La edición digital de textos literarios: planteamientos y perspectivas de futuro

La edición digital de textos literarios se encuentra sumida en un proceso de evolución constante con amplísimas perspectivas de desarrollo gracias a las soluciones tecnológicas disponibles. Sin embargo, las posibilidades teóricas de la actual sociedad de la información contrastan ampliamente con el panorama real de edición que nos encontramos en España, donde la llamada “brecha digital” se hace patente. Este trabajo se ocupa, en primer lugar, de evaluar el panorama internacional de recursos, herramientas y proyectos en el ámbito de la edición digital de textos literarios para después analizar sus claves de evolución y otros aspectos relevantes, como el acceso a los contenidos en abierto, el uso de interfaces amigables y accesibles, el cumplimiento de los estándares, el trabajo interdisciplinar y la interoperabilidad. El objetivo fi nal de estos análisis y refl exiones es ofrecer nuevas pautas y perspectivas de futuro que trasciendan más allá del texto como mero objeto de estudio.Digital scholarly edition is conditioned by a continuous and challenging process of change due to the availability of many different technical solutions. However, those theoretical possibilities face with the real situation of editions in Spain, where the so-called “digital gap” grows constantly. This paper deals with the analysis of the international panorama of resources, tools and projects related to digital scholarly editions. Its objective is to study their ways of evolution and other relevant aspects, as open access, friendly interfaces, accessibility, standardization level, interdisciplinary work and interoperability. The fi nal aim of these analysis and refl ections is to offer new guidelines and perspectives for the future of the text, taking it further than a simple study object

The Diachronic Spanish Sonnet Corpus (DISCO): TEI and Linked Open Data Encoding, Data Distribution and Metrical Findings

We present a corpus covering 4094 sonnets in Spanish by 1204 authors, from the 15th to the 19th centuries, extracted from HTML sources. The corpus was encoded in TEI. Author metadata not available in a standardized format in the sources were systematically retrieved or inferred from the sources and added to the corpus, e.g. author gender or VIAF IDs. RDFa was used to render TEI semantics in the Linked Open Data paradigm. Scansion was annotated automatically with the ADSO Scansion System. Enjambment was annotated automatically with our enjambment detection tool (ANJA). Stanza types were also annotated. The corpus covers both canonical and non-canonical authors, from Europe and Latin America. The range of authors and periods, the use of both TEI and RDFa for interoperability, and the combination of metrical and enjambment annotations goes beyond previously available digital resources for the study of poetry in Spanish. This corpus is a contribution within an area where digital resources are scarce. We also present some literary analysis results that illustrate the type of research questions that can be answered with the corpus

Estudio para la mejora de la calidad del vino albariño

Premio de Investigación, Real Academia Galega de Ciencias, convocatoria 2009.[EN]Twenty-two clones from Albariño variety (Vitis vinifera L.), from an initial collection of 115 clones, were selected on the basis of their ampelographic, molecular and sanitary characteristics. These selected clones were studied from the agronomic and oenological point of view, and were also quantified for their levels of susceptibility to Powdery Mildew, Oidium and Botrytis. An ecotypic yeast was selected, its use has been patented and it is being exploited. Musts obtained from the previously selected Albariño clones were fermented with this yeast, essentially by increasing the content in volatile substances of interest (terpens: linalool and geraniol; norisoprenoids: α-ionone and β- damascenone), leading to wines with improved fermentative dynamic and sensorial attributes.[ES]En base a características ampelográficas, moleculares y sanitarias, se seleccionaron 22 clones de la variedad Albariño (Vitis vinifera L.), partiendo de 115 iniciales. Sobre los clones seleccionados se ha llevado a cabo un estudio agronómico y enológico, así como la cuantificación de los niveles de susceptibilidad a Mildiu, Oídio y Botrytis. Se ha seleccionado una levadura ecotípica, cuyo uso ha sido patentado y se encuentra en explotación. Con ella se fermentaron los mostos obtenidos a partir de los clones de Albariño previamente seleccionados, dando lugar a vinos con una dinámica fermentativa xPremio de Investigación, Real Academia Galega de Ciencias, convocatoria 2009 y unos atributos sensoriales mejorados, fundamentalmente en base al aumento del contenido en sustancias volátiles de interés (terpenos: linalool y geraniol; norisoprenoides: α-ionona y β- damascenona).La actividad realizada ha sido financiada, además de por la Bodega Terras Gauda S.A., por la Xunta de Galicia (PGIDIT04TAL035E), y por el propio CSIC (PIE 2004 7 0E 214).Peer reviewe

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

[EN] Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovacion, Generalitat Valenciana.Albiol Colomer, A.; Albiol, F.; Paredes Palacios, R.; Plasencia-Martínez, JM.; Blanco Barrio, A.; García Santos, JM.; Tortajada, S.... (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights into Imaging. 13(1):1-12. https://doi.org/10.1186/s13244-022-01250-311213

Diagnostico del Impacto del Palangre de Fondo en los Hábitats Bentónicos en los LICs de la RN20000

En prens

A genome-wide association study follow-up suggests a possible role for PPARG in systemic sclerosis susceptibility

Introduction: A recent genome-wide association study (GWAS) comprising a French cohort of systemic sclerosis (SSc) reported several non-HLA single-nucleotide polymorphisms (SNPs) showing a nominal association in the discovery phase. We aimed to identify previously overlooked susceptibility variants by using a follow-up strategy.<p></p> Methods: Sixty-six non-HLA SNPs showing a P value <10-4 in the discovery phase of the French SSc GWAS were analyzed in the first step of this study, performing a meta-analysis that combined data from the two published SSc GWASs. A total of 2,921 SSc patients and 6,963 healthy controls were included in this first phase. Two SNPs, PPARG rs310746 and CHRNA9 rs6832151, were selected for genotyping in the replication cohort (1,068 SSc patients and 6,762 healthy controls) based on the results of the first step. Genotyping was performed by using TaqMan SNP genotyping assays. Results: We observed nominal associations for both PPARG rs310746 (PMH = 1.90 × 10-6, OR, 1.28) and CHRNA9 rs6832151 (PMH = 4.30 × 10-6, OR, 1.17) genetic variants with SSc in the first step of our study. In the replication phase, we observed a trend of association for PPARG rs310746 (P value = 0.066; OR, 1.17). The combined overall Mantel-Haenszel meta-analysis of all the cohorts included in the present study revealed that PPARG rs310746 remained associated with SSc with a nominal non-genome-wide significant P value (PMH = 5.00 × 10-7; OR, 1.25). No evidence of association was observed for CHRNA9 rs6832151 either in the replication phase or in the overall pooled analysis.<p></p> Conclusion: Our results suggest a role of PPARG gene in the development of SSc

XLI Jornadas de teatro clásico

Jornadas organizadas por el Instituto Almagro de teatro clásico, dedicadas a la figura de Agustín Moreto

Transient inhibition of the JAK/STAT pathway prevents B-ALL development in genetically predisposed mice

Preventing development of childhood B-cell acute lymphoblastic leukemia (B-ALL), a disease with devastating effects, is a longstanding and unsolved challenge. Heterozygous germline alterations in the PAX5 gene can lead to B-ALL upon accumulation of secondary mutations affecting the JAK/STAT signaling pathway. Preclinical studies have shown that this malignant transformation occurs only under immune stress such as exposure to infectious pathogens. Here we show in Pax5+/− mice that transient, early-life administration of clinically relevant doses of ruxolitinib, a JAK1/2 inhibitor, significantly mitigates the risk of B-ALL following exposure to infection; 1 of 29 animals treated with ruxolitinib developed B-ALL versus 8 of 34 untreated mice. Ruxolitinib treatment preferentially targeted Pax5+/− versus wild-type B-cell progenitors and exerted unique effects on the Pax5+/− B-cell progenitor transcriptional program. These findings provide the first in vivo evidence for a potential strategy to prevent B-ALL development.C. Cobaleda and C. Vicente-Dueñas labs are members of the EU COST Action LEGEND (CA16223). Research in C. Vicente-Dueñas group has been funded by Instituto de Salud Carlos III through the project " PI17/00167 and by a “Miguel Servet Grant” [CPII19/00024 - AES 2017-2020; co-funded by European Regional Development Fund (ERDF)/European Social Fund (ESF) "A way to make Europe"/"Investing in your future"]. J.J. Yang and K.E. Nichols receive funding from the American Lebanese Syrian Associated Charities (ALSAC) and R01CA241452 from the NCI. Research in ISG group is partially supported by FEDER and by SAF2015-64420-R MINECO/FEDER, UE, RTI2018-093314-B-I00 MCIU/AEI/FEDER, UE, 9659122185-122185-4-21 MCIU/AEI/FEDER, UE, by Junta de Castilla y León (UIC-017, CSI001U16, CSI234P18, and CSI144P20). M. Ramírez-Orellana and I. Sánchez-García have been supported by the Fundacion Unoentrecienmil (CUNINA project). C. Cobaleda, M. Ramírez-Orellana, and I. Sánchez-García have been supported by the Fundación Científica de la Asociación Española contra el Cáncer (PRYCO211305SANC). A. Casado-García (CSI067-18) and M. Isidro-Hernández (CSI021-19) are supported by FSE-Conserjería de Educación de la Junta de Castilla y León 2019 and 2020 (ESF, European Social Fund) fellowship, respectively. J. Raboso-Gallego is supported by a scholarship from University of Salamanca co-financed by Banco Santander and ESF. S. Alemán-Arteaga is supported by an Ayuda para Contratos predoctorales para la formación de doctores (PRE2019-088887)

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovación, Generalitat Valenciana.Peer reviewe