Centennial olive trees as a reservoir of genetic diversity

Background and AimsGenetic characterization and phylogenetic analysis of the oldest trees could be a powerful tool both for germplasm collection and for understanding the earliest origins of clonally propagated fruit crops. The olive tree (Olea europaea L.) is a suitable model to study the origin of cultivars due to its long lifespan, resulting in the existence of both centennial and millennial trees across the Mediterranean Basin.MethodsThe genetic identity and diversity as well as the phylogenetic relationships among the oldest wild and cultivated olives of southern Spain were evaluated by analysing simple sequence repeat markers. Samples from both the canopy and the roots of each tree were analysed to distinguish which trees were self-rooted and which were grafted. The ancient olives were also put into chronological order to infer the antiquity of traditional olive cultivars.Key ResultsOnly 9·6 % out of 104 a priori cultivated ancient genotypes matched current olive cultivars. The percentage of unidentified genotypes was higher among the oldest olives, which could be because they belong to ancient unknown cultivars or because of possible intra-cultivar variability. Comparing the observed patterns of genetic variation made it possible to distinguish which trees were grafted onto putative wild olives.ConclusionsThis study of ancient olives has been fruitful both for germplasm collection and for enlarging our knowledge about olive domestication. The findings suggest that grafting pre-existing wild olives with olive cultivars was linked to the beginnings of olive growing. Additionally, the low number of genotypes identified in current cultivars points out that the ancient olives from southern Spain constitute a priceless reservoir of genetic diversity

The proteome of large or small extracellular vesicles in pig seminal plasma differs, defining sources and biological functions

Seminal plasma contains many morphologically heterogeneous extracellular vesicles (sEVs). These are sequentially released by cells of the testis, epididymis and accessory sex glands, and involved in male and female reproductive processes. This study aimed to in-depth define sEV-subsets isolated by ultrafiltration and size-exclusion chromatography (SEC), decode their proteomic profiles using liquid chromatography-tandem mass spectrometry (LC-MS/MS), and to quantify identified proteins using Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS). The sEV-subsets defined Large (L-EVs) or Small (S-EVs) by their protein concentration, morphology, size distribution and EV-specific protein markers and purity. LC-MS/MS identified a total of 988 proteins, 737 of them quantified by SWATH in S-EVs, L-EVs and non-EVs-enriched samples (18-20 SEC-eluted fractions). The differential expression analysis revealed 197 differentially abundant proteins between both EV-subsets, S-EVs and L-EVs, and 37 and 199 between S-EVs and L-EVs vs non-EVs-enriched samples, respectively. The gene ontology (GO) enrichment analysis of differentially abundant proteins suggested, based on the type of protein detected, that S-EVs could be mainly released through an apocrine blebbing pathway and be involved in modulating the immune environment of the female reproductive tract as well as during sperm-oocyte interaction. In contrast, L-EVs could be released by fusion of multivesicular bodies with the plasma membrane becoming involved in sperm physiological processes, such as capacitation and avoidance of oxidative stress. In conclusion, this study provides a procedure capable of isolating of subsets of EVs from pig seminal plasma with a high degree of purity and shows differences in the proteomic profile between EV-subsets,indicating different sources and biological functions for the sEVs

A Novel, LAT/Lck Double Deficient T Cell Subline J.CaM1.7 for Combined Analysis of Early TCR Signaling

Intracellular signaling through the T cell receptor (TCR) is essential for T cell development and function. Proper TCR signaling requires the sequential activities of Lck and ZAP-70 kinases, which result in the phosphorylation of tyrosine residues located in the CD3 ITAMs and the LAT adaptor, respectively. LAT, linker for the activation of T cells, is a transmembrane adaptor protein that acts as a scaffold coupling the early signals coming from the TCR with downstream signaling pathways leading to cellular responses. The leukemic T cell line Jurkat and its derivative mutants J.CaM1.6 (Lck deficient) and J.CaM2 (LAT deficient) have been widely used to study the first signaling events upon TCR triggering. In this work, we describe the loss of LAT adaptor expression found in a subline of J.CaM1.6 cells and analyze cis-elements responsible for the LAT expression defect. This new cell subline, which we have called J.CaM1.7, can re-express LAT adaptor after Protein Kinase C (PKC) activation, which suggests that activation-induced LAT expression is not affected in this new cell subline. Contrary to J.CaM1.6 cells, re-expression of Lck in J.CaM1.7 cells was not sufficient to recover TCR-associated signals, and both LAT and Lck had to be introduced to recover activatory intracellular signals triggered after CD3 crosslinking. Overall, our work shows that the new LAT negative J.CaM1.7 cell subline could represent a new model to study the functions of the tyrosine kinase Lck and the LAT adaptor in TCR signaling, and their mutual interaction, which seems to constitute an essential early signaling event associated with the TCR/CD3 complex.This research was funded by Consejeria de Salud de Andalucia, Junta de Andalucia (grant PI-0055-2017 to E.A.), and Fundacion Biomedica Cadiz Proyectos INIBICA 2019 (grant LI19/I14NCO15 to E.A. and M.M.A.-E.)

Localización e identificación automática de pólipos mediante una red neuronal convolucional por regiones

Este trabajo expone la metodología llevada a cabo para la aplicación de un modelo Deep Learning con el fin de detectar pólipos de forma automática, así como su posición en videos de colonoscopia. Se plantearon diferentes métodos y diversas técnicas que pudieran aplicarse sobre el conjunto de datos proporcionado por el 2018 Sub-challenge Gastrointestinal Image ANAlysis. Seleccionamos el método Faster Regional Convolutional Neural Network para abarcar el problema planteado. Para la extracción de características empleamos el modelo ResNet50. Aplicamos técnicas de data augmentation para incrementar el conjunto de datos empleado en el entrenamiento del modelo. También aplicamos hard negative mining para reforzar el aprendizaje del background o fondo, reducir el porcentaje de falsos positivos y mejorar el rendimiento.This work exposes the methodology carried out for the application of a Deep Learning model in the context of automatic polyp detection and its location in colonoscopy videos. Different methods were proposed as well as the different techniques that can be applied on the given dataset provided by the 2018 Sub-challenge Gastrointestinal Image ANAlysis. We chose the Faster Regional Convolutional Neural Network method to solve this problem. We used ResNet50 in the first part of this algorithm to extract the main image features. We applied hard negative mining and data augmentation techniques to increase the dataset used in the training of the model. We also used hard negative mining to get a better learning of background, reducing false negatives and improving the performance

Clasificación de tumores en cáncer de mama basado en redes neuronales de convolución

El cáncer de mama es una de las causas más frecuentes de mortalidad en las mujeres. Con la llegada de los sistemas inteligentes, la detección automática de tumores en mamografías se ha convertido en un gran reto y puede jugar un papel crucial para mejorar el diagnóstico médico. En este trabajo, se propone un sistema de diagnóstico asistido por ordenador basado en técnicas de Deep Learning, específicamente en redes neuronales de convolución (CNN). El sistema está dividido en dos partes: en primer lugar, se realiza un preprocesamiento sobre las mamografías extraídas de una base de datos pública; posteriormente, las CNNs extraen características de las imágenes preprocesadas para finalmente clasificarlas en función de los dos tipos de tumores existentes: benignos y malignos. Los resultados de este estudio muestran que el sistema tiene una precisión del 80% en clasificación de tumores.Breast cancer is one of the most frequent causes of mortality in women. With the arrival of the artificial intelligent, the automatic detection of tumors in mammograms has become a big challenge and can play a crucial role in improving medical diagnosis. In this work, a computer-aided diagnosis system based on Deep Learning techniques, specifically in Convolutional Neural Networks (CNN), is proposed. The system is divided into two parts: first, a preprocessing is performed on mammograms taken from a public database; then, the CNN extracts features of the preprocessed images to finally classify them accordingly to the type of tissue. The results of this study show that the system has an accuracy of 80% in the classification

Deep learning as a tool for improving efficiency the of glial tumor diagnosis

La aplicación de técnicas basadas en Inteligencia Artificial como apoyo a la detección y diagnosis de cáncer mediante imagen es una práctica muy extendida hoy día. Además, el reconocimiento por regiones de interés y otros algoritmos constituyen una rama de investigación amplia que mejoran considerablemente la calidad de la clasificación. En este trabajo, se propone como caso de estudio la identificación del tumor glial con Imágenes por Resonancia Magnética de pacientes sanos y enfermos mediante la combinación de algoritmos de Deep Learning de detección de regiones que se basan en la extracción de regiones de interés en la imagen utilizando una red Spatial Pyramid Pooling combinado con la modificación de las imágenes de entrada con el algoritmo Fuzzy c-means. Obteniendo un acierto cercano al del personal sanitario.Nowadays, existing Artificial Intelligent techniques are used as a support for cancer detection and diagnosis. Moreover, regional object interest and other related algorithms have become common place for improving the quality of the classification. Opening a wide field of interest and research. In this work, a deep neural network based on a new pooling strategy and image segmentation (Fuzzy c-means) [1] is proposed for glial tumor in Magnetic Resonance Imaging (MRI) images by using Region of Interests methods as Spatial Pyramid Pooling. The power of SPP-net is the possibility of working with feature maps from images with different sizes, and then subsampling these features to generate a fixed-length set and to implement finally a classification step

Supported Porous Nanostructures Developed by Plasma Processing of Metal Phthalocyanines and Porphyrins

The large area scalable fabrication of supported porous metal and metal oxide nanomaterials is acknowledged as one of the greatest challenges for their eventual implementation in on-device applications. In this work, we will present a comprehensive revision and the latest results regarding the pioneering use of commercially available metal phthalocyanines and porphyrins as solid precursors for the plasma-assisted deposition of porous metal and metal oxide films and three-dimensional nanostructures (hierarchical nanowires and nanotubes). The most advanced features of this method relay on its ample general character from the point of view of the porous material composition and microstructure, mild deposition and processing temperature and energy constrictions and, finally, its straightforward compatibility with the direct deposition of the porous nanomaterials on processable substrates and device-architectures. Thus, taking advantage of the variety in the composition of commercially available metal porphyrins and phthalocyanines, we present the development of metal and metal oxides layers including Pt, CuO, Fe2O3, TiO2, and ZnO with morphologies ranging from nanoparticles to nanocolumnar films. In addition, we combine this method with the fabrication by low-pressure vapor transport of single-crystalline organic nanowires for the formation of hierarchical hybrid organic@metal/metal-oxide and @metal/metal-oxide nanotubes. We carry out a thorough characterization of the films and nanowires using SEM, TEM, FIB 3D, and electron tomography. The latest two techniques are revealed as critical for the elucidation of the inner porosity of the layers.Ministerio de Ciencia, Innovación y Universidades MAT2016-79866-R, PID2019- 110430GB-C21Consejería de Economía y Conocimiento, Junta de Andalucía P18- RT-348

SciencePro Project: Towards Excellence in Bilingual Teaching

This paper provides an account of the progress of SciencePro, an innovative, interdisciplinary and interinstitutional education project, whose ultimate aim is to improve student teachers’ knowledge, abilities and attitudes for teaching Natural Sciences in a foreign language. The team is working on the development of good practices at university level: enhancing profi ciency of cross-curricular competences, including scientific contents, knowledge and foreign language skills oriented to scientific discourse, with a view to developing a more appropriate teaching approach. The Practicum period is needed in order to test out the improvement of these specific professional competences, which should be more suitable to modern bilingual school contexts.El contenido de este artículo da cuenta de los avances de SciencePro, un proyecto de innovación educativa, de carácter interdisciplinar e interinstitucional, cuyo fin último se dirige a la mejora del conocimiento, aptitudes y actitudes de los futuros maestros para la enseñanza de la asignatura de Ciencias Naturales en una lengua extranjera. El equipo trabaja en el desarrollo de buenas prácticas –en el nivel universitario– que den lugar al dominio de competencias transversales –incluyendo contenidos científicos, conocimiento y destrezas en lengua extranjera orientadas hacia el discurso científico y un enfoque metodológico adecuado. El periodo de Prácticum nos sirve para comprobar la mejora de aquellas competencias profesionales específicas que más se adecúan a los contextos escolares bilingües

Highly Anisotropic Organometal Halide Perovskite Nanowalls Grown by Glancing-Angle Deposition

Polarizers are ubiquitous components in current optoelectronic devices as displays or photographic cameras. Yet, control over light polarization is an unsolved challenge, since the main drawback of the existing display technologies is the significant optical losses. In such a context, organometal halide perovskites (OMHP) can play a decisive role given their flexible synthesis with tunable optical properties such as bandgap and photoluminescence, and excellent light emission with a low non-radiative recombination rate. Therefore, along with their outstanding electrical properties have elevated hybrid perovskites as the material of choice in photovoltaics and optoelectronics. Among the different OMHP nanostructures, nanowires and nanorods have lately arisen as key players in the control of light polarization for lighting or detector applications. Herein, the fabrication of highly aligned and anisotropic methylammonium lead iodide perovskite nanowalls by glancing-angle deposition, which is compatible with most substrates, is presented. Their high alignment degree provides the samples with anisotropic optical properties such as light absorption and photoluminescence. Furthermore, their implementation in photovoltaic devices provides them with a polarization-sensitive response. This facile vacuum-based approach embodies a milestone in the development of last-generation polarization-sensitive perovskite-based optoelectronic devices such as lighting appliances or self-powered photodetectors