Real-time EMG Control for Hand Exoskeletons

The goal of this project is to develop a system for hand exoskeletons control in real time. Robotic systems are useful for rehabilitation therapies due to their ability to help patients perform repetitive movements. Hand recovery is especially critical because hands are necessary to perform many daily life activities. Exoskeletons developed for hand rehabilitation can benefit from a real-time control system that activates the robotic devices at the same time as the patient is performing a movement. Real-time control of these systems can be achieved using different methods. In this project, electromyographic (EMG) signals from the patient’s forearm are used. The controlled robotic systems are soft hand exoskeletons actuated with Shape-Memory Alloys (SMA) wires. The SMA wires are controlled with a microcontroller. The main objective of these exoskeletons is to help the patient with the movement of grasping an object and releasing it afterwards. Machine learning is used to detect the intention of the patient to grasp or release an object based on the patient’s EMG signals. Once one of these movements is detected, real-time communication with the microcontroller is achieved and the necessary SMA wires are activated. The system is developed in Matlab, and it involves signal acquisition, signal rectification, signal segmentation, feature extraction, dimensionality reduction, signal classification, and communication with the microcontroller. To differentiate between the movements of grasping and releasing an object, three different classifiers will be tested: Artificial Neural Networks (ANN), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Their performance will be compared using a confusion matrix and the best one will be selected for the system’s algorithm. Control is achieved with a time delay of less than 1 second for the action of grasping and of less than 2 seconds for the action of releasing is achieved, almost accomplishing the objective of developing a real-time control system. Several improvements are proposed to decrease this time delay.Ingeniería Biomédic

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

The use of sentiment analysis methods has increased in recent years across a wide range of disciplines. Despite the potential impact of the development of opinions during political elections, few studies have focused on the analysis of sentiment dynamics and their characterization from statistical and mathematical perspectives. In this paper, we apply a set of basic methods to analyze the statistical and temporal dynamics of sentiment analysis on political campaigns and assess their scope and limitations. To this end, we gathered thousands of Twitter messages mentioning political parties and their leaders posted several weeks before and after the 2019 Spanish presidential election. We then followed a twofold analysis strategy: (1) statistical characterization using indices derived from well-known temporal and information metrics and methods –including entropy, mutual information, and the Compounded Aggregated Positivity Index– allowing the estimation of changes in the density function of sentiment data; and (2) feature extraction from nonlinear intrinsic patterns in terms of manifold learning using autoencoders and stochastic embeddings. The results show that both the indices and the manifold features provide an informative characterization of the sentiment dynamics throughout the election period. We found measurable variations in sentiment behavior and polarity across the political parties and their leaders and observed different dynamics depending on the parties’ positions on the political spectrum, their presence at the regional or national levels, and their nationalist or globalist aspirations

On the Statistical and Temporal Dynamics of Sentiment Analysis

Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at ..

IPSC‐based modeling of THD recapitulates disease phenotypes and reveals neuronal malformation

Tyrosine hydroxylase deficiency (THD) is a rare genetic disorder leading to dopaminergic depletion and early-onset Parkinsonism. Affected children present with either a severe form that does not respond to L-Dopa treatment (THD-B) or a milder L-Dopa responsive form (THD-A). We generated induced pluripotent stem cells (iPSCs) from THD patients that were differentiated into dopaminergic neurons (DAn) and compared with control-DAn from healthy individuals and gene-corrected isogenic controls. Consistent with patients, THD iPSC-DAn displayed lower levels of DA metabolites and reduced TH expression, when compared to controls. Moreover, THD iPSC-DAn showed abnormal morphology, including reduced total neurite length and neurite arborization defects, which were not evident in DAn differentiated from control-iPSC. Treatment of THD-iPSC-DAn with L-Dopa rescued the neuronal defects and disease phenotype only in THDA-DAn. Interestingly, L-Dopa treatment at the stage of neuronal precursors could prevent the alterations in THDB-iPSC-DAn, thus suggesting the existence of a critical developmental window in THD. Our iPSC-based model recapitulates THD disease phenotypes and response to treatment, representing a promising tool for investigating pathogenic mechanisms, drug screening, and personalized management

Sustainable Production in the Food Industry Volume 6: Sustainable Primary Production (6): 128-149 (2021)

22 Páginas.-- 2 FigurasChallenges in the food industry relate not only to demographic pressure and environment protection, but also to new consumer demands and specific health requirements. Multidisciplinary studies on raw materials, waste valorisation, efficient green technologies, new products and biodegradable smart packaging are needed. Real-time process control, quality, safety and authenticity will rely on powerful proces analytical technology, multi-sensors and advanced computing and digitalisation systemsPeer reviewe

A Novel Circulating MicroRNA for the Detection of Acute Myocarditis.

The diagnosis of acute myocarditis typically requires either endomyocardial biopsy (which is invasive) or cardiovascular magnetic resonance imaging (which is not universally available). Additional approaches to diagnosis are desirable. We sought to identify a novel microRNA for the diagnosis of acute myocarditis. To identify a microRNA specific for myocarditis, we performed microRNA microarray analyses and quantitative polymerase-chain-reaction (qPCR) assays in sorted CD4+ T cells and type 17 helper T (Th17) cells after inducing experimental autoimmune myocarditis or myocardial infarction in mice. We also performed qPCR in samples from coxsackievirus-induced myocarditis in mice. We then identified the human homologue for this microRNA and compared its expression in plasma obtained from patients with acute myocarditis with the expression in various controls. We confirmed that Th17 cells, which are characterized by the production of interleukin-17, are a characteristic feature of myocardial injury in the acute phase of myocarditis. The microRNA mmu-miR-721 was synthesized by Th17 cells and was present in the plasma of mice with acute autoimmune or viral myocarditis but not in those with acute myocardial infarction. The human homologue, designated hsa-miR-Chr8:96, was identified in four independent cohorts of patients with myocarditis. The area under the receiver-operating-characteristic curve for this novel microRNA for distinguishing patients with acute myocarditis from those with myocardial infarction was 0.927 (95% confidence interval, 0.879 to 0.975). The microRNA retained its diagnostic value in models after adjustment for age, sex, ejection fraction, and serum troponin level. After identifying a novel microRNA in mice and humans with myocarditis, we found that the human homologue (hsa-miR-Chr8:96) could be used to distinguish patients with myocarditis from those with myocardial infarction. (Funded by the Spanish Ministry of Science and Innovation and others.).Supported by a grant (PI19/00545, to Dr. Martín) from the Ministry of Science and Innovation through the Carlos III Institute of Health–Fondo de Investigación Sanitaria; by a grant from the Biomedical Research Networking Center on Cardiovascular Diseases (to Drs. Martín, Sánchez-Madrid, and Ibáñez); by grants (S2017/BMD-3671-INFLAMUNE-CM, to Drs. Martín and Sánchez-Madrid; and S2017/BMD-3867-RENIM-CM, to Dr. Ibáñez) from Comunidad de Madrid; by a grant (20152330 31, to Drs. Martín, Sánchez-Madrid, and Alfonso) from Fundació La Marató de TV3; by grants (ERC-2011-AdG 294340-GENTRIS, to Dr. Sánchez-Madrid; and ERC-2018-CoG 819775-MATRIX, to Dr. Ibáñez) from the European Research Council; by grants (SAF2017-82886R, to Dr. Sánchez-Madrid; RETOS2019-107332RB-I00, to Dr. Ibáñez; and SAF2017-90604-REDT-NurCaMeIn and RTI2018-095928-BI00, to Dr. Ricote) from the Ministry of Science and Innovation; by Fondo Europeo de Desarrollo Regional (FEDER); and by a 2016 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation to Dr. Martín. The National Center for Cardiovascular Research (CNIC) is supported by the Carlos III Institute of Health, the Ministry of Science and Innovation, the Pro CNIC Foundation, and by a Severo Ochoa Center of Excellence grant (SEV-2015-0505). Mr. Blanco-Domínguez is supported by a grant (FPU16/02780) from the Formación de Profesorado Universitario program of the Spanish Ministry of Education, Culture, and Sports. Ms. Linillos-Pradillo is supported by a fellowship (PEJD-2016/BMD-2789) from Fondo de Garantía de Empleo Juvenil de Comunidad de Madrid. Dr. Relaño is supported by a grant (BES-2015-072625) from Contratos Predoctorales Severo Ochoa para la Formación de Doctores of the Ministry of Economy and Competitiveness. Dr. Alonso-Herranz is supported by a fellowship from La Caixa–CNIC. Dr. Caforio is supported by Budget Integrato per la Ricerca dei Dipartimenti BIRD-2019 from Università di Padova. Dr. Das is supported by grants (UG3 TR002878 and R35 HL150807) from the National Institutes of Health and the American Heart Association through its Strategically Focused Research Networks.S

COVID-19 outbreaks in a transmission control scenario: challenges posed by social and leisure activities, and for workers in vulnerable conditions, Spain, early summer 2020

Severe acute respiratory syndrome coronavirus 2 community-wide transmission declined in Spain by early May 2020, being replaced by outbreaks and sporadic cases. From mid-June to 2 August, excluding single household outbreaks, 673 outbreaks were notified nationally, 551 active (>6,200 cases) at the time. More than half of these outbreaks and cases coincided with: (i) social (family/friends’ gatherings or leisure venues) and (ii) occupational (mainly involving workers in vulnerable conditions) settings. Control measures were accordingly applied

Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study

Background In spite of the global implementation of standardized surgical safety checklists and evidence-based practices, general surgery remains associated with a high residual risk of preventable perioperative complications and adverse events. This study was designed to validate the hypothesis that a new “Trigger Tool” represents a sensitive predictor of adverse events in general surgery. Methods An observational multicenter validation study was performed among 31 hospitals in Spain. The previously described “Trigger Tool” based on 40 specific triggers was applied to validate the predictive power of predicting adverse events in the perioperative care of surgical patients. A prediction model was used by means of a binary logistic regression analysis. Results The prevalence of adverse events among a total of 1,132 surgical cases included in this study was 31.53%. The “Trigger Tool” had a sensitivity and specificity of 86.27% and 79.55% respectively for predicting these adverse events. A total of 12 selected triggers of overall 40 triggers were identified for optimizing the predictive power of the “Trigger Tool”. Conclusions The “Trigger Tool” has a high predictive capacity for predicting adverse events in surgical procedures. We recommend a revision of the original 40 triggers to 12 selected triggers to optimize the predictive power of this tool, which will have to be validated in future studies

Innocampus Explora: Nuevas formas de comunicar ciencia

[EN] Innocampus Explora aims to show the students of the Burjassot-Paterna campus of the Universitat de València how the different scientific degrees are interrelated. To do this we propose activities in which students and teachers work together to cover the interdisciplinary nature of science, both in everyday and professional issues. Throughout this course the activities developed relate to new ways to communicate science. With the development of this project we contribute to a transversal quality education for all the participating students.[ES] Innocampus Explora tiene por objetivo mostrar a los estudiantes del campus de Burjassot-Paterna de la Universitat de València cómo los diferentes grados científicos están interrelacionados. Para ello proponemos actividades en las que estudiantes y profesores trabajen conjuntamente para abarcar la interdisciplinariedad de la ciencia, tanto en temas cotidianos como profesionales. A lo largo de este curso las actividades desarrolladas se relacionan con las nuevas formas de comunicar ciencia. Con el desarrollo de este proyecto contribuimos a una formación transversal de calidad para todos los estudiantes participantes.Moros Gregorio, J.; Rodrigo Martínez, P.; Torres Piedras, C.; Montoya Martínez, L.; Peña Peña, J.; Pla Díaz, M.; Galarza Jiménez, P.... (2019). Innocampus Explora: Nuevas formas de comunicar ciencia. En IN-RED 2019. V Congreso de Innovación Educativa y Docencia en Red. Editorial Universitat Politècnica de València. 814-823. https://doi.org/10.4995/INRED2019.2019.10449OCS81482

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry

Aim To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). Methods Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. Results As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p = 0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.211, 95%CI 0.067-0.667, p = 0.008). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). Conclusions Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality