Refrigerador doméstico con efecto Peltier, acumuladores térmicos y termosifones evaporativos

Referencia OEPM: P9801016.-- Fecha de solicitud: 14/05/1998.-- Titular: Consejo Superior de Investigaciones Científicas (CSIC).La patente se refiere a instalaciones frigoríficas que funcionan gracias a la combinación de elementos productores de efecto Peltier y termosifones con cambio de fase líquido-vapor. Básicamente consta de: 1) Recinto de refrigeración. 2) Disipador de calor. 3) Un circuito cerrado y estanco conteniendo un fluido, que hierve o se evapora. 4) Elementos de efecto Peltier (primera etapa). 5) Termosifón que transporta el calor, a las caras frías de las pastillas de efecto Peltier de la primera etapa. 6) Termosifón que intercambia el calor con el anterior y transporta éste de las caras calientes de las pastillas Peltier de la segunda etapa. 7) Elementos de efecto Peltier (segunda etapa). 8) Termosifón que transporta el calor, del recinto a baja temperatura, a las caras frías de las placas Peltier de la segunda etapa.Peer reviewe

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

Neuropsychology of perception and facial expression of emotions: studies with children and non human primates

Este estudio revisa diferentes investigaciones sobre la neuropsicología de la percepción y la expresión facial de emociones en niños y en primates no humanos. Se parte de las teorías neodarwinianas sobre el origen de la emoción, que presuponen la existencia de circuitos cerebrales específicos para la expresión y comprensión del afecto y consideran que estos comportamientos son innatos y filogenéticamente determinados . A partir de la literatura examinada, las conclusiones principales que pueden extraerse son las siguientes: (1) A lo largo del primer año de vida los niños manifiestan capacidades básicas para reconocer emociones. Entre otros correlatos neurobiológicos, se ha observado que este proceso se corresponde con una mayor actividad del hemisferio cerebral derecho, frente al izquierdo. (2) En primates no humanos, y desde distintas aproximaciones experimentales, se ha encontrado un patrón similar al de nuestra especie en tareas de reconocimiento y discriminación de expresiones faciales de emociones.(3) Los lactantes expresan emociones con acciones musculares similares a las descritas en adultos. La mayoría de los autores señalan que el hemisferio cerebral derecho muestra una mayor implicación que el izquierdo, también en el caso de la expresión emocional y para todas las emociones estudiadas. No obstante desde algunas investigaciones se concluye que el hemisferio izquierdo es el dominante en el caso de la expresión de emociones positivas y el hemisferio derecho sólo en las negativas. (4) Los estudios sobre expresión facial en primates no humanos indican que existe una asimetría facial que se manifiesta en una mayor intensidad de la expresión en la mitad izquierda de la cara y, de manera correspondiente, en una mayor implicación del hemisferio cerebral derecho. Se concluye esta revisión considerando que la existencia de semejanzas anatómicas y funcionales entre adultos, niños y primates no humanos justifica el interés por los estudios de ontogenia y filogenia para conocer mejor las bases neurobiológicas que subyacen a la conducta emocionalThis study reviews different researches on the neuropsychology of facial perception and expression of emotions in children and non-human primates. It parts from neodarwinian theories on the origin of the emotion that take into account the existence of specific cerebral circuits for the expression and understanding of affect, and consider such behaviours as innate and phylogenetically determined. From literature reviewed, the main conclusions that can be extracted are the following ones: (1) During the first year of life infants show basic abilities to recognize emotions. Among other neurobiological correlates, it has been observed that this process corresponds with a greater activity of the right hemisphere, as opposed to the left one. (2) From different experimental approaches, human and non-human primates show a similar pattern in the recognition and discrimination of facial expressions of emotions. (3) Infants express emotions with similar muscular actions to those described in adults. Most of the authors also point out a greater involvement of the right hemisphere than the left one, for all of the emotional expressions. However, some studies report that right hemisphere is dominant only for the negative expressions, while the left hemisphere is for the positive ones. (4) Studies on facial expression in human and non-human primates show an asymmetry in the production of emotional responses with a greater involvement of the left side of the face (right cerebral hemisphere). This review concludes that the existence of anatomical and functional similarities in non-human primates, children and adults justifies the interest by the ontogenetic and phylogenetic studies in order to understand the neurobiological bases underlying emotional behaviou

Multiagent System For Predicting The Co2 Exchange In The North Atlantic Ocean

This paper presents a multiagent system developed to predict the behaviour of the Atlantic Ocean in relation to the sinks/sources of CO2. The heart of the multiagent system is an intelligent agent capable of automatically making predictions about the flux of CO2 in the North Atlantic ocean. The multiagent system has been tested in simulation conditions and this work presents the preliminary obtained results

Evaluation of points of improvement in NGS data analysis

[EN]DNA sequencing is a fundamental technique in molecular biology that allows the exact sequence of nucleotides in a DNA sample to be read. Over the past decades, DNA sequencing has seen significant advances, evolving from manual and laborious techniques to modern high-throughput techniques. Despite these advances, interpretation and analysis of sequencing data continue to present challenges. Artificial Intelligence (AI), and in particular machine learning, has emerged as an essential tool to address these challenges. The application of AI in the sequencing pipeline refers to the use of algorithms and models to automate, optimize and improve the precision of the sequencing process and its subsequent analysis. The Sanger sequencing method, introduced in the 1970s, was one of the first to be widely used. Although effective, this method is slow and is not suitable for sequencing large amounts of DNA, such as entire genomes. With the arrival of next generation sequencing (NGS) in the 21st century, greater speed and efficiency in obtaining genomic data has been achieved. However, the exponential increase in the amount of data produced has created a bottleneck in its analysis and interpretation

Application of hybrid algorithms and Explainable Artificial Intelligence ingenomic sequencing

[EN]DNA sequencing is one of the fields that has advanced the most in recent years within clinical genetics and human biology. However, the large amount of data generated through next generation sequencing (NGS) techniques requires advanced data analysis processes that are sometimes complex and beyond the capabilities of clinical staff. Therefore, this work aims to shed light on the possibilities of applying hybrid algorithms and explainable artificial intelligence (XAI) to data obtained through NGS. The suitability of each architecture will be evaluated phase by phase in order to offer final recommendations that allow implementation in clinical sequencing workflow

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

File formats used in next generation sequencing: A literature review

[EN]Next-generation sequencing (NGS) has revolutionized the field of genomics, allowing a detailed and precise look at DNA. As this technology advanced, the need arose for standardized file formats to represent, analyze and store the vast data sets produced. In this article, we review the key file formats used in NGS: FASTA, FASTQ, BED, GFF, and VCF. The FASTA format, one of the oldest, provides a basic representation of genomic and protein sequences, identifiable by unique headers. FASTQ is essential for NGS, as it stores both the sequence and the associated quality information. BED provides a tabular representation of genomic loci, while GFF details the localization and structure of genomic features in reference sequences. Finally, VCF has emerged as the predominant standard for documenting genetic variants, from simple SNPs to complex structural variants. The adoption and adaptation of these formats have been fundamental for progress in bioinformatics and genomics. They provide a foundation on which to build sophisticated analyses, from gene discovery and function prediction to the identification of disease-associated variants. With a clear understanding of these formats, researchers and practitioners are better equipped to harness the power and potential of next-generation sequencing.This 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 fun