Parkinson’s disease (PD) is a progressive neurodegenerative disorder whose prevalence has steadily been rising over the years. Specialist neurologists across the world assess and diagnose patients with PD, although the diagnostic process is time-consuming and various symptoms take years to appear, which means that the diagnosis is prone to human error. The partial automatization of PD assessment and diagnosis through computational processes has therefore been considered for some time. One well-known tool for PD assessment is finger tapping (FT), which can now be assessed through computer vision (CV). Artificial intelligence and related advances over recent decades, more specifically in the area of CV, have made it possible to develop computer systems that can help specialists assess and diagnose PD. The aim of this study is to review some advances related to CV techniques and FT so as to offer insight into future research lines that technological advances are now opening up.

The consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson’s disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs.

Protein fitness prediction plays a crucial role in the advancement of protein engineering endeavours. However, the combinatorial complexity of the protein sequence space and the limited availability of assay-labelled data hinder the efficient optimization of protein properties. Data-driven strategies utilizing machine learning methods have emerged as a promising solution, yet their dependence on labelled training datasets poses a significant obstacle. To overcome this challenge, in this work, we explore various ways of introducing the latent information present in evolutionarily related sequences (homologous sequences) into the training process. To do so, we establish several strategies based on semi-supervised learning (unsupervised pre-processing and wrapper methods) and perform a comprehensive comparison using 19 datasets containing protein-fitness pairs. Our findings reveal that using the information present in the homologous sequences can improve the performance of the models, especially when the number of available labelled sequences is considerably low. Specifically, the combination of a sequence encoding method based on Direct Coupling Analysis (DCA), with MERGE (a hybrid regression framework that combines evolutionary information with supervised learning) and an SVM regressor, outperforms other encodings (PAM250, UniRep, eUniRep) and other semi-supervised wrapper methods (Tri-Training Regressor, Co-Training Regressor). In summary, the demonstrated performance gains of this strategy mark a substantial leap towards more robust and reliable predictive models for protein engineering tasks. This advancement holds the potential to streamline the design and optimisation of proteins for diverse applications in biotechnology and therapeutics. We explore several semi-supervised learning strategies capable of including the homologous sequences (unlabelled) to the protein of interest in the training process. Among them, we present two new methods to exploit the information in the homologous sequences: i) a new generalised version of MERGE capable of employing any regressor as a base estimator; ii) the Tri-Training Regressor method, an adaptation of the Tri-Training method for regression problems. We find that the information inherent in the homologous sequences has the ability to improve the predictive capacity of models when the number of available sequences is scarce, especially when using the DCA encoding together with MERGE and an SVM regressor.

Understanding the scientific and social structure of a discipline is a fundamental aspect for scientific evaluation processes, identifying trends and niches, and balancing the trade-off between exploitation and exploration in research. In the present contribution, the production of doctoral theses is used as a proxy to analyze the scientific structure of the knowledge area of business organization in Spain. To that end, a complex networks approach is selected, and two different networks are built: (i) the social network of co-participation in thesis examining committees and thesis supervision, and (ii) a bipartite network of theses and thesis descriptors. The former has a modular structure that is partially explained by thematic specialization in different subdisciplines. The latter serves to assess the interdisciplinary structure of the discipline, as it enables the characterization of affinity levels between fields, research poles and thematic clusters. Our results have implications for the scientific evaluation and formal definition of related fields.

Moodle quizzes are a convenient way of online assessment, benefiting both teachers and students. Unfortunately, their preparation is slow, cumbersome, and error-prone. In addition to the effort of designing the questions, it is necessary to enter them in Moodle. Furthermore, for questions that require images, these must first be created and then uploaded to Moodle. If this process has to be repeated with many questions, the required work becomes prohibitive. This paper presents a tool that solves all these problems, allowing the automatic and massive generation of questions for the evaluation of the knowledge about lexical analysis algorithms. The evaluation of these algorithms is relevant in the subjects of both automata and formal languages and in those of compiler design. The tool allows the creation of exercises of configurable complexity, after which the text, tables, and associated images are generated for quick upload to Moodle. The main impact of this tool is the substantial reduction of question preparation time, reducing hundreds or thousands of interactions with Moodle forms to just a few simple steps. In addition, the ease of question generation makes it possible to prepare self-assessment questionnaires for the students, something that they greatly appreciate as a study aid.

La obra «Sistemas Inteligentes aplicados a la Psicología» tiene como objetivo acercar al alumnado de Psicología a nuevos conceptos relacionados con la computación, el análisis de datos y la aplicación de recursos de neuropsicología en el diagnóstico y la intervención en problemáticas propias del ámbito profesional de la Psicología. Asimismo, este trabajo busca concienciar a los y las profesionales del área sobre la necesidad de ampliar su formación en estas herramientas, cuya utilización resulta esencial en la sociedad del siglo XXI. Este manual también puede resultar de interés para otros perfiles profesionales, como ingenieros biomédicos o de la salud, ya que la perspectiva de trabajo interdisciplinar es clave para la aplicación efectiva de estos recursos tecnológicos, así como para la extracción e interpretación del gran volumen de datos que generan. En particular, se aborda el uso de tecnología multicanal integrada mediante dispositivos interconectados que registran señales como el rastreo ocular, la respuesta psicogalvánica de la piel y el electroencefalograma. Además, se introduce el uso de técnicas de aprendizaje automático para el procesamiento y análisis de los datos recogidos. La obra cuenta con un prólogo de la Dra. Rebeca Cerezo Menéndez, de la Universidad de Oviedo, experta de reconocido prestigio internacional en la materia. Los capítulos han sido elaborados por profesorado de la Universidad de Burgos, miembros de los grupos de investigación ADMIRABLE y DATAHES, así como de las Unidades de Investigación Reconocidas de la Junta de Castilla y León n.º 170 y n.º 348, cuyos trabajos en estos ámbitos gozan de reconocimiento internacional.