Teachers’ professional identity and Movements for Pedagogic Renovation

Esta investigación estudia si la identidad profesional de los docentes inmersos en los Movimientos de Renovación Pedagógica (MRP) se ve influida de manera sustancial por formar parte de estos colectivos. Por medio de una metodología cualitativa –se emplean entrevistas en profundidad para recopilar información– se analiza la identidad profesional de seis profesores involucrados en MRP, con amplia y media experiencia docente. Los resultados permiten afirmar que la identidad profesional de los docentes entrevistados se ve modificada por formar parte activa de los MRPThis research studies whether the teachers’ professional identity immersed in Movements for Pedagogic Renovation (MRP) is affected by being part of these groups. The methodology used is the qualitative because we use interviews to collect data, which were did to six teachers involved in MRP with a wide and medium teaching experience. The results affirm that the interviewed teachers’ professional identity is modified by being an active part of the MR

Rural European population facing the challenges of global citizenship education

Supporting the creation of a critical citizen, with deeply rooted community values of solidarity and active compromise to the spread and development of those values, is making means believing in a better world in which no one can nor should feel excluded. Nevertheless, this is met with differing opinions. This study approaches attitudes towards Global Citizenship Education specifically though the framework of the population of rural Europeans who participated in the Rural DEAR Agenda ‒ EYD 2015 Project. In order to do, quantitative research was carried out based on a survey and questionnaire developed. The questionnaire focused on interest in international solidarity; for example, opinions and attitudes towards injustices suffered by impoverished countries, as well as opinions surrounding the possibility of changing this situation. The results obtained reveal a shocking reality in many ways relative to international solidarity and a low level of compromise regarding taking action to change the current situation.Apoyar la creación de un ciudadano crítico, con valores comunitarios arraigados de solidaridad y compromiso activo con la difusión y el desarrollo de esos valores, es hacer creer en un mundo mejor en el que nadie pueda ni deba sentirse excluido. Sin embargo, esto topa con opiniones divergentes. En este estudio, se abordan las actitudes hacia la educación para la ciudadanía global, específicamente a través de la población de europeos rurales que participaron en el Proyecto Rural Dear Agenda ‒ EYD 2015. Para ello, se llevó a cabo una investigación cuantitativa basada en una encuesta y un cuestionario previamente elaborados. El cuestionario se centraba en el interés por la solidaridad internacional; por ejemplo, las opiniones y actitudes ante las injusticias que sufren los países empobrecidos, así como las opiniones en torno a la posibilidad de cambiar dicha situación. Los resultados obtenidos revelan una realidad chocante en muchos aspectos relativos a la solidaridad internacional y un bajo nivel de compromiso en cuanto a la adopción de medidas para cambiar la situación actual

Revolutionizing Pharmaceuticals: Generative Artificial Intelligence as a bibliographic assistant

[EN]Artificial Generative Intelligence (AGI) has exploded into biomedical and pharmaceutical research, fundamentally transforming the way scientists approach literature review, experiment design, and reagent and antibody selection. This article explores how IAG, supported by advanced machine learning and natural language processing models, has revolutionized these processes. The IAG streamlines literature review, extracting relevant information, identifying emerging patterns and trends in the scientific literature, and generating innovative hypotheses. It also acts as an advanced search tool, allowing researchers to quickly access accurate information in an ocean of data. A prominent example of this application is BenchSci, a platform that uses the IAG to recommend reagents and antibodies based on real experimental data and scientific literature. This integration of IAG into experimental design promises to accelerate research, reduce costs, and improve the precision of experiments. Together, the IAG is presented as a catalyst for discoveries in pharmaceutical and biomedical research, offering unprecedented potential to advance the understanding and treatment of diseases, and improve decision-making in the industry

NGS data analysis: a review of major tools and pipeline frameworks for variant discovery

[EN]The analysis of genetic data has always been a problem due to the large amount of information available and the difficulty in isolating that which is relevant. However, over the years progress in sequencing techniques has been accompanied by a development of computer techniques to the current application of artificial intelligence. We can summarize the phases of sequence analysis in the following: quality assessment, alignment, pre-variant processing, variant calling and variant annotation. In this article we will review and comment on the tools used in each phase of genetic sequencing, and analyze the drawbacks and advantages offered by each of them

Review of state-of-the-art algorithms for genomics data analysis pipelines

[EN]The advent of big data and advanced genomic sequencing technologies has presented challenges in terms of data processing for clinical use. The complexity of detecting and interpreting genetic variants, coupled with the vast array of tools and algorithms and the heavy computational workload, has made the development of comprehensive genomic analysis platforms crucial to enabling clinicians to quickly provide patients with genetic results. This chapter reviews and describes the pipeline for analyzing massive genomic data using both short-read and long-read technologies, discussing the current state of the main tools used at each stage and the role of artificial intelligence in their development. It also introduces DeepNGS (deepngs.eu), an end-to-end genomic analysis web platform, including its key features and applications

Application of Deep Symbolic Learning in NGS

[EN]The application of Deep Symbolic Learning in genomic analysis has begun to gain traction as a promising approach to interpret and understand vast data sets derived from DNA sequencing. Next-generation sequencing (NGS) techniques have revolutionized the field of clinical genetics and human biology, generating massive volumes of data that require advanced tools for analysis. However, traditional methods are often too abstract or complicated for clinical staff. This work focuses on exploring how Deep Symbolic Learning, a subfield of explainable artificial intelligence (XAI), can be effectively applied to NGS data. A detailed evaluation of the suitability of different architectures will be carried out

Deep Symbolic Learning Architecture for Variant Calling in NGS

[EN]The Variant Detection process (Variant Calling) is fundamental in bioinformatics, demanding maximum precision and reliability. This study examines an innovative integration strategy between a traditional pipeline developed in-house and an advanced Intelligent System (IS). Although the original pipeline already had tools based on traditional algorithms, it had limitations, particularly in the detection of rare or unknown variants. Therefore, SI was introduced with the aim of providing an additional layer of analysis, capitalizing on deep and symbolic learning techniques to improve and enhance previous detections. The main technical challenge lay in interoperability. To overcome this, NextFlow, a scripting language designed to manage complex bioinformatics workflows, was employed. Through NextFlow, communication and efficient data transfer between the original pipeline and the SI were facilitated, thus guaranteeing compatibility and reproducibility. After the Variant Calling process of the original system, the results were transmitted to the SI, where a meticulous sequence of analysis was implemented, from preprocessing to data fusion. As a result, an optimized set of variants was generated that was integrated with previous results. Variants corroborated by both tools were considered to be of high reliability, while discrepancies indicated areas for detailed investigations. The product of this integration advanced to subsequent stages of the pipeline, usually annotation or interpretation, contextualizing the variants from biological and clinical perspectives. This adaptation not only maintained the original functionalities of the pipeline, but was also enhanced with the SI, establishing a new standard in the Variant Calling process. This research offers a robust and efficient model for the detection and analysis of genomic variants, highlighting the promise and applicability of blended learning in bioinformaticsThis study has been funded by the AIR Genomics project (with file number CCTT3/20/SA/0003), through the call 2020 R&D PROJECTS ORIENTED TO THE EXCELLENCE AND COMPETITIVE IMPROVEMENT OF THE CCTT by the Institute of Business Competitiveness of Castilla y León and FEDER fund

Generative AI in Clinical Trials: Revolutionizing Design, Analysis and Prediction in Medical Research

[EN]In the context of clinical trials, the implementation and adaptation of emerging technologies is essential to accelerate and improve medical research. This article explores the impact and applications of generative artificial intelligence (AI) at various stages of clinical trials. Its potential benefits are highlighted, such as optimizing patient selection, innovation in protocol design, accurate prediction of outcomes, and deeper analysis of complex data. Furthermore, the importance of combining these advanced tools with human knowledge and experience is highlighted, and the associated ethical and privacy considerations are mentioned. With generative AI

Deep Symbolic Learning Architecture for Variant Calling in NSG

[EN]In the era of genomics, efficient and accurate analysis of genomic sequences is essential. Next-generation sequencing (NGS) technology has revolutionised the field of genomics by providing a massive volume of data on an unprecedented scale. One of the critical steps in the analysis of this data is variant calling, where genetic variations are identified from DNA sequences. In this context, we have explored the use of Deep Symbolic Learning (DSL) as an innovative computational approach that combines deep learning with symbolic representations. In this article, we discuss the principles of DSL and its applicability in genomics. We examine the advantages and challenges of its use in the context of variant calling and highlight the importance of meticulous validation. To ensure the quality of the results, it is essential to adopt appropriate validation techniques and specific software tools. We provide a detailed overview of these techniques and tools, with the aim of establishing clear standards for the implementation and validation of DSL algorithms in genomic pipelines. This research highlights the potential of the DSL to improve the accuracy of variant discovery, offering promising prospects for the genomics of the future

Integrating Nextflow and AWS for Large-Scale Genomic Analysis: A Hypothetical Case Study

[EN]This article explores the innovative combination of Nextflow and Amazon Web Services (AWS) to address the challenges inherent in large-scale genomic analysis. Focusing on a hypothetical case called "The Pacific Genome Atlas", it illustrates how a research organization could approach the sequencing and analysis of 10,000 genomes. Although the "Pacific Genome Atlas" is a fictional example used for illustrative purposes only, it highlights the real challenges associated with large genomic projects, such as handling huge volumes of data and the need for intensive computational analysis. Through the integration of Nextflow, a workflow management tool, with the AWS cloud infrastructure, we demonstrate how these challenges can be overcome, offering scalable, flexible and cost-effective solutions for genomic research. The adoption of modern technologies, such as those described in this article, is essential to advance the field of genomics and accelerate scientific discoveries.The present study has been funded by the AIR Genomics project (file number CCTT3/20/SA/0003) through the 2020 call for R&D Projects Oriented towards Excellence and Competitive Improvement of CCTT by the Institute of Business Competitiveness of Castilla y León and FEDER fund