Desarrollo de material virtual e interactivo para la docencia práctica de la Anatomía Humana I en el Grado de Medicina a través de b-learning

El objetivo principal de este proyecto es virtualizar parcialmente la docencia práctica de la asignatura Anatomía Humana I del grado de Medicina, con el objetivo de incrementar la implicación del alumnado en el proceso de aprendizaje y reforzar los conocimientos adquiridos en las sesiones prácticas, todo ello, a través de un modelo docente mixto basado en Blended Learning (b-learning), que aúna las ventajas de la enseñanza presencial tradicional y la virtual (e-learning)

A Proposal for Multimodal Emotion Recognition Using Aural Transformers and Action Units on RAVDESS Dataset

The work leading to these results was supported by the Spanish Ministry of Science and Innovation through the projects GOMINOLA (PID2020-118112RB-C21 and PID2020-118112RB-C22, funded by MCIN/AEI/10.13039/501100011033), CAVIAR (TEC2017-84593-C2-1-R, funded by MCIN/AEI/10.13039/501100011033/FEDER "Una manera de hacer Europa"), and AMIC-PoC (PDC2021-120846-C42, funded by MCIN/AEI/10.13039/501100011033 and by "the European Union "NextGenerationEU/PRTR"). This research also received funding from the European Union's Horizon2020 research and innovation program under grant agreement No 823907 (http://menhir-project.eu, accessed on 17 November 2021). Furthermore, R.K.'s research was supported by the Spanish Ministry of Education (FPI grant PRE2018-083225).Emotion recognition is attracting the attention of the research community due to its multiple applications in different fields, such as medicine or autonomous driving. In this paper, we proposed an automatic emotion recognizer system that consisted of a speech emotion recognizer (SER) and a facial emotion recognizer (FER). For the SER, we evaluated a pre-trained xlsr-Wav2Vec2.0 transformer using two transfer-learning techniques: embedding extraction and fine-tuning. The best accuracy results were achieved when we fine-tuned the whole model by appending a multilayer perceptron on top of it, confirming that the training was more robust when it did not start from scratch and the previous knowledge of the network was similar to the task to adapt. Regarding the facial emotion recognizer, we extracted the Action Units of the videos and compared the performance between employing static models against sequential models. Results showed that sequential models beat static models by a narrow difference. Error analysis reported that the visual systems could improve with a detector of high-emotional load frames, which opened a new line of research to discover new ways to learn from videos. Finally, combining these two modalities with a late fusion strategy, we achieved 86.70% accuracy on the RAVDESS dataset on a subject-wise 5-CV evaluation, classifying eight emotions. Results demonstrated that these modalities carried relevant information to detect users’ emotional state and their combination allowed to improve the final system performance.Spanish Government PID2020-118112RB-C21 PID2020-118112RB-C22 MCIN/AEI/10.13039/501100011033 TEC2017-84593-C2-1-R MCIN/AEI/10.13039/501100011033/FEDER PDC2021-120846-C42European Union "NextGenerationEU/PRTR")European Union's Horizon2020 research and innovation program 823907German Research Foundation (DFG) PRE2018-08322

Spatial variability of COVID-19 first wave severity and transmission intensity in Spain: the influence of meteorological factors

Within the same country, Spain, with the same cultural aspects and containment policies (without lockdown), why in the initial moment of the COVID-19 first wave, given a significant number of infections, the disease prospered more intensely in some areas than in others? The hypothesis is that the meteorological factors, that is, the utbreak weather conditions are relevant factors which could be used as early indicators of the COVID-19 first wave severity and transmission intensity. This paper presents a model that allows predicting COVID-19 first wave severity and transmission intensity in Spain based on early weather informatio

GPCA vs. PCA in Recognition and 3-D Localization of Ultrasound Reflectors

In this paper, a new method of classification and localization of reflectors, using the time-of-flight (TOF) data obtained from ultrasonic transducers, is presented. The method of classification and localization is based on Generalized Principal Component Analysis (GPCA) applied to the TOF values obtained from a sensor that contains four ultrasound emitters and 16 receivers. Since PCA works with vectorized representations of TOF, it does not take into account the spatial locality of receivers. The GPCA works with two-dimensional representations of TOF, taking into account information on the spatial position of the receivers. This report includes a detailed description of the method of classification and localization and the results of achieved tests with three types of reflectors in 3-D environments: planes, edges, and corners. The results in terms of processing time, classification and localization were very satisfactory for the reflectors located in the range of 50–350 cm

Fast heuristic method to detect people in frontal depth images

This paper presents a new method for detecting people using only depth images captured by a camera in a frontal position. The approach is based on first detecting all the objects present in the scene and determining their average depth (distance to the camera). Next, for each object, a 3D Region of Interest (ROI) is processed around it in order to determine if the characteristics of the object correspond to the biometric characteristics of a human head. The results obtained using three public datasets captured by three depth sensors with different spatial resolutions and different operation principle (structured light, active stereo vision and Time of Flight) are presented. These results demonstrate that our method can run in realtime using a low-cost CPU platform with a high accuracy, being the processing times smaller than 1 ms per frame for a 512 × 424 image resolution with a precision of 99.26% and smaller than 4 ms per frame for a 1280 × 720 image resolution with a precision of 99.77%

People re-identification using depth and intensity information from an overhead sensor

This work presents a new people re-identification method, using depth and intensity images, both of them captured with a single static camera, located in an overhead position. The proposed solution arises from the need that exists in many areas of application to carry out identification and re-identification processes to determine, for example, the time that people remain in a certain space, while fulfilling the requirement of preserving people's privacy. This work is a novelty compared to other previous solutions, since the use of top-view images of depth and intensity allows obtaining information to perform the functions of identification and re-identification of people, maintaining their privacy and reducing occlusions. In the procedure of people identification and re-identification, only three frames of intensity and depth are used, so that the first one is obtained when the person enters the scene (frontal view), the second when it is in the central area of the scene (overhead view) and the third one when it leaves the scene (back view). In the implemented method only information from the head and shoulders of people with these three different perspectives is used. From these views three feature vectors are obtained in a simple way, two of them related to depth information and the other one related to intensity data. This increases the robustness of the method against lighting changes. The proposal has been evaluated in two different datasets and compared to other state-of-the-art proposal. The obtained results show a 96,7% success rate in re-identification, with sensors that use different operating principles, all of them obtaining depth and intensity information. Furthermore, the implemented method can work in real time on a PC, without using a GPU.Ministerio de Economía y CompetitividadAgencia Estatal de InvestigaciónUniversidad de Alcal

Serum biomarkers for the differentiation of autoimmune pancreatitis from pancreatic ductal adenocarcinoma

Autoimmune pancreatitis (AIP), a chronic inflammation caused by the immune system attacking the pancreas, usually presents imaging and clinical features that overlap with those of pancreatic ductal adenocarcinoma (PDAC). Serum biomarkers, substances that quantitatively change in sera during disease development, are a promising non-invasive tool with high utility for differentiating between these diseases. In this way, the presence of AIP is currently suspected when serum concentrations of immunoglobulin G4 (IgG4) antibody are elevated. However, this approach has some drawbacks. Notably, IgG4 antibody concentrations are also elevated in sera from some patients with PDAC. This review focuses on the most recent and relevant serum biomarkers proposed to differentiate between AIP and PDAC, evaluating the usefulness of immunoglobulins, autoantibodies, chemokines, and cytokines. The proposed serum biomarkers have proven useful, although most studies had a small sample size, did not examine their presence in patients with PDAC, or did not test them in humans. In addition, current evidence suggests that a single serum biomarker is unlikely to accurately differentiate these diseases and that a set of biomarkers will be needed to achieve adequate specificity and sensitivity, either alone or i

Spatial variability in threshold temperatures of heat wave mortality: impact assessment on prevention plans

Spain’s current heat wave prevention plans are activated according to administrative areas. This study analyses the determination of threshold temperatures for triggering prevention-plan activation by reference to isoclimatic areas, and describes the public health benefits. We subdivided the study area – the Madrid Autonomous Region (MAR) – into three, distinct, isoclimatic areas: ‘North’, ‘Central’ and ‘South’, and grouped daily natural-cause mortality (ICD-10: A00-R99) in towns of over 10,000 inhabitants (2000–2009 period) accordingly. Using these three areas rather than the MAR as a whole would have resulted in a possible decrease in mortality of 73 persons (38– 108) in the North area, and in aborting unnecessary activation of the plan 153 times in the Central area and 417 times in the South area. Our results indicate that extrapolating this methodology would bring benefits associated with a reduction in attributable mortality and improved effectiveness of public health interventions.This study was funded by a ‘Miguel Servet type 1’ grant (SEPY 1037/14), as well as a Health Research Fund grant (Fondo de Investigaciones Sanitarias/FIS Project ENPY1133/16 from the Carlos III Institute of Health

Evaluating Metabolite-Based Biomarkers for Early Diagnosis of Pancreatic Cancer: A Systematic Review

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with five-year survival rates around 10%. The only curative option remains complete surgical resection, but due to the delay in diagnosis, less than 20% of patients are eligible for surgery. Therefore, discovering diagnostic biomarkers for early detection is crucial for improving clinical outcomes. Metabolomics has become a powerful technology for biomarker discovery, and several metabolomic-based panels have been proposed for PDAC diagnosis, but these advances have not yet been translated into the clinic. Therefore, this review focused on summarizing metabolites identified for the early diagnosis of PDAC in the last five years. Bibliographic searches were performed in the PubMed, Scopus and WOS databases, using the terms “Biomarkers, Tumor”, “Pancreatic Neoplasms”, “Early Diagnosis”, “Metabolomics” and “Lipidome” (January 2018–March 2023), and resulted in the selection of fourteen original studies that compared PDAC patients with subjects with other pancreatic diseases. These investigations showed amino acid and lipid metabolic pathways as the most commonly altered, reflecting their potential for biomarker research. Furthermore, other relevant metabolites such as glucose and lactate were detected in the pancreas tissue and body fluids from PDAC patients. Our results suggest that the use of metabolomics remains a robust approach to improve the early diagnosis of PDAC. However, these studies showed heterogeneity with respect to the metabolomics techniques used and further studies will be needed to validate the clinical utility of these biomarkersB-TIC-414-UGR18 (Junta deAndalucía 2020) (FEDER

Serum nuclear magnetic resonance metabolomics analysis of human metastatic colorectal cancer: Biomarkers and pathway analysis

Junta de Andalucía, Grant/Award Numbers: 102C2000004, UAL2020-AGR-B1781, P20_01041; Gobierno de España, Grant/Award Numbers: PDC2021– 121248-I00, PLEC2021–007774; Instituto de Salud Carlos III (ISCIII), Grant/Award Number: PI19/01478; CTS-107 and FQM-376 groupsWe describe the use of nuclear magnetic resonance metabolomics to analyze blood serum samples from healthy individuals (n = 26) and those with metastatic colorectal cancer (CRC; n = 57). The assessment, employing both linear and nonlinear multivari- ate data analysis techniques, revealed specific metabolite changes associated with metastatic CRC, including increased levels of lactate, glutamate, and pyruvate, and decreased levels of certain amino acids and total fatty acids. Biomarker ratios such as glutamate-to-glutamine and pyruvate-to-alanine were also found to be related to CRC. The study also found that glutamate was linked to progression-free survival and that both glutamate and 3-hydroxybutyrate were risk factors for metastatic CRC. Additionally, gas chromatography coupled to flame-ionization detection was utilized to analyze the fatty acid profile and pathway analysis was performed on the profiled metabolites to understand the metabolic processes involved in CRC. A correlation was also found between the presence of certain metabolites in the blood of CRC patients and certain clinical features.Junta de Andalucia 102C2000004, UAL2020-AGR-B1781, P20_01041Gobierno de España MCIN/AEI/10.13039/501100011033/Unión Europea “Next GenerationEU”/PRTR (PDC2021–121248-I00 and PLEC2021–007774)Instituto de Salud Carlos III (ISCIII) (PI19/01478) (FEDER)CTS-107FQM-37