Intelligent energy storage management trade-off system applied to Deep Learning predictions

The control of the electrical power supply is one of the key bases to reach the sustainable development goals set by United Nations. The achievement of these objectives encourages a dual strategy of creation and diffusion of renewable energies and other technologies of zero emission. Thus, meet the emerging necessities require, inevitably, a significant transformation of the building sector to improve the design of the electrical infrastructure. This improvement should be linked to advanced techniques that allows the identification of complex patterns in large amount of data, such as Deep Learning ones, in order to mitigate potential uncertainties. Accurate electricity and energy supply prediction models, in combination with storage systems will be reflected directly in efficiency improvements in buildings. In this paper, a branch of Deep Learning models, known as Standard Neural Networks, are used to predict electricity consumption and photovoltaic generation with the purpose of reduce the energy wasted, by managing the storage system using Reinforcement Learning technique. Specifically, Deep Reinforcement Learning is applied using the Deep Q-Learning agent. Furthermore, the accuracy of the predicted variables is measured by means of normalized Mean Bias Error (nMBE), and normalized Root Mean Squared Error (nRMSE). The methodologies developed are validated in an existing building, the School of Mining and Energy Engineering located on the Campus of the University of Vigo.Agencia Estatal de Investigación | Ref. TED2021-130677B-I00Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

Machine learning and deep learning models applied to photovoltaic production forecasting

The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ is a common alternative to improve sustainability in this sector. An efficient trade-off of the photovoltaic source in the fields of Zero Energy Buildings (ZEB), nearly Zero Energy Buildings (nZEB) or MicroGrids (MG) requires an accurate forecast of photovoltaic production. These systems constantly generate data that are not used. Artificial Intelligence methods can take advantage of this missing information and provide accurate forecasts in real time. Thus, in this manuscript a comparative analysis is carried out to determine the most appropriate Artificial Intelligence methods to forecast photovoltaic production in buildings. On the one hand, the Machine Learning methods considered are Random Forest (RF), Extreme Gradient Boost (XGBoost), and Support Vector Regressor (SVR). On the other hand, Deep Learning techniques used are Standard Neural Network (SNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The models are checked with data from a real building. The models are validated using normalized Mean Bias Error (nMBE), normalized Root Mean Squared Error (nRMSE), and the coefficient of variation (R2). Standard deviation is also used in conjunction with these metrics. The results show that the models forecast the test set with errors of less than 2.00% (nMBE) and 7.50% (nRMSE) in the case of considering nights, and 4.00% (nMBE) and 11.50% (nRMSE) if nights are not considered. In both situations, the R2 is greater than 0.85 in all models.Universidade de Vigo | Ref. 00VI 131H 641021

Proteómica redox diferencial: identificación de proteínas oxidadas reversiblemente en respuesta a hipoxia

Comunicaciones a congreso

Impacto del manejo agronómico y fertilización intensiva en los suelos de Panao

In order to evaluate the impact of long-term agronomic management and intensive f ertilizat io n on the soil properties of Pachitea, this study was carried out. Five lots were taken f rom the province of Pachitea, representative soils of the type of management carried out in this agricultural context. A virgin or pristine soil as absolute control (SV), a soil with less than 20 years of traditional agriculture (SAT1), a soil with intensive f ertilization f or more than 40 years (SFI), a soil with traditional agriculture between 20-40 years (SAT2), a soil with organic f ertilization f or more than 40 years (SAO) and a f orest soil (SB). Soil samples were taken f ro m each 20 cm deep lot and all their physical and chemical properties were analyzed. The results were analyzed in a principal component analysis (PCA), analysis of variance (ANOVA) and regression analysis. The results showed that the agricultural use of the changes changed all the physical and chemical properties of the soil over time. The most sensitive characteristics were pH and organic matter (OM). The changeable acidity (AC) was explained by the presence of Aluminum (Al) and very little by hydrogen (H), suggesting that the degradation of soils is quite strong in this context.Con el objetivo de evaluar el impacto del manejo agronómico y fertilización intensiva de largo plazo sobre las propiedades de suelos de Pachitea, se realizó este estudio. Se tomaron cinco lotes de la provincia de Pachitea, suelos representativos del tipo de manejo que se realiza en este contexto agrícola. Un suelo virgen o prístino como testigo absoluto (SV), un suelo con menos de 20 años de agricultura tradicional (SAT1), un suelo con fertilización intensiva por más de 40 años (SFI), un suelo con agricultura tradicional entre 20-40 años (SAT2), un suelo con abonamiento orgánico por más de 40 años (SAO) y un suelo de bosque (SB). Se tomaron muestras de suelo de cada lote de 20 cm de profundidad y se analizaron todas las propiedades físicas y químicas de los mismos. Los resultados se analizaron utilizado un análisis de componentes principales (ACP), análisis de varianza (ANOVA) y análisis de regresión. Los resultados mostraron que el uso agrícola de los suelos cambió todas las propiedades físicas y químicas del suelo en el largo plazo. Las características más sensibles fueron el pH y la materia orgánica (MO). La acidez cambiable (AC) estuvo explicado por la presencia de Aluminio (Al) y muy poco por el hidrogeno (H), sugiriendo que la degradación de los suelos es bastante fuerte en este contexto

Effect of β-Glucans in Diets on Growth, Survival, Digestive Enzyme Activity, and Immune System and Intestinal Barrier Gene Expression for Tropical Gar (Atractosteus tropicus) Juveniles

The application of β-1,3/1,6-glucan derived from yeast at five concentrations (0%, 0.5%, 1.0%, 1.5%, and 2.0%) in formulated diets was evaluated in juveniles for its effects on the growth, survival, digestive enzymatic activity, and expression of genes associated with the immune system (interlukin-10 (IL-10), transforming growth factor (TGF), occludin (OCC), mucin2 (MUC2), lysozyme (LYS), and nucleotide-binding and oligomerization domain 2 (NOD2)) in tropical gar (Atractosteus tropicus). For the experiment, three replicates of 30 fish per experimental unit (70 L) were cultivated for 62 days. The growth results showed no statistically significant differences in relation to weight and total length between treatments. The activity of digestive enzymes (alkaline proteases, trypsin, leucine aminopeptidase, and amylase) did not show significant differences between treatments, except for chymotrypsin activity, where fish fed 1.0% and 1.5% of β-glucans showed higher activities compared with the rest of the treatments. On the other hand, the analysis of gene expression did not show significant differences between treatments, although a tendency of increase in the expression of IL-10, TGF, MUC2, and OCC was observed with an addition of 1.5% of the prebiotic, but there was a decrease in the fish fed with 2% of the prebiotic. It is possible to include concentrations of between 0.5% and 1.5% of β-glucans in the diets for A. tropicus, with no detectable adverse effects on growth, survival, digestive enzyme activity, or specific gene expression. β-glucan 1,3/1,6 added at 1.0% and 1.5% in the diet significantly increases chymotrypsin activity.info:eu-repo/semantics/publishedVersio

P53 wild-type colorectal cancer cells that express a fetal gene signature are associated with metastasis and poor prognosis

Current therapy against colorectal cancer (CRC) is based on DNA-damaging agents that remain ineffective in a proportion of patients. Whether and how non-curative DNA damage-based treatment affects tumor cell behavior and patient outcome is primarily unstudied. Using CRC patient-derived organoids (PDO)s, we show that sublethal doses of chemotherapy (CT) does not select previously resistant tumor populations but induces a quiescent state specifically to TP53 wildtype (WT) cancer cells, which is linked to the acquisition of a YAP1-dependent fetal phenotype. Cells displaying this phenotype exhibit high tumor-initiating and metastatic activity. Nuclear YAP1 and fetal traits are present in a proportion of tumors at diagnosis and predict poor prognosis in patients carrying TP53 WT CRC tumors. We provide data indicating the higher efficacy of CT together with YAP1 inhibitors for eradication of therapy resistant TP53 WT cancer cells. Together these results identify fetal conversion as a useful biomarker for patient prognosis and therapy prescription. The failure of chemotherapy in colorectal cancer is currently unclear. Here, the authors show that upon sub-lethal dose of chemotherapy wild-type p53 colorectal cancers acquire a quiescence-like phenotype and a YAP-dependent fetal-like intestinal stem cell state associated with a higher metastatic activity and poor prognosis in patients

RICORS2040 : The need for collaborative research in chronic kidney disease

Chronic kidney disease (CKD) is a silent and poorly known killer. The current concept of CKD is relatively young and uptake by the public, physicians and health authorities is not widespread. Physicians still confuse CKD with chronic kidney insufficiency or failure. For the wider public and health authorities, CKD evokes kidney replacement therapy (KRT). In Spain, the prevalence of KRT is 0.13%. Thus health authorities may consider CKD a non-issue: very few persons eventually need KRT and, for those in whom kidneys fail, the problem is 'solved' by dialysis or kidney transplantation. However, KRT is the tip of the iceberg in the burden of CKD. The main burden of CKD is accelerated ageing and premature death. The cut-off points for kidney function and kidney damage indexes that define CKD also mark an increased risk for all-cause premature death. CKD is the most prevalent risk factor for lethal coronavirus disease 2019 (COVID-19) and the factor that most increases the risk of death in COVID-19, after old age. Men and women undergoing KRT still have an annual mortality that is 10- to 100-fold higher than similar-age peers, and life expectancy is shortened by ~40 years for young persons on dialysis and by 15 years for young persons with a functioning kidney graft. CKD is expected to become the fifth greatest global cause of death by 2040 and the second greatest cause of death in Spain before the end of the century, a time when one in four Spaniards will have CKD. However, by 2022, CKD will become the only top-15 global predicted cause of death that is not supported by a dedicated well-funded Centres for Biomedical Research (CIBER) network structure in Spain. Realizing the underestimation of the CKD burden of disease by health authorities, the Decade of the Kidney initiative for 2020-2030 was launched by the American Association of Kidney Patients and the European Kidney Health Alliance. Leading Spanish kidney researchers grouped in the kidney collaborative research network Red de Investigación Renal have now applied for the Redes de Investigación Cooperativa Orientadas a Resultados en Salud (RICORS) call for collaborative research in Spain with the support of the Spanish Society of Nephrology, Federación Nacional de Asociaciones para la Lucha Contra las Enfermedades del Riñón and ONT: RICORS2040 aims to prevent the dire predictions for the global 2040 burden of CKD from becoming true

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2