Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing

[EN] Purpose The main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals. Methods We employed natural language processing (NLP) techniques along with ML models like decision trees, extreme gradient boosting (XGB), k-nearest neighbors algorithm (KNN), and DL models such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), bidirectional encoder representations from transformers (BERT and BERTweet). We extracted a dataset of 404,627 tweets from Twitter users using the platform’s API and classified them based on whether they were written by individuals claiming to have ASD (ASD users) or by those without ASD (non-ASD users). From this dataset, we used a subset of 90,000 tweets (45,000 from each classification group) for the training and testing of these models. Results The application of our AI models yielded promising results, with the predictive model reaching an accuracy of almost 88% when classifying texts that potentially originated from individuals with ASD. Conclusion Our research demonstrated the potential of using AI, particularly DL models, in enhancing the accuracy of ASD detection and diagnosis. This innovative approach signifies the critical role AI can play in advancing early diagnostic techniques, enabling better patient outcomes and underlining the importance of early identification of ASD, especially in children.SIUniversidad de Leó

TEDNet: Twin Encoder Decoder Neural Network for 2D Camera and LiDAR Road Detection

[EN] Robust road surface estimation is required for autonomous ground vehicles to navigate safely. Despite it becoming one of the main targets for autonomous mobility researchers in recent years, it is still an open problem in which cameras and LiDAR sensors have demonstrated to be adequate to predict the position, size and shape of the road a vehicle is driving on in different environments. In this work, a novel Convolutional Neural Network model is proposed for the accurate estimation of the roadway surface. Furthermore, an ablation study has been conducted to investigate how different encoding strategies affect model performance, testing 6 slightly different neural network architectures. Our model is based on the use of a Twin Encoder-Decoder Neural Network (TEDNet) for independent camera and LiDAR feature extraction, and has been trained and evaluated on the Kitti-Road dataset. Bird’s Eye View projections of the camera and LiDAR data are used in this model to perform semantic segmentation on whether each pixel belongs to the road surface. The proposed method performs among other state-of-the-art methods and operates at the same frame-rate as the LiDAR and cameras, so it is adequate for its use in real-time applications.SIThis work is partially supported by Universidad de León, under the ”Programa Propio de Investigación de la Universidad de León 2021” grant

Heart disease risk prediction using deep learning techniques with feature augmentation

[EN] Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

ROS-MATLAB HMI for industrial robot teleoperation

[Resumen] ROS (Robot Operating System) es un framework para el desarrollo de sistemas robóticos de código abierto ampliamente utilizado en la industria y la investigación. Los sistemas robóticos asistidos requieren una comunicación fluida y fiable entre ROS y el controlador externo, a fin de lograr un control y supervisión eficaz del mismo. En este artículo, se presenta el procedimiento necesario para establecer la comunicación a través de una red local entre un robot móvil que implementa ROS y un dispositivo remoto desde el que enviar y recibir información del robot. Además, se propone un ejemplo de HMI (Interfaz Hombre-Máquina) desarrollado en MATLAB, y que puede ser instalada en equipos Windows, Linux o MacOSX, para la teleoperación de un robot en un entorno industrial. La comunicación bidireccional en tiempo real, las capacidades de procesamiento de datos y su versatilidad la convierten en una herramienta completa para la gestión de datos robóticos en entornos industriales.[Abstract] ROS (Robot Operating System) is an open-source robotics development framework widely used in industry and research. Human assisted robotic systems require of robust and constant communication between ROS and the remote controller in order to achieve effective control and monitoring of the system. In this paper, we propose a methodology to connect a mobile ROS with a remote device, using a wireless local network. This device serves as link between the robot and the rest of the system. Additionally, an HMI (Human-Machine Interface) developed using MATLAB is presented. The HMI can be implemented in Windows, Linux or MacOSX computers and provides teleoperation capabilities for a robot in an industrial environment. Real-time bidirectional communication, data processing capabilities and intrinsic versatility make this HMI a robust tool for robotic data management in industrial applications.Ministerio Ciencia e Innovación; PLEC2021-007819Comunidad de Madrid; S2018/NMT-433

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals

A novel intelligent approach for man‐in‐the‐middle attacks detection over internet of things environments based on message queuing telemetry transport

[EN] One of the most common attacks is man-in-the-middle (MitM) which, due to its complex behaviour, is difficult to detect by traditional cyber-attack detection systems. MitM attacks on internet of things systems take advantage of special features of the protocols and cause system disruptions, making them invisible to legitimate elements. In this work, an intrusion detection system (IDS), where intelligent models can be deployed, is the approach to detect this type of attack considering network alterations. Therefore, this paper presents a novel method to develop the intelligent model used by the IDS, being this method based on a hybrid process. The first stage of the process implements a feature extraction method, while the second one applies different supervised classification techniques, both over a message queuing telemetry transport (MQTT) dataset compiled by authors in previous works. The contribution shows excellent performance for any compared classification methods. Likewise, the best results are obtained using the method with the highest computational cost. Thanks to this, a functional IDS will be able to prevent MQTT attacks.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

A novel intelligent approach for Man-In-The-Middle attacks detection over IoT environments based on MQTT

One of the most common attacks is man-in-the-middle (MitM) which, due to its complex behaviour, is difficult to detect by traditional cyber-attack detection systems. MitM attacks on internet of things systems take advantage of special features of the protocols and cause system disruptions, making them invisible to legitimate elements. In this work, an intrusion detection system (IDS), where intelligent models can be deployed, is the approach to detect this type of attack considering network alterations. Therefore, this paper presents a novel method to develop the intelligent model used by the IDS, being this method based on a hybrid process. The first stage of the process implements a feature extraction method, while the second one applies different supervised classification techniques, both over a message queuing telemetry transport (MQTT) dataset compiled by authors in previous works. The contribution shows excellent performance for any compared classification methods. Likewise, the best results are obtained using the method with the highest computational cost. Thanks to this, a functional IDS will be able to prevent MQTT attacks.Spanish National Cybersecurity Institute (INCIBE) and developed Research Institute of Applied Sciences in Cybersecurity (RIASC). CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). Álvaro Michelena’s research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/), under the “Formación de Profesorado Universitario” grant with reference FPU21/00932. Martín Bayón’s research was supported by Universidad de León, under the "Programa Propio de Investigación de la Universidad de León 2021" grant. University of Leon. Support for ULE local research projects program. Ref. 2021/00145/001. Internal Code U252

Proyecto Verbum

Se desarrolla un proyecto de innovación educativa que pretende incrementar el número de lecturas efectuadas por el alumnado tomando como referencia los años anteriores, alcanzando un grado de disfrute de la propia actividad de leer. Para motivar el hábito de leer se procura vincular los contenidos de los textos a leer a los intereses y preocupaciones del alumnado, proporcionando un listado y son ellos mismos quines elige sus lecturas. Se trata de concienciar ala alumnado de que la lectura y el buen uso de la lengua son instrumentos fundamentales para su desarrollo personal y educativo. El proyecto pretende a su vez implicar a los familiares en el proceso educativo de sus hijos o hijas, abriendo nuevas vías de comunicación entre las personas y propiciando encuentros entre lectores, ya sean presenciales o a través de la red. A través de las Nuevas Tecnologías se pretende ampliar las posibilidades técnicas en las actividades lectoras. El profesorado presenta los argumentos, acciones o personajes de los libros de lectura a elegir para que elija el que prefiera. El alumnado una vez leído el libro elegido, realiza un comentario del texto e invita a sus compañeros y compañeras a leerlo si así lo cree conveniente a modo de consejo. Los alumnos y alumnas que han leído un mismo libro preparan en grupo una exposición ante el resto de la clase, del contenido del libro, de sus impresiones, valoraciones y estructura. El profesorado de las diferentes áreas advierte al alumnado los contenidos relacionados en la lectura con su materia y les proporciona información adicional que les permita sacar el máximo provecho de la lectura. En el centro se han desarrolla actividades extraescolares y complementarias como encuentros con escritores, exposiciones sobre autores y obras, recitales de poesía, lecturas dramatizadas. La valoración general del proyecto tanto por parte del profesorado como del alumnado se es positiva, a pesar de las dificultades generadas.Castilla y LeónConsejería de Educación. Dirección General de Universidades e Investigación; Monasterio de Nuestra Señora de Prado, Autovía Puente Colgante s. n.; 47071 Valladolid; +34983411881; +34983411939ES

Diseño y desarrollo de una herramienta audiovisual para la docencia virtual de la inspección veterinaria oficial de pescados y productos de la pesca en un mercado central

El objetivo global de este Proyecto de Innovación Docente es la creación de vídeos explicativos como una herramienta de aprendizaje incorporada en el Campus Virtual para mejorar el estudio sobre las actividades de higiene, inspección y control alimentario que se realizan en el Mercado Central de Pescados de Mercamadrid. La creación y el empleo de estos vídeos están dirigidos, en un principio, a los estudiantes universitarios de Grado en Veterinaria que cursan la asignatura de Higiene, Inspección y Seguridad Alimentaria. En este Proyecto se han creado vídeos explicativos que tratan sobre: (i) los controles oficiales realizados por los Técnicos Superiores Veterinarios de Mercamadrid; (ii) los riesgos sanitarios asociados al consumo de pescados, crustáceos y moluscos; (iii) la frescura del pescado; (iv) el etiquetado del pescado; (v) la identificación de especies de pescado y marisco; (vi) la prevención de fraudes en la comercialización de pescados y mariscos; y (vii) la autentificación de pescados fileteados mediante técnicas de análisis

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)