Merging datasets for emotion analysis

Context. Applying sentiment analysis is in general a laborious task. Furthermore, if we add the task of getting a good quality dataset with balanced distribution and enough samples, the job becomes more complicated. Objective. We want to find out whether merging compatible datasets improves emotion analysis based on machine learning (ML) techniques, compared to the original, individual datasets. Method. We obtained two datasets with Covid-19-related tweets written in Spanish, and then built from them two new datasets combining the original ones with different consolidation of balance. We analyzed the results according to precision, recall, F1-score and accuracy. Results. The results obtained show that merging two datasets can improve the performance of ML models, particularly the F1-score, when the merging process follows a strategy that optimizes the balance of the resulting dataset. Conclusions. Merging two datasets can improve the performance of ML models for emotion analysis, whilst saving resources for labeling training data. This might be especially useful for several software engineering activities that leverage on ML-based emotion analysis techniques.This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB.Peer ReviewedPostprint (author's final draft

Applying transfer learning to sentiment analysis in social media

Context: Sentiment analysis is an NLP technique that can be used to automatically obtain the sentiment of a crowd of end-users regarding a software application. However, applying sentiment analysis is a difficult task, especially considering the need of obtaining enough good quality data for training a Machine Learning (ML) model. To address this challenge, transfer learning can help us save time and get better performance results with a limited amount of data. Objective: In this paper, we aim at identifying to which degree transfer learning improves the results of sentiment analysis of messages shared by end-users in social media. Method: We propose a tool-supported framework able to monitor and analyze the sentiment of tweets with different ML models and settings. Using the proposed framework, we apply transfer learning and conduct a set of experiments with multiple datasets. Results: The performance of different ML models with transfer learning from different datasets are obtained and discussed, showing how different factors affect the results, and discussing how they have to be considered when applying transfer learning.This work has been partially supported by the Spanish project DOGO4ML (contract PID2020-117191RB-I00).Peer ReviewedPostprint (author's final draft

Arxiu de la ciutat a la nau 8 de Can Batlló

Tercer premiPostprint (author's final draft

Prognostic value of the Stanniocalcin-2/PAPP-A/IGFBP-4 axis in ST-segment elevation myocardial infarction

Altres ajuts: Fundació La MARATÓ de TV3 (201502 and 201516)The aim of the present study was to evaluate the prognostic value of the Stanniocalcin-2/PAPP-A/IGFBP-4 axis in patients with ST-segment elevation myocardial infarction (STEMI). Observational cohort study performed in 1085 consecutive STEMI patients treated with early reperfusion between February 2011 and August 2014. Stanniocalcin-2, PAPP-A, and IGFBP-4 were measured using state-of-the art immunoassays. The primary outcome was the composite endpoint of all-cause mortality and readmission due to heart failure (HF). Median follow-up was 3.3 years (IQR 1.0-3.7), during which 176 patients (16.2%) presented a composite endpoint. Multivariable cox regression analysis revealed that Stanniocalcin-2 (HR 2.06; 95% CI 1.13-3.75; p = 0.018), IGFBP-4 (HR 1.73; 95% CI 1.14-2.64; p = 0.010), Killip-Kimball class III-IV (HR 1.40; 95% CI 1.13-1.74; p = 0.002), NT-ProBNP (HR 1.21; 95% CI 1.07-1.37; p = 0.002), age (HR 1.06; 95% CI 1.04-1.08; p < 0.001) and left ventricular ejection fraction (HR 0.97; 95% CI 0.95-0.98; p < 0.001) were independent predictors of the composite endpoint. A model containing Stanniocalcin-2 and IGFBP-4 on top of clinical variables significantly improved C-index discrimination (p = 0.036). Stanniocalcin-2 was also identified as independent predictor of all-cause mortality (HR 2.23; 95% CI 1.16-4.29; p = 0.017) and readmission due to HF (HR 3.42; 95% CI 1.22-9.60; p = 0.020). In STEMI patients, Stanniocalcin-2 and IGFBP-4 emerged as independent predictors of all-cause death and readmission due to HF. The Stanniocalcin-2/PAPP-A/IGFBP-4 axis exhibits a significant role in STEMI risk stratification

TFG 2014/2015

Amb aquesta publicació, EINA, Centre universitari de Disseny i Art adscrit a la Universitat Autònoma de Barcelona, dóna a conèixer el recull dels Treballs de Fi de Grau presentats durant el curs 2014-2015. Voldríem que un recull com aquest donés una idea més precisa de la tasca que es realitza a EINA per tal de formar nous dissenyadors amb capacitat de respondre professionalment i intel·lectualment a les necessitats i exigències de la nostra societat. El treball formatiu s’orienta a oferir resultats que responguin tant a paràmetres de rigor acadèmic i capacitat d’anàlisi del context com a l’experimentació i la creació de nous llenguatges, tot fomentant el potencial innovador del disseny.Con esta publicación, EINA, Centro universitario de diseño y arte adscrito a la Universidad Autónoma de Barcelona, da a conocer la recopilación de los Trabajos de Fin de Grado presentados durante el curso 2014-2015. Querríamos que una recopilación como ésta diera una idea más precisa del trabajo que se realiza en EINA para formar nuevos diseñadores con capacidad de responder profesional e intelectualmente a las necesidades y exigencias de nuestra sociedad. El trabajo formativo se orienta a ofrecer resultados que respondan tanto a parámetros de rigor académico y capacidad de análisis, como a la experimentación y la creación de nuevos lenguajes, al tiempo que se fomenta el potencial innovador del diseño.With this publication, EINA, University School of Design and Art, affiliated to the Autonomous University of Barcelona, brings to the public eye the Final Degree Projects presented during the 2014-2015 academic year. Our hope is that this volume might offer a more precise idea of the task performed by EINA in training new designers, able to speak both professionally and intellectually to the needs and demands of our society. The educational task is oriented towards results that might respond to the parameters of academic rigour and the capacity for contextual analysis, as well as to considerations of experimentation and the creation of new languages, all the while reinforcing design’s innovative potential

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Desenvolupament d'un robot mòbil terrestre educatiu per a la introducció al ROS

Curs 2015-2016Aquest Treball Final de Master consisteix en el desenvolupament d’un robot mòbil terrestre de baix cost, opensource i openhardware per tal de que estudiants de robòtica el puguin fer servir per introduir-se a ROS, un dels sistemes operatius per a robots més utilitzats actualment. El projecte s’ha dividit en tres parts, repartint-les entre en Juan Pedro López, l’Oriol Orra i en Marc Genevat. La part d’aquest projecte que es presenta en aquesta memòria s’ha centrat en el disseny i fabricació de la plataforma, la modelització del sistema en URDF i la implementació del model en l’entorn de simulació Gazebo. Gràcies a una bona organització, esforç i les noves tecnologies de fabricació, la majoria dels requeriments que es van proposar que complís la plataforma s’han complet i alguns d’ells amb resultats sorprenents. El cost de fabricació ha resultat ser molt més baix de l’esperat i el pes de la plataforma i la seva capacitat de càrrega també han resultat ser molt bons.This Master Final Project consists of the development of a low-cost educational mobile ground robot, open source and open hardware because robotics students can introduce themselves into ROS, one of the most used robotics operation systems. The project is divided in three main parts, distributing them among Juan Pedro López, Oriol Orra and Marc Genevat. This report is focused on the part centralized on the platform design and Building, the URDF System modelization and the model implementation in the simulation environment Gazebo. Thanks to a good organization, effort and the new technologies of fabrication, the most part of requirements that were suggested to be accomplished by the platform have been so and some of them with surprising results. The cost of production has resulted to be much lower of what was expected and the platform’s weight and its payload have also turned out to be good.Director/a: Carlos J. Rosales Gallego