Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

Meteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing information related to the topic under study. Such datasets are not always available in an appropriate format and its preparation and pre-processing implies a lot of time and effort by researchers. This paper presents a novel software tool with a user-friendly GUI to create datasets by means of management and data integration of meteorological observations from two data sources: the National Data Buoy Center and the National Centers for Environmental Prediction and for Atmospheric Research Reanalysis Project. Such datasets can be created using buoys and reanalysis data through customisable procedures, in terms of temporal resolution, predictive and objective variables, and can be used by SC and ML methodologies for prediction tasks (classification or regression). The objective is providing the research community with an automated and versatile system for the casuistry that entails well-formed and quality data integration, potentially leading to better prediction models. The software tool can be used as a supporting tool for coastal and ocean engineering applications, sustainable energy production, or environmental modelling; as well as for decision-making in the design and building of coastal protection structures, marine transport, ocean energy converters, and well-planned running of offshore and coastal engineering activities. Finally, to illustrate the applicability of the proposed tool, a case study to classify waves depending on their significant height and to predict energy flux in the Gulf of Alaska is presented

One month in advance prediction of air temperature from Reanalysis data with eXplainable Artificial Intelligence techniques

In this paper we have tackled the problem of long-term air temperature prediction with eXplainable Artificial Intelligence (XAI) models. Specifically, we have evaluated the performance of an Artificial Neural Network (ANN) architecture with sigmoidal neurons in the hidden layer, trained by means of an evolutionary algorithm (Evolutionary ANNs, EANNs). This XAI model architecture (XAI-EANN) has been applied to the long-term air temperature prediction at different sub-regions of the South of the Iberian Peninsula. In this case, the average August air temperature has been predicted from ERA5 Reanalysis data variables, obtaining good predictions skills and explainable models in terms of the input climatological variables considered. A cluster analysis has been first carried out in terms of the average air temperature in the zone, in such a way that a number of sub-regions with different air temperature behaviour have been defined. The proposed XAI-EANN model architecture has been applied to each of the defined sub-regions, in order to find significant differences among them, which can be explained with the XAI-EANN models obtained. Finally, a comprehensive comparison against some state-of-the-art techniques has also been carried out, concluding that there are statistically significant differences in terms of accuracy in favour of the proposed XAI-EANN model, which also benefits from being an XAI model

Development and validation of the Gender-Equity Model for Liver Allocation (GEMA) to prioritise candidates for liver transplantation: a cohort study

BACKGROUND: The Model for End-stage Liver Disease (MELD) and its sodium-corrected variant (MELD-Na) have created gender disparities in accessing liver transplantation. We aimed to derive and validate the Gender-Equity Model for liver Allocation (GEMA) and its sodium-corrected variant (GEMA-Na) to amend such inequities. METHODS: In this cohort study, the GEMA models were derived by replacing creatinine with the Royal Free Hospital glomerular filtration rate (RFH-GFR) within the MELD and MELD-Na formulas, with re-fitting and re-weighting of each component. The new models were trained and internally validated in adults listed for liver transplantation in the UK (2010-20; UK Transplant Registry) using generalised additive multivariable Cox regression, and externally validated in an Australian cohort (1998-2020; Royal Prince Alfred Hospital [Australian National Liver Transplant Unit] and Austin Hospital [Victorian Liver Transplant Unit]). The study comprised 9320 patients: 5762 patients for model training, 1920 patients for internal validation, and 1638 patients for external validation. The primary outcome was mortality or delisting due to clinical deterioration within the first 90 days from listing. Discrimination was assessed by Harrell's concordance statistic. FINDINGS: 449 (5·8%) of 7682 patients in the UK cohort and 87 (5·3%) of 1638 patients in the Australian cohort died or were delisted because of clinical deterioration within 90 days. GEMA showed improved discrimination in predicting mortality or delisting due to clinical deterioration within the first 90 days after waiting list inclusion compared with MELD (Harrell's concordance statistic 0·752 [95% CI 0·700-0·804] vs 0·712 [0·656-0·769]; p=0·001 in the internal validation group and 0·761 [0·703-0·819] vs 0·739 [0·682-0·796]; p=0·036 in the external validation group), and GEMA-Na showed improved discrimination compared with MELD-Na (0·766 [0·715-0·818] vs 0·742 [0·686-0·797]; p=0·0058 in the internal validation group and 0·774 [0·720-0·827] vs 0·745 [0·690-0·800]; p=0·014 in the external validation group). The discrimination capacity of GEMA-Na was higher in women than in the overall population, both in the internal (0·802 [0·716-0·888]) and external validation cohorts (0·796 [0·698-0·895]). In the pooled validation cohorts, GEMA resulted in a score change of at least 2 points compared with MELD in 1878 (52·8%) of 3558 patients (25·0% upgraded and 27·8% downgraded). GEMA-Na resulted in a score change of at least 2 points compared with MELD-Na in 1836 (51·6%) of 3558 patients (32·3% upgraded and 19·3% downgraded). In the whole cohort, 3725 patients received a transplant within 90 days of being listed. Of these patients, 586 (15·7%) would have been differently prioritised by GEMA compared with MELD; 468 (12·6%) patients would have been differently prioritised by GEMA-Na compared with MELD-Na. One in 15 deaths could potentially be avoided by using GEMA instead of MELD and one in 21 deaths could potentially be avoided by using GEMA-Na instead of MELD-Na. INTERPRETATION: GEMA and GEMA-Na showed improved discrimination and a significant re-classification benefit compared with existing scores, with consistent results in an external validation cohort. Their implementation could save a clinically meaningful number of lives, particularly among women, and could amend current gender inequities in accessing liver transplantation. FUNDING: Junta de Andalucía and EDRF

Cumulative exposure to tacrolimus and incidence of cancer after liver transplantation

Cancer is the leading cause of death after liver transplantation (LT). This multicenter case–control nested study aimed to evaluate the effect of maintenance immunosuppression on post-LT malignancy. The eligible cohort included 2495 LT patients who received tacrolimus-based immunosuppression. After 13 922 person/years follow-up, 425 patients (19.7%) developed malignancy (cases) and were matched with 425 controls by propensity score based on age, gender, smoking habit, etiology of liver disease, and hepatocellular carcinoma (HCC) before LT. The independent predictors of post-LT malignancy were older age (HR = 1.06 [95% CI 1.05–1.07]; p < .001), male sex (HR = 1.50 [95% CI 1.14–1.99]), smoking habit (HR = 1.96 [95% CI 1.42–2.66]), and alcoholic liver disease (HR = 1.53 [95% CI 1.19–1.97]). In selected cases and controls (n = 850), the immunosuppression protocol was similar (p = .51). An increased cumulative exposure to tacrolimus (CET), calculated by the area under curve of trough concentrations, was the only immunosuppression-related predictor of post-LT malignancy after controlling for clinical features and baseline HCC (CET at 3 months p = .001 and CET at 12 months p = .004). This effect was consistent for de novo malignancy (after excluding HCC recurrence) and for internal neoplasms (after excluding non-melanoma skin cancer). Therefore, tacrolimus minimization, as monitored by CET, is the key to modulate immunosuppression in order to prevent cancer after LT

Galician Atlantic Islands National Park: Challenges for the Conservation and Management of a Maritime-Terrestrial Protected Area

At present, biodiversity conservation and management in Spanish National Parks in Spain must respond to a series of regulations at a European, national and regional level, also adapting to scientific-technical progress. The availability of increasingly precise data on the values to be conserved (ecosystems, habitats, species, geodiversity) in these protected areas enables more detailed management, but also requires more rigorous, powerful, and multidisciplinary tools. Maritime-terrestrial national parks are highly sensitive areas to public use, so their impact must be one of the most important factors to take into account when planning their management. This work evaluates the past and present challenges for conservation in Galician Atlantic Islands National Park (NW Spain), where biodiversity conservation and management has evolved over time in a significant way, providing a valid case study applicable to other national parks worldwide, as well as similar situations in other contexts and scenarios. Future challenges are arising in the National Park to improve the conservation status of natural habitats and wildlife, mainly through new European initiatives that may establish important synergies with other countries

Genomic Characterization of Host Factors Related to SARS-CoV-2 Infection in People with Dementia and Control Populations: The GR@ACE/DEGESCO Study

Emerging studies have suggested several chromosomal regions as potential host genetic factors involved in the susceptibility to SARS-CoV-2 infection and disease outcome. We nested a COVID-19 genome-wide association study using the GR@ACE/DEGESCO study, searching for susceptibility factors associated with COVID-19 disease. To this end, we compared 221 COVID-19 confirmed cases with 17,035 individuals in whom the COVID-19 disease status was unknown. Then, we performed a meta-analysis with the publicly available data from the COVID-19 Host Genetics Initiative. Because the APOE locus has been suggested as a potential modifier of COVID-19 disease, we added sensitivity analyses stratifying by dementia status or by disease severity. We confirmed the existence of the 3p21.31 region (LZTFL1, SLC6A20) implicated in the susceptibility to SARS-CoV-2 infection and TYK2 gene might be involved in COVID-19 severity. Nevertheless, no statistically significant association was observed in the COVID-19 fatal outcome or in the stratified analyses (dementia-only and non-dementia strata) for the APOE locus not supporting its involvement in SARS-CoV-2 pathobiology or COVID-19 prognosis

Newer generations of multi-target CAR and STAb-T immunotherapeutics: NEXT CART Consortium as a cooperative effort to overcome current limitations

Adoptive T cellular immunotherapies have emerged as relevant approaches for treating cancer patients who have relapsed or become refractory (R/R) to traditional cancer treatments. Chimeric antigen receptor (CAR) T-cell therapy has improved survival in various hematological malignancies. However, significant limitations still impede the widespread adoption of these therapies in most cancers. To advance in this field, six research groups have created the “NEXT Generation CART MAD Consortium” (NEXT CART) in Madrid’s Community, which aims to develop novel cell-based immunotherapies for R/R and poor prognosis cancers. At NEXT CART, various basic and translational research groups and hospitals in Madrid concur to share and synergize their basic expertise in immunotherapy, gene therapy, and immunological synapse, and clinical expertise in pediatric and adult oncology. NEXT CART goal is to develop new cell engineering approaches and treatments for R/R adult and pediatric neoplasms to evaluate in multicenter clinical trials. Here, we discuss the current limitations of T cell-based therapies and introduce our perspective on future developments. Advancement opportunities include developing allogeneic products, optimizing CAR signaling domains, combining cellular immunotherapies, multi-targeting strategies, and improving tumor-infiltrating lymphocytes (TILs)/T cell receptor (TCR) therapy. Furthermore, basic studies aim to identify novel tumor targets, tumor molecules in the tumor microenvironment that impact CAR efficacy, and strategies to enhance the efficiency of the immunological synapse between immune and tumor cells. Our perspective of current cellular immunotherapy underscores the potential of these treatments while acknowledging the existing hurdles that demand innovative solutions to develop their potential for cancer treatment fully

Autoevalaución y coevaluación del proceso del trabajo en equipo en el marco del EEES

Memoria ID-0134. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2015-2016

5to. Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad. Memoria académica

El V Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad, CITIS 2019, realizado del 6 al 8 de febrero de 2019 y organizado por la Universidad Politécnica Salesiana, ofreció a la comunidad académica nacional e internacional una plataforma de comunicación unificada, dirigida a cubrir los problemas teóricos y prácticos de mayor impacto en la sociedad moderna desde la ingeniería. En esta edición, dedicada a los 25 años de vida de la UPS, los ejes temáticos estuvieron relacionados con la aplicación de la ciencia, el desarrollo tecnológico y la innovación en cinco pilares fundamentales de nuestra sociedad: la industria, la movilidad, la sostenibilidad ambiental, la información y las telecomunicaciones. El comité científico estuvo conformado formado por 48 investigadores procedentes de diez países: España, Reino Unido, Italia, Bélgica, México, Venezuela, Colombia, Brasil, Estados Unidos y Ecuador. Fueron recibidas un centenar de contribuciones, de las cuales 39 fueron aprobadas en forma de ponencias y 15 en formato poster. Estas contribuciones fueron presentadas de forma oral ante toda la comunidad académica que se dio cita en el Congreso, quienes desde el aula magna, el auditorio y la sala de usos múltiples de la Universidad Politécnica Salesiana, cumplieron respetuosamente la responsabilidad de representar a toda la sociedad en la revisión, aceptación y validación del conocimiento nuevo que fue presentado en cada exposición por los investigadores. Paralelo a las sesiones técnicas, el Congreso contó con espacios de presentación de posters científicos y cinco workshops en temáticas de vanguardia que cautivaron la atención de nuestros docentes y estudiantes. También en el marco del evento se impartieron un total de ocho conferencias magistrales en temas tan actuales como la gestión del conocimiento en la universidad-ecosistema, los retos y oportunidades de la industria 4.0, los avances de la investigación básica y aplicada en mecatrónica para el estudio de robots de nueva generación, la optimización en ingeniería con técnicas multi-objetivo, el desarrollo de las redes avanzadas en Latinoamérica y los mundos, la contaminación del aire debido al tránsito vehicular, el radón y los riesgos que representa este gas radiactivo para la salud humana, entre otros