Changes in severity, mortality, and virus genome among a Spanish cohort of patients hospitalized with SARS-CoV-2

Comparing pandemic waves could aid in understanding the evolution of COVID-19. The objective of the present study was to compare the characteristics and outcomes of patients hospitalized for COVID-19 in different pandemic waves in terms of severity and mortality. We performed an observational retrospective cohort study of 5,220 patients hospitalized with SARS-CoV-2 infection from February to September 2020 in Aragon, Spain. We compared ICU admissions and 30-day mortality, clinical characteristics, and risk factors of the first and second waves of COVID-19. The SARS-CoV-2 genome was also analyzed in 236 samples. Patients in the first wave (n¿=¿2,547) were older (median age 74 years [IQR 60–86] vs. 70 years [53–85]; p¿<¿0.001) and had worse clinical and analytical parameters related to severe COVID-19 than patients in the second wave (n¿=¿2,673). The probability of ICU admission at 30 days was 16% and 10% (p¿<¿0.001) and the cumulative 30-day mortality rates 38% and 32% in the first and second wave, respectively (p¿=¿0.007). Survival differences were observed among patients aged 60 to 80 years. We also found some variability among death risk factors and the viral genome between waves. Therefore, the two analyzed COVID-19 pandemic waves were different in terms of disease severity and mortality

Hybrid (bolted/bonded)joints applied to aeronautic parts: analytical two-dimensional model of a single-lap joint

The mechanical behavior of hybrid (bolted/bonded) joints is investigated. The joints under study are balanced single-lap joints, and an elastic behavior of the materials is assumed. A fully parametric analytical two-dimensional model, based on the Finite Element Method, is presented. A special Finite Element ("Bonded Beams" element) is computed in order to simulate the bonded adherends. The simulation of fasteners is examined through experimental and numerical approaches. Good agreement was found between the experimental and numerical results

AUTHOR'S PROOF Emergence of Influential Spreaders in Modified Rumor Models AUTHOR'S PROOF

Abstract The burst in the use of online social networks over the last decade has provided evidence that current rumor spreading models miss some fundamental ingredients in order to reproduce how information is disseminated. In particular, recent literature has revealed that these models fail to reproduce the fact that some nodes in a network have an influential role when it comes to spread a piece of information. In this work, we introduce two mechanisms with the aim of filling the gap between theoretical and experimental results. The first model introduces the assumption that spreaders are not always active whereas the second model considers the possibility that an ignorant is not interested in spreading the rumor. In both cases, results from numerical simulations show a higher adhesion to real data than classical rumor spreading models. Our results shed some light on the mechanisms underlying the spreading of information and ideas in large social systems and pave the way for more realistic diffusion models. Keywords Rumor spreading -Online social networks -Human activity patterns Abstract The burst in the use of online social networks over the last decade has provided evidence that current rumor spreading models miss some fundamental ingredients in order to reproduce how information is disseminated. In particular, recent literature has revealed that these models fail to reproduce the fact that some nodes in a network have an influential role when it comes to spread a piece of information. In this work, we introduce two mechanisms with the aim of filling the gap between theoretical and experimental results. The first model introduces the assumption that spreaders are not always active whereas the second model considers the possibility that an ignorant is not interested in spreading the rumor. In both cases, results from numerical simulations show a higher adhesion to real data than classical rumor spreading models. Our results shed some light on the mechanisms underlying the spreading of information and ideas in large social systems and pave the way for more realistic diffusion models