dyslexia. Due to the limitations in obtaining real data for preliminary testing, the generation of in silico data, referred to as ”virtual children,” has been implemented. This approach allows for the simulation of a wide range of profiles and response patterns, enabling comprehensive testing of the system before its implementation with real users. The behavior of virtual readers is modeled using logistic curves, which reflect the natural evolution of users in a system that suggests words ordered by difficulty over time. By introducing variations to the model based on the coefficients that define the logistic curve, response sequences with different difficulty levels and learning rates can be simulated. To evaluate the stability of the system, multiple variations are generated from a given virtual child, creating a shadow of possible sequences. The generation of virtual children using logistic curves and the controlled introduction of variations in their responses provide a robust framework for testing the recommendation system, ensuring its reliability and adaptability to the individual needs of children with dyslexia.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.

Background: Alzheimer’s disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10 % to 15 % per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments. Method: The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of one vs. rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level. Results: The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25 % classification score on the test subset which consists of 160 real subjects. Comparison with Existing Method(s): The classifier yielded the best performance when compared to: i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and iii) bagging ensemble learning. Conclusions: A robust method has been proposed for the international challenge on MCI prediction based on MRI data.This work was supported by the MINECO/FEDER under TEC2015-64718-R project, the Consejería de Economía, Innovacion, Ciencia, y Empleo of the Junta de Andalucía under the P11-TIC-7103 Excellence Project and the Salvador de Madariaga Mobility Grants 2017

Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson’s Disease.

Finding new biomarkers to model Parkinson’s Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD, but also as a way to measure quantitatively the loss of dopaminergic neurons mainly concentrated at substantia nigra. Within this context, the work presented here tries to provide a set of imaging features based on morphological characteristics extracted from I[123]-Ioflupane SPECT scans to discern between HC and PD participants in a balanced set of 386 scans from Parkinson’s Progression Markers Initiative (PPMI) database. These features, obtained from isosurfaces of each scan at different intensity levels, have been classified through the use of classical Machine Learning classifiers such as Support-Vector-Machines (SVM) or Na¨ıve Bayesian and compared with the results obtained using a Multi-Layer Perceptron (MLP). The proposed system, based on a Mann-Whitney-Wilcoxon U-Test for feature selection and the SVM approach, yielded a 97.04% balanced accuracy when the performance was evaluated using a 10-fold cross-validation. This proves the reliability of these biomarkers, especially those related to sphericity, center of mass, number of vertices, 2D-projected perimeter or the 2D-projected eccentricity; among others, but including both internal and external isosurfaces.This work was supported by the MINECO/FEDER under the RTI2018-098913-B-I00 and PGC2018- 098813-B-C32 projects and the General Secretariat of Universities, Research and Technology, Junta de Andalucía under the Excellence FEDER Project ATIC-117-UGR18

Análisis del perfil motivacional de estudiantes universitarios de tercer semestre del programa de psicología

Resulta de gran importancia la aplicación de instrumentos para medir el nivel motivacional de una población, ya que, al obtener ciertos datos permite determinar la forma adecuada de interactuar con los mismos. El análisis del perfil motivacional aporta información relevante para el desempeño académico de los estudiantes universitarios. Para lograr dicho propósito se empleó un instrumento que evalúa el perfil motivacional tomando como referencia el modelo de rueda de motivos que expone Valderrama (2010), el cual, evalúa diversos motivos que pueden influir en el rendimiento académico y en otras conductas laborales. Dicho instrumento cuenta con una escala de respuesta de tipo likert con 6 opciones, desde 1 (nada importante para mi) a 6 (extremadamente importante para mi) . En lo que corresponde al procesamiento de la información, se utilizó la plataforma formularios de google; Con esto se obtuvieron los datos más importantes desde el análisis estadísticoIt is of great importance to use instruments to measure the motivational level of a population, since, by obtaining certain data, it allows you to determine the appropriate way to interact with them. The analysis of the motivational profile provides relevant information for the academic performance of university students. To achieve this purpose, an instrument was used that evaluates the motivational profile based on the motif wheel model presented by Valderrama (2010) which evaluates various motives that can influence performance and other work behaviors. This instrument has a likert response scale with 6 options, from 1 being (nothing important to me) to 6 being (extremely important to me). As for the processing of information, the google forms platform was used; with this, the most important data from the statistical analysis were obtained

Granger Causality-based Information Fusion Applied to Electrical Measurements from Power Transformers.

In the immediate future, with the increasing presence of electrical vehicles and the large increase in the use of renewable energies, it will be crucial that distribution power networks are managed, supervised and exploited in a similar way as the transmission power systems were in previous decades. To achieve this, the underlying infrastructure requires automated monitoring and digitization, including smart-meters, wide-band communication systems, electronic device based-local controllers, and the Internet of Things. All of these technologies demand a huge amount of data to be curated, processed, interpreted and fused with the aim of real-time predictive control and supervision of medium/low voltage transformer substations. Wiener–Granger causality, a statistical notion of causal inference based on Information Fusion could help in the prediction of electrical behaviour arising from common causal dependencies. Originally developed in econometrics, it has successfully been applied to several fields of research such as the neurosciences and is applicable to time series data whereby cause precedes effect. In this paper, we demonstrate the potential of this methodology in the context of power measures for providing theoretical models of low/medium power transformers. Up to our knowledge, the proposed method in this context is the first attempt to build a data-driven power system model based on G-causality. In particular, we analysed directed functional connectivity of electrical measures providing a statistical description of observed responses, and identified the causal structure within data in an exploratory analysis. Pair-wise conditional G-causality of power transformers, their independent evolution in time, and the joint evolution in time and frequency are discussed and analysed in the experimental section.This work was partly supported by the MINECO/ FEDER under the RTI2018- 098913-B100 project. The authors would like to acknowledge the support of 370 CDTI (Centro para el Desarrollo Tecnologico Industrial, Ministerio de Cien cia, Innovacion y Universidades and FEDER, SPAIN) under the PASTORA project (Ref.: ITC-20181102). and to thank the companies within the PAS TORA consortium: Endesa, Ayesa, Ormaz´abal and Ingelectus. We would like to thank the reviewers for their thoughtful comments and efforts towards im 375 proving our manuscript. Finally, JM Gorriz would like to thank Dr G´omez Exp´osito for his helpful advice and comments

Using XAI in the Clock Drawing Test to reveal the cognitive impairment pattern.

he prevalence of dementia is currently increasing worldwide. This syndrome produces a deteriorationin cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing itsprogress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessmentin which an individual has to manually draw a clock on a paper. There are a lot of scoring systems forthis test and most of them depend on the subjective assessment of the expert. This study proposes acomputer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDTand obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessingpipeline in which the clock is detected, centered and binarized to decrease the computational burden.Then, the resulting image is fed into a Convolutional Neural Network (CNN) to identify the informativepatterns within the CDT drawings that are relevant for the assessment of the patient’s cognitive status.Performance is evaluated in a real context where patients with CI and controls have been classified byclinical experts in a balanced sample size of 3282 drawings. The proposed method provides an accuracyof 75.65% in the binary case-control classification task, with an AUC of 0.83. These results are indeedrelevant considering the use of the classic version of the CDT. The large size of the sample suggests thatthe method proposed has a high reliability to be used in clinical contexts and demonstrates the suitabilityof CAD systems in the CDT assessment process. Explainable artificial intelligence (XAI) methods areapplied to identify the most relevant regions during classification. Finding these patterns is extremelyhelpful to understand the brain damage caused by CI. A validation method using resubstitution withupper bound correction in a machine learning approach is also discusseThis work was supported by the MCIN/ AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018- 098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de An765 dalucia) and FEDER under CV20-45250, A-TIC080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. JimenezMesa and the Margarita-Salas grant to J.E. Arco

Dual-System Recommendation Architecture for Adaptive Reading Intervention Platform Tailored for Dyslexic Learners

Dyslexia poses substantial literacy challenges with profound academic and psychosocial impacts for affected children. Though evidence affirms that early reading interventions can significantly improve outcomes, traditional one-size-fits-all approaches often fail to address students’ unique skill gaps. This study details an adaptive reading platform that customizes word recognition tasks to each learner’s evolving abilities using embedded recommender engines. Initial standardized assessments categorize words by difficulty and cluster students by competency level. An integrated word generator then expands the benchmark lexicon by algorithmically manipulating phonetic properties to modulate complexity. Dual intra-user and inter-user systems track learner performance to tailor content to individuals’ pacing. Heuristic bootstrapping and simulated user data facilitate cold start recommendations and evaluate model robustness. Analysis of five virtual student response patterns demonstrates platform reliability against volatility. Successive interventions display narrowing score dispersion alongside upwards literacy trajectories. Logarithmic score pro-gressions signify responsive tuning to emerging mastery, accelerating ad-vancement, and tapering gains as maximal outcomes reached. Results validate system effectiveness in optimizing challenge levels to unlock growth for neuro-logical diversity. Rapid stabilization around optimal zones signifies an efficiently learned model while improved achievement confirms scaffolding precision. Learning curves substantiate tailored recommendation efficacy and signal user transitions from constructing new knowledge to demonstrative skill gains. Overall, the approach shows immense promise in administering personalized, engagement-focused reading support.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Epidemiological characterization of ischemic heart disease at different altitudes: a nationwide population-based analysis from 2011 to 2021 in Ecuador

Background Cardiovascular diseases, including ischemic heart disease, are the leading cause of prema- ture death and disability worldwide. While traditional risk factors such as smoking, obesity, and diabetes have been thoroughly investigated, non-traditional risk factors like high-alti- tude exposure remain underexplored. This study aims to examine the incidence and mortal- ity rates of ischemic heart disease over the past decade in Ecuador, a country with a diverse altitude profile spanning from 0 to 4,300 meters. Methods We conducted a geographic distribution analysis of ischemic heart disease in Ecuador, uti- lizing hospital discharge and mortality data from the National Institute of Census and Statis- tics for the years 2011–2021. Altitude exposure was categorized according to two distinct classifications: the traditional division into low ( 2,500 m) altitudes, as well as the classification proposed by the International Society of Mountain Medicine, which delineates low (2500 m. Men had more pronounced rates across altitudes, exhibiting 138.7% and 150.0% higher incidence at low and high altitudes respectively, and mortality rates increased by 48.3% at low altitudes and 23.2% at high altitudes relative to women. Conclusion Ecuador bears a significant burden of ischemic heart disease (IHD), with men being more affected than women in terms of incidence. However, women have a higher percentage of mortality post-hospital admission. Regarding elevation, our analysis, using two different alti- tude cutoff points, reveals higher mortality rates in low-altitude regions compared to high- altitude areas, suggesting a potential protective effect of high elevation on IHD risk. Never- theless, a definitive dose-response relationship between high altitude and reduced IHD risk could not be conclusively established

Síndrome de Guillain Barré relacionados a infección por SARS – CoV 2 en Lima, Perú. Reporte de casos

COVID-19 predominantly affects the respiratory tract, but extrapulmonary involvement including the nervous system has been reported. We report two patients who presented SARS-CoV-2 associated Guillain-Barre syndrome.La COVID -19 afecta predominantemente el sistema respiratorio, pero también se ha descrito compromiso extrapulmonar, incluido la afectación del sistema nervioso.  Se describen los casos de dos pacientes con infección por SARS –CoV-2 que desarrollaron el síndrome de Guillain Barré