Análisis de uniones soldadas de componentes de semirremolque para la predicción de fallo frente a fatiga

Proyecto final de carrera de Ingeniería Técnica Industrial Mecánica "ANÁLISIS DE UNIONES SOLDADAS DE COMPONENTES DE SEMIRREMOLQUE PARA LA PREDICCIÓN DE FALLO RENTE A FATIGA

Agent-Based Model to Study and Quantify the Evolution Dynamics of Android Malware Infection

[EN] In the last years the number of malware Apps that the users download to their devices has risen. In this paper, we propose an agentbased model to quantify the Android malware infection evolution, modeling the behavior of the users and the different markets where the users may download Apps. The model predicts the number of infected smartphones depending on the type of malware. Additionally, we will estimate the cost that the users should afford when the malware is in their devices. We will be able to analyze which part is more critical: the users, giving indiscriminate permissions to the Apps or not protecting their devices with antivirus software, or the Android platform, due to the vulnerabilities of the Android devices that permit their rooted. We focus on the community of Valencia, Spain, although the obtained results can be extrapolated to other places where the number of Android smartphones remains fairly stable.This work has been partially supported by the Ministerio de Econom´ıa y Competitividad Grant MTM2013-41765-P.Alegre Sanahuja, J.; Camacho Vidal, FJ.; Cortés López, JC.; Santonja, F.; Villanueva Micó, RJ. (2014). Agent-Based Model to Study and Quantify the Evolution Dynamics of Android Malware Infection. Abstract and Applied Analysis. 2014:1-10. https://doi.org/10.1155/2014/623436S1102014Di Cerbo, F., Girardello, A., Michahelles, F., & Voronkova, S. (2011). Detection of Malicious Applications on Android OS. Lecture Notes in Computer Science, 138-149. doi:10.1007/978-3-642-19376-7_12Shabtai, A., Kanonov, U., Elovici, Y., Glezer, C., & Weiss, Y. (2011). «Andromaly»: a behavioral malware detection framework for android devices. Journal of Intelligent Information Systems, 38(1), 161-190. doi:10.1007/s10844-010-0148-xBose, A., & Shin, K. G. (2011). Agent-based modeling of malware dynamics in heterogeneous environments. Security and Communication Networks, 6(12), 1576-1589. doi:10.1002/sec.298Wang, P., Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A.-L. (2009). Understanding the Spreading Patterns of Mobile Phone Viruses. Science, 324(5930), 1071-1076. doi:10.1126/science.1167053Mylonas, A., Kastania, A., & Gritzalis, D. (2013). Delegate the smartphone user? Security awareness in smartphone platforms. Computers & Security, 34, 47-66. doi:10.1016/j.cose.2012.11.004Hoare, A., Regan, D. G., & Wilson, D. P. (2008). Sampling and sensitivity analyses tools (SaSAT) for computational modelling. Theoretical Biology and Medical Modelling, 5(1), 4. doi:10.1186/1742-4682-5-

Toward an Improvement of the Analysis of Neural Coding

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.This work has been supported in part by the Spanish national research program (MAT2015-69967-C3-1), by Research Chair Bidons Egara and by a research grant of the Spanish Blind Organization (ONCE)

BRAF V600E mutational load as a prognosis biomarker in malignant melanoma

Analyzing the mutational load of driver mutations in melanoma could provide valuable information regarding its progression. We aimed at analyzing the heterogeneity of mutational load of BRAF V600E in biopsies of melanoma patients of different stages, and investigating its potential as a prognosis factor. Mutational load of BRAF V600E was analyzed by digital PCR in 78 biopsies of melanoma patients of different stages and 10 nevi. The BRAF V600E load was compared among biopsies of different stages. Results showed a great variability in the load of V600E (0%-81%). Interestingly, we observed a significant difference in the load of V600E between the early and late melanoma stages, in the sense of an inverse correlation between BRAF V600E mutational load and melanoma progression. In addition, a machine learning approach showed that the mutational load of BRAF V600E could be a good predictor of metastasis in stage II patients. Our results suggest that BRAF V600E is a promising biomarker of prognosis in stage II patients.This research was supported by the Basque Government (grants ELKARTEK-KK2016-036 and KK2017-041 to MDB, grant IT1138-16 to SA and predoctoral fellowship PRE_2014_1_419 to AS), and by the University of the Basque Country (UPV/EHU) (grant GIU17/066). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Neuroprotective properties of queen bee acid by autophagy induction

Autophagy is a conserved intracellular catabolic pathway that removes cytoplasmic components to contribute to neuronal homeostasis. Accumulating evidence has increasingly shown that the induction of autophagy improves neuronal health and extends longevity in several animal models. Therefore, there is a great interest in the identification of effective autophagy enhancers with potential nutraceutical or pharmaceutical properties to ameliorate age-related diseases, such as neurodegenerative disorders, and/or promote longevity. Queen bee acid (QBA, 10-hydroxy-2-decenoic acid) is the major fatty acid component of, and is found exclusively in, royal jelly, which has beneficial properties for human health. It is reported that QBA has antitumor, anti-inflammatory, and antibacterial activities and promotes neurogenesis and neuronal health; however, the mechanism by which QBA exerts these effects has not been fully elucidated. The present study investigated the role of the autophagic process in the protective effect of QBA. We found that QBA is a novel autophagy inducer that triggers autophagy in various neuronal cell lines and mouse and fly models. The beclin-1 (BECN1) and mTOR pathways participate in the regulation of QBA-induced autophagy. Moreover, our results showed that QBA stimulates sirtuin 1 (SIRT1), which promotes autophagy by the deacetylation of critical ATG proteins. Finally, QBA-mediated autophagy promotes neuroprotection in Parkinson’s disease in vitro and in a mouse model and extends the lifespan of Drosophila melanogaster. This study provides detailed evidences showing that autophagy induction plays a critical role in the beneficial health effects of QBA.This research was supported by a grant (IB18048) from Junta de Extremadura, Spain, and a grant (RTI2018-099259-A-I00) from Ministerio de Ciencia e Innovación, Spain. This work was also partially supported by “Fondo Europeo de Desarrollo Regional” (FEDER) from the European Union. Part of the equipment employed in this work has been funded by Generalitat Valeciana and co-financed with ERDF funds (OP EDRF of Comunitat Valenciana 2014-2020). G.M-C is supported by University of Extremadura (ONCE Foundation). M.P-B is a recipient of a fellowship from the “Plan Propio de Iniciación a la Investigación, Desarrollo Tecnológico e Innovación (University of Extremadura).” S.M.S.Y-D is supported by CIBERNED. E.U-C was supported by an FPU predoctoral fellowship FPU16/00684 from Ministerio de Educación, Cultura y Deporte. A.B. was supported by a postdoctoral fellowship (APOSTD2017/077). M.S.A. was supported by a predoctoral fellowship (ACIF/2018/071) both from the Conselleria d’Educació, Investigació, Cultura i Esport (Generalitat Valenciana). E.A-C was supported by a grant (IB18048) from Junta de Extremadura, Spain. S.C-C was supported by an FPU predoctoral fellowship FPU19/04435 from Ministerio de Educación, Cultura y Deporte. J.M.B-S. P was funded by the “Ramón y Cajal” program (RYC-2018-025099). J.M.F. received research support from the Instituto de Salud Carlos III, CIBERNED (CB06/05/004). M.N-S was funded by the “Ramon y Cajal” Program (RYC-2016-20883) Spain

Lenalidomide and dexamethasone with or without clarithromycin in patients with multiple myeloma ineligible for autologous transplant: a randomized trial

Although case-control analyses have suggested an additive value with the association of clarithromycin to continuous lenalidomide and dexamethasone (Rd), there are not phase III trials confirming these results. In this phase III trial, 286 patients with MM ineligible for ASCT received Rd with or without clarithromycin until disease progression or unacceptable toxicity. The primary endpoint was progression-free survival (PFS). With a median follow-up of 19 months (range, 0-54), no significant differences in the median PFS were observed between the two arms (C-Rd 23 months, Rd 29 months; HR 0.783, p = 0.14), despite a higher rate of complete response (CR) or better in the C-Rd group (22.6% vs 14.4%, p = 0.048). The most common G3-4 adverse events were neutropenia [12% vs 19%] and infections [30% vs 25%], similar between the two arms; however, the percentage of toxic deaths was higher in the C-Rd group (36/50 [72%] vs 22/40 [55%], p = 0.09). The addition of clarithromycin to Rd in untreated transplant ineligible MM patients does not improve PFS despite increasing the ?CR rate due to the higher number of toxic deaths in the C-Rd arm. Side effects related to overexposure to steroids due to its delayed clearance induced by clarithromycin in this elderly population could explain these results. The trial was registered in clinicaltrials.gov with the name GEM-CLARIDEX: Ld vs BiRd and with the following identifier NCT02575144. The full trial protocol can be accessed from ClinicalTrials.gov. This study received financial support from BMS/Celgene

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis