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El problema objeto de estudio surgió de la preocupación por la falta de capacidad de los estudiantes de grado quinto para interpretar, argumentar y hacer propuestas sobre situaciones de su entorno geográfico, en el marco del desarrollo de las clases en el área de Ciencias Sociales. Es por esa razón que se propuso analizar la incidencia de una propuesta pedagógica basada en la investigación como metodología para el fortalecimiento de la competencia comunicativa durante la pandemia. La investigación tuvo un enfoque cualitativo con alcance descriptivo, involucrando una muestra de tipo no probabilístico conformada por 24 estudiantes de grado quinto. A través del contraste entre la prueba de diagnóstico y la prueba de final, se produjo un mejoramiento de la competencia comunicativa. Dentro de las conclusiones, se pone de relieve el impacto de la propuesta de intervención, sobre todo, en el desarrollo de las habilidades y destrezas de los estudiantes durante la lectura de textos relacionados con los temas del entorno local, abordados desde el área de Ciencias Sociales. Se consideran muy importantes los avances obtenidos principalmente en las competencias argumentativa y propositiva, precisamente allí donde la prueba de diagnóstico mostró los desempeños más desfavorables.

Abstract: A fundamental challenge in the fight against COVID -19 is the development of reliable and accurate tools to predict disease progression in a patient. This information can be extremely useful in distinguishing hospitalized patients at higher risk for needing UCI from patients with low severity. How SARS-CoV-2 infection will evolve is still unclear. Methods: A novel pipeline was developed that can integrate RNA-Seq data from different databases to obtain a genetic biomarker COVID -19 severity index using an artificial intelligence algorithm. Our pipeline ensures robustness through multiple cross-validation processes in different steps. Results: CD93, RPS24, PSCA, and CD300E were identified as a COVID -19 severity gene signature. Furthermore, using the obtained gene signature, an effective multi-class classifier capable of discriminating between control, outpatient, inpatient, and ICU COVID -19 patients was optimized, achieving an accuracy of 97.5%. Conclusion: In summary, during this research, a new intelligent pipeline was implemented with the goal of developing a specific gene signature that can detect the severity of patients suffering COVID -19. Our approach to clinical decision support systems achieved excellent results, even when processing unseen samples. Our system can be of great clinical utility for the strategy of planning, organizing and managing human and material resources, as well as for automatically classifying the severity of patients affected by COVID -19.

Background: Despite all the medical advances introduced for personalized patient treatment and the research supported in search of genetic patterns inherent to the occurrence of its different manifestations on the human being, the unequivocal and effective treatment of cancer, unfortunately, remains as an unresolved challenge within the scientific panorama. Until a universal solution for its control is achieved, early detection mechanisms for preventative diagnosis increasingly avoid treatments, resulting in unreliable effectiveness. The discovery of unequivocal gene patterns allowing us to discern between multiple pathological states could help shed light on patients suspected of an oncological disease but with uncertainty in the histological and immunohistochemical results. Methods: This study presents an approach for pan-cancer diagnosis based on gene expression analysis that determines a reduced set of 12 genes, making it possible to distinguish between the main 14 cancer diseases. Results: Our cascade machine learning process has been robustly designed, obtaining a mean F1 score of 92% and a mean AUC of 99.37% in the test set. Our study showed heterogeneous over-or underexpression of the analyzed genes, which can act as oncogenes or tumor suppressor genes. Upregulation of LPAR5 and PAX8 was demonstrated in thyroid cancer samples. KLF5 was highly expressed in the majority of cancer types. Conclusion: Our model constituted a useful tool for pan-cancer gene expression evaluation. In addition to providing biological clues about a hypothetical common origin of cancer, the scalability of this study promises to be very useful for future studies to reinforce, confirm, and extend the biological observations presented here. Code availability and datasets are stored in the following GitHub repository to aim for the research reproducibility: https://github.com/CasedUgr/PanCancerClassification.

In this paper, the analysis of the design of the single-cylinder steam engine of the Grasshopper beam designed by Henry Muncaster in 1912 is shown. This engine was incorporated into ships and railways and it was published in the Model Engineer journal in 1957. It shows great complexity due to its ability to transform reciprocating movement into rotary movement and its high number of elements (more than 150). To this end, a study of computer-aided engineering (CAE) was carried out using the software Autodesk Inventor Nastran, consisting of a linear static analysis using the finite element method (FEM) of the 3D CAD model under real operating conditions in order to learn if it was well designed, according to material resistance criteria. The assembly is analyzed in the two most unfavorable situations in order to determine the von Mises stresses, the displacements and the safety coefficient distributions. Finally, it was found that the work pressure (maximum admissible steam pressure in the admission) was 0.15 MPa.