A note on the combustion of blends of diesel and soya, sunflower and rapeseed vegetable oils in a light boiler

Producción CientíficaThis paper deals with the study of the vegetable oils (VO) used as fuel for heating. The properties of sunflower, rape and soya oils are studied and these are compared with the properties of C-diesel fuel (used for heating domestic purposes in Spain). The mixtures of VO and diesel are studied and characterized and, finally, the results of a series of combustion trials of the mixtures in a conventional heating installation with a mechanical pulverization burner are presented. The results show that viscosity of VO limits the use of blends up to 40% of them, and the oxygen present in their structures contributes to an efficiency gai

Combustion of Soya Oil and Diesel Oil Mixtures for Use in Thermal Energy Production

Producción CientíficaIn August 2005, Spain approved the Plan for Renewable Energy Sources for the period 2005-2010 (P.E.R.), including co-combustion installations. Co-combustion in the P.E.R. aims to increase power output by 12,185.3 GWh in five years and shows great interest in studies of the combustion of mixtures of fossil and bio-combustible fuels. This paper presents studies of the co-combustion of soya oil and diesel for thermal heating. The paper begins with a characterization of soya oil as well as mixtures of this oil, with diesel, as fuels. The combustion of the soya oil mixtures and diesel is made in an installation, where the pressure of injection as well as the air volume of the burner can be changed. The obtained results inside to be the environmental average legislation and a greater efficiency of combustion is found. The conclusions show that the use of mixtures of soya oil and Diesel for producing thermal energy in conventional equipment is feasible

Methodological improvements in the final project of Civil Engineering Degree

The final project of an engineering degree represents a test of maturity where students must crossexamine all the contents studied in the degree which adds technical difficulties. Traditionally the students are supervised by only one teacher and they usually also take a previous course in the writing of the project. Currently, the program of the Civil Engineering Degree contemplates this activity in the last semester with an allocation of 18 credits. The hardest difficulty noted by students and teachers is the short time estimated for writing it. A teaching innovation project is presented herein which aims to define a working protocol in order to help the student in a more efficient and close way during the development of the task. Among the methodological changes introduced, it should be highlighted the tutoring by professors from different areas of knowledge instead of only one professor as it was traditionally done. Thus, coordination mechanisms must be implemented to guarantee the achievement of the proposed objectives. The innovation project also helps to identify tasks that can be advanced in time and gain time for the effective drafting of the project. Some other solutions found will be presented as well as a comparison of the results obtained along this year with those of the former methodology

Therapeutic Management and Long-Term Outcome of Hy-Perthyroidism in Patients with Antithyroid-Induced Agranu-Locytosis: A Retrospective, Multicenter Study

Background: Antithyroid drug-induced agranulocytosis (AIA) (neutrophils <500/mu L) is a rare but serious complication in the treatment of hyperthyroidism. Methodology: Adult patients with AIA who were followed up at 12 hospitals in Spain were retrospectively studied. A total of 29 patients were studied. The etiology of hyperthyroidism was distributed as follows: Graves' disease (n = 21), amiodarone-induced thyrotoxicosis (n = 7), and hyperfunctioning multinodular goiter (n = 1). Twenty-one patients were treated with methimazole, as well as six patients with carbimazole and two patients with propylthiouracil. Results: The median (IQR) time to development of agranulocytosis was 6.0 (4.0-11.5) weeks. The most common presenting sign was fever accompanied by odynophagia. All of the patients required admission, reverse isolation, and broad-spectrum antibiotics; moreover, G-CSF was administered to 26 patients (89.7%). Twenty-one patients received definitive treatment, thirteen patients received surgery, nine patients received radioiodine, and one of the patients required both treatments. Spontaneous normalization of thyroid hormone values occurred in six patients (four patients with amiodarone-induced thyrotoxicosis and two patients with Graves' disease), and two patients died of septic shock secondary to AIA. Conclusions: AIA is a potentially lethal complication that usually appears around 6 weeks after the initiation of antithyroid therapy. Multiple drugs are required to control hyperthyroidism before definitive treatment; additionally, in a significant percentage of patients (mainly in those treated with amiodarone), hyperthyroidism resolved spontaneously

Guía de gestión integrada de plagas: mango

Coordinadores: Ángel Martín Gil y Gregoria Aranda Aranda

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algo-rithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits.Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12).The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non -treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables.Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified.The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-alpha levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58).In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-alpha (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption.Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algorithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits. Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12). The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non-treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables. Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified. The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-α levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58). In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-α (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption. Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity.This study was supported in part by grants from the Carlos III Health Institute through the Ministry of Science, Innovation and Universities (PI15/00662, PI15/0039, PI15/00204, PI19/01040), co-funded by the European Regional Development Fund (ERDF) “A way to build Europe”, CIBERSAM, and the Catalan Agency for the Management of University and Research Grants (AGAUR 2017 SGR 1247). We also thank CERCA Programme/Generalitat de Catalunya for institutional support. Work partially supported by Biobank HUB-ICO-IDIBELL, integrated in the Spanish Biobank Network and funded by Instituto de Salud Carlos III (PT17/0015/0024) and by Xarxa Bancs de Tumors de Catalunya sponsored by Pla Director d’Oncologia de Catalunya (XBTC). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. YSC work is supported by the FPI predoctoral grant (FPI 2016/17) from Universidad Autonoma de Madrid. VS received an Intensification of the Research Activity Grant from the Instituto de Salud Carlos III (INT21/00055) during 202

Estado de conservación de las tortugas marinas en España (revisión del periodo 2013-2018)

[ES] El presente documento revisa la situación y aporta nuevos datos para las tortugas marinas en España durante el periodo 2013- 2018. Se revisa el estado de conservación para el periodo 2013- 2018 en cada una de las demarcaciones marítimas españolas: Levante¿Baleares y Estrecho¿Alborán para el Mediterráneo, y Noratlántica, Sudatlántica y Macaronesia en aguas del océano Atlántico. Se incluyen análisis de tendencias, las presiones y amenazas que afectan a cada especie y bibliografía actualizada. Para Caretta caretta, se recopila la información de los nidos en las costas mediterráneas españolas desde 2013 hasta 2018 y los movimientos de juveniles nacidos en España, tras su liberación.Camiñas, JA.; Báez, JC.; Ayllón, E.; Marco, A.; Hernández-Sastre, L.; López-Pérez, MI.; Moreno-Colera, H.... (2021). Estado de conservación de las tortugas marinas en España (revisión del periodo 2013-2018). Anales de Biología. 43:175-198. https://doi.org/10.6018/analesbio.43.17S1751984

Microglial Hemoxygenase-1 Deletion Reduces Inflammation in the Retina of Old Mice with Tauopathy

Tauopathies such as Alzheimer’s disease are characterized by the accumulation of neurotoxic aggregates of tau protein. With aging and, especially, in Alzheimer’s patients, the inducible enzyme heme oxygenase 1 (HO-1) progressively increases in microglia, causing iron accumulation, neuroinflammation, and neurodegeneration. The retina is an organ that can be readily accessed and can reflect changes that occur in the brain. In this context, we evaluated how the lack of microglial HO-1, using mice that do not express HO-1 in microglia (HMO-KO), impacts retinal macro and microgliosis of aged subjects (18 months old mice) subjected to tauopathy by intrahippocampal delivery of AAV-hTauP301L (TAU). Our results show that although tauopathy, measured as anti-TAUY9 and anti-AT8 positive immunostaining, was not observed in the retina of WT-TAU or HMO-KO+TAU mice, a morphometric study of retinal microglia and macroglia showed significant retinal changes in the TAU group compared to the WT group, such as: (i) increased number of activated microglia, (ii) retraction of microglial processes, (iii) increased number of CD68+ microglia, and (iv) increased retinal area occupied by GFAP (AROA) and C3 (AROC3). This retinal inflammatory profile was reduced in HMO-KO+TAU mice. Conclusion: Reduction of microglial HO-1 could be beneficial to prevent tauopathy-induced neuroinflammation