ViTS: Video tagging system from massive web multimedia collections

The popularization of multimedia content on the Web has arised the need to automatically understand, index and retrieve it. In this paper we present ViTS, an automatic Video Tagging System which learns from videos, their web context and comments shared on social networks. ViTS analyses massive multimedia collections by Internet crawling, and maintains a knowledge base that updates in real time with no need of human supervision. As a result, each video is indexed with a rich set of labels and linked with other related contents. ViTS is an industrial product under exploitation with a vocabulary of over 2.5M concepts, capable of indexing more than 150k videos per month. We compare the quality and completeness of our tags with respect to the ones in the YouTube-8M dataset, and we show how ViTS enhances the semantic annotation of the videos with a larger number of labels (10.04 tags/video), with an accuracy of 80,87%.Postprint (published version

VLX-Stories: a semantically linked event platform for media publishers

In the recent years, video sharing in social media from different video recording devices has resulted in a exponential growth of videos on the Internet. Such video data is continuously increasing with daily recordings related to a wide number of topics. In this context, video understanding has become a critical problem to address. Video search and indexation benefits from the use of keyword tags related to the video content, but most of the shared video content does not contain these tags. Although the use of deep learning has become essential for image analysis in several areas, video domain is still a relatively unexplored field for these type of methods. On the other hand knowledge graphs as Freebase or WordNet store high quantities of information about the word and relations that can be used to disambiguate concepts and relate them through contextual information In this research project we search to explore and improve the understanding of video content through the use of automatic tagging models based on Machine Learning and Deep Learning techniques, improved by the use of knowledge bases.Peer ReviewedPostprint (published version

Enhancing online knowledge graph population with semantic knowledge

Knowledge Graphs (KG) are becoming essential to organize, represent and store the world’s knowledge, but they still rely heavily on humanly-curated structured data. Information Extraction (IE) tasks, like disambiguating entities and relations from unstructured text, are key to automate KG population. However, Natural Language Processing (NLP) methods alone can not guarantee the validity of the facts extracted and may introduce erroneous information into the KG. This work presents an end-to-end system that combines Semantic Knowledge and Validation techniques with NLP methods, to provide KG population of novel facts from clustered news events. The contributions of this paper are two-fold: First, we present a novel method for including entity-type knowledge into a Relation Extraction model, improving F1-Score over the baseline with TACRED and TypeRE datasets. Second, we increase the precision by adding data validation on top of the Relation Extraction method. These two contributions are combined in an industrial pipeline for automatic KG population over aggregated news, demonstrating increased data validity when performing online learning from unstructured web data. Finally, the TypeRE and AggregatedNewsRE datasets build to benchmark these results are also published to foster future research in this field.This work was partially supported by the Government of Catalonia under the industrial doctorate 2017 DI 011.Peer ReviewedPostprint (author's final draft

Exposing and Overcoming Limitations of Clinical Laboratory Tests in COVID-19 by Adding Immunological Parameters; A Retrospective Cohort Analysis and Pilot Study

BackgroundTwo years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited. ObjectivesTo measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively. Findings1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests. ConclusionsLaboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible

Development and validation of a clinical score to estimate progression to severe or critical state in Covid-19 pneumonia hospitalized patients

The prognosis of a patient with Covid-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with Covid-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, analytical, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1,152 patients presented with Covid-19 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 hours of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤5%, 6-25%, and >25% exhibited disease progression, respectively. A simple risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.Carlos III Health Institute, Spain, Ministry of Economy and Competitiveness (SPAIN) and the European Regional Development Fund (FEDER)Instituto de Salud Carlos II

Mitigación con Sistemas Silvopastoriles en Latinoamérica: Aportes para la incorporación en los sistemas de Medición Reporte y Verificación bajo la CMNUCC

En Latinoamérica el 46% de las emisiones de GEI proviene del cambio de usos de la tierra y el 20% de la agricultura, en donde el 58% y el 70% de las emisiones son debidas a la ganadería. El continuo crecimiento de este sector (+32% previsto al 2050) ha impulsado la expansión de la frontera agropecuaria en los bosques, generando múltiples impactos ambientales entre los cuales se encuentra la emisión de Gases Efecto Invernadero (GEI). Sin embargo, el sector tiene un alto potencial de mitigación reconocido por políticas, estrategias y programas de mitigación nacionales como las Contribuciones Nacionalmente Determinadas (NDC) y de desarrollo sectorial como las Acciones de Mitigación nacionalmente Apropiadas (NAMA). Entre estas acciones se incluye la implementación de sistemas silvopastoriles, cuya medición monitoreo y reporte a escala nacional presenta un estado de avance muy limitado, dejando su aporte a la mitigación invisible. A través de un Grupo Técnico de Trabajo ad hoc se han analizado el avance de los países de la región en la incorporación de los sistemas silvopastoriles en los sistemas nacionales de Medición/Monitoreo, Reporte y Verificación (MRV) de los Inventarios Nacionales de Gases Efecto Invernadero, y los requerimientos a cumplir para esto, generando una hoja de ruta a corto-medio plazo así como unas orientaciones técnicas para reducir la brecha existente

Mitigación con Sistemas Silvopastoriles en Latinoamérica: Aportes para la incorporación en los sistemas de Medición Reporte y Verificación bajo la CMNUCC

En Latinoamérica el 46% de las emisiones de GEI proviene del cambio de usos de la tierra y el 20% de la agricultura, en donde el 58% y el 70% de las emisiones son debidas a la ganadería. El continuo crecimiento de este sector (+32% previsto al 2050) ha impulsado la expansión de la frontera agropecuaria en los bosques, generando múltiples impactos ambientales entre los cuales se encuentra la emisión de Gases Efecto Invernadero (GEI). Sin embargo, el sector tiene un alto potencial de mitigación reconocido por políticas, estrategias y programas de mitigación nacionales como las Contribuciones Nacionalmente Determinadas (NDC) y de desarrollo sectorial como las Acciones de Mitigación nacionalmente Apropiadas (NAMA). Entre estas acciones se incluye la implementación de sistemas silvopastoriles, cuya medición monitoreo y reporte a escala nacional presenta un estado de avance muy limitado, dejando su aporte a la mitigación invisible. A través de un Grupo Técnico de Trabajo ad hoc se han analizado el avance de los países de la región en la incorporación de los sistemas silvopastoriles en los sistemas nacionales de Medición/Monitoreo, Reporte y Verificación (MRV) de los Inventarios Nacionales de Gases Efecto Invernadero, y los requerimientos a cumplir para esto, generando una hoja de ruta a corto-medio plazo así como unas orientaciones técnicas para reducir la brecha existente

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2