Insuficiencia cardiaca: valoración pronóstica de los cambios en el ADE

Introducción: Diversos trabajos han demostrado el valor del ancho de distribución eritrocitaria (ADE) como biomarcador pronóstico independiente de morbilidad y mortalidad en la insuficiencia cardiaca (IC) aguda y crónica. Algunos autores han sugerido el valor de ADE≥15,4% como referencia para la toma de decisiones en la práctica clínica habitual. Asimismo, es escasa la evidencia sobre el posible valor pronóstico de la variabilidad del ADE en determinaciones seriadas ambulatorias. Objetivos: Comprobar si en una población de pacientes ingresados por IC aguda, el valor de referencia ADE≥15,4%, los valores de ADE estratificados en cuartiles y la variación en el tiempo del ADE se relacionan con una mayor incidencia tanto de muerte global como de reingreso por IC descompensada (endpoints primarios) tras un periodo de seguimiento ambulatorio (6 meses). Material y métodos: Se estudió una cohorte de 221 pacientes ingresados por IC aguda, recogiendo datos clínicos, analíticos y ecocardiográficos. Posteriormente se realizó un seguimiento ambulatorio en la consulta monográfica de IC (6 meses), siendo los pacientes controlados al primer, tercer y sexto mes tras el alta. Se realizó el análisis estadístico mediante SPSS 20.0. Resultados: En total, fallecieron 22 pacientes y 67 reingresaron por IC descompensada. La edad media fue 79,41(8,08) años; 52% varones; fracción de eyección del ventrículo izquierdo: 52,89% (13,92); ADE: 15,8% (intervalo intercuartílico 14,65-17,6%). Los pacientes con ADE≥15,4% tuvieron mayor incidencia de mortalidad global y de reingreso por IC descompensada (p=0,036 y p=0,05 respectivamente). El análisis de supervivencia distinguió según el valor de ADE≥15,4% un aumento de mortalidad global (log rank test= 0,034). A su vez, se observó un incremento no significativo de incidencia de endpoints primarios a través de los cuartiles de ADE (p=0,174 y p=0,078, respectivamente) y en aquellos pacientes cuyo ADE aumentó en el tiempo (p=0,119 y p=0,200, respectivamente). Conclusión: El punto de corte de ADE≥15,4% se asoció en nuestra cohorte con una mayor incidencia de mortalidad global y de reingreso por IC descompensada, pero no se mantuvo la significación al estratificar la población en cuartiles de ADE. Asimismo, la elevación progresiva del ADE parece relacionarse con un mayor riesgo de mortalidad global y reingreso por IC

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

[EN] The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R-CV(2) (cross-validated coefficient of determination) for the best-fit models.This research was partially funded by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 and RTC-2017-6389-5.Jimeno-Sáez, P.; Senent-Aparicio, J.; Cecilia-Canales, JM.; Pérez-Sánchez, J. (2020). Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). International Journal of Environmental research and Public Health (Online). 17(4):1-14. https://doi.org/10.3390/ijerph17041189S114174Pérez-Ruzafa, A., Pérez-Ruzafa, I. M., Newton, A., & Marcos, C. (2019). Coastal Lagoons: Environmental Variability, Ecosystem Complexity, and Goods and Services Uniformity. Coasts and Estuaries, 253-276. doi:10.1016/b978-0-12-814003-1.00015-0Kennish, M. J. (2015). Coastal Lagoons. Encyclopedia of Earth Sciences Series, 140-143. doi:10.1007/978-94-017-8801-4_47García-Ayllón, S. (2019). New Strategies to Improve Co-Management in Enclosed Coastal Seas and Wetlands Subjected to Complex Environments: Socio-Economic Analysis Applied to an International Recovery Success Case Study after an Environmental Crisis. Sustainability, 11(4), 1039. doi:10.3390/su11041039Le Moal, M., Gascuel-Odoux, C., Ménesguen, A., Souchon, Y., Étrillard, C., Levain, A., … Pinay, G. (2019). Eutrophication: A new wine in an old bottle? Science of The Total Environment, 651, 1-11. doi:10.1016/j.scitotenv.2018.09.139Alcolea, A., Contreras, S., Hunink, J. E., García-Aróstegui, J. L., & Jiménez-Martínez, J. (2019). Hydrogeological modelling for the watershed management of the Mar Menor coastal lagoon (Spain). Science of The Total Environment, 663, 901-914. doi:10.1016/j.scitotenv.2019.01.375Nixon, S. W. (1995). Coastal marine eutrophication: A definition, social causes, and future concerns. Ophelia, 41(1), 199-219. doi:10.1080/00785236.1995.10422044Huang, J., Gao, J., & Zhang, Y. (2015). Combination of artificial neural network and clustering techniques for predicting phytoplankton biomass of Lake Poyang, China. Limnology, 16(3), 179-191. doi:10.1007/s10201-015-0454-7Canfield, D. E. (1983). PREDICTION OF CHLOROPHYLL A CONCENTRATIONS IN FLORIDA LAKES: THE IMPORTANCE OF PHOSPHORUS AND NITROGEN. Journal of the American Water Resources Association, 19(2), 255-262. doi:10.1111/j.1752-1688.1983.tb05323.xPhillips, G., Pietiläinen, O.-P., Carvalho, L., Solimini, A., Lyche Solheim, A., & Cardoso, A. C. (2008). Chlorophyll–nutrient relationships of different lake types using a large European dataset. Aquatic Ecology, 42(2), 213-226. doi:10.1007/s10452-008-9180-0EL PAÍS https://elpais.com/elpais/2019/10/22/inenglish/1571743580_215496.htmlGarcía-Ayllón, S. (2017). Integrated management in coastal lagoons of highly complexity environments: Resilience comparative analysis for three case-studies. Ocean & Coastal Management, 143, 16-25. doi:10.1016/j.ocecoaman.2016.10.007Garcia-Ayllon, S. (2018). The Integrated Territorial Investment (ITI) of the Mar Menor as a model for the future in the comprehensive management of enclosed coastal seas. Ocean & Coastal Management, 166, 82-97. doi:10.1016/j.ocecoaman.2018.05.004Pérez-Ruzafa, A., Campillo, S., Fernández-Palacios, J. M., García-Lacunza, A., García-Oliva, M., Ibañez, H., … Marcos, C. (2019). Long-Term Dynamic in Nutrients, Chlorophyll a, and Water Quality Parameters in a Coastal Lagoon During a Process of Eutrophication for Decades, a Sudden Break and a Relatively Rapid Recovery. Frontiers in Marine Science, 6. doi:10.3389/fmars.2019.00026Iglesias, C., Martínez Torres, J., García Nieto, P. J., Alonso Fernández, J. R., Díaz Muñiz, C., Piñeiro, J. I., & Taboada, J. (2013). Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain. Water Resources Management, 28(2), 319-331. doi:10.1007/s11269-013-0487-9Najah, A., El-Shafie, A., Karim, O. A., & El-Shafie, A. H. (2012). Application of artificial neural networks for water quality prediction. Neural Computing and Applications, 22(S1), 187-201. doi:10.1007/s00521-012-0940-3Li, X., Cheng, Z., Yu, Q., Bai, Y., & Li, C. (2017). Water-Quality Prediction Using Multimodal Support Vector Regression: Case Study of Jialing River, China. Journal of Environmental Engineering, 143(10), 04017070. doi:10.1061/(asce)ee.1943-7870.0001272Su, J., Wang, X., Zhao, S., Chen, B., Li, C., & Yang, Z. (2015). A Structurally Simplified Hybrid Model of Genetic Algorithm and Support Vector Machine for Prediction of Chlorophyll a in Reservoirs. Water, 7(12), 1610-1627. doi:10.3390/w7041610Abba, S. I., Hadi, S. J., & Abdullahi, J. (2017). River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Computer Science, 120, 75-82. doi:10.1016/j.procs.2017.11.212Juntunen, P., Liukkonen, M., Pelo, M., Lehtola, M. J., & Hiltunen, Y. (2012). Modelling of Water Quality: An Application to a Water Treatment Process. Applied Computational Intelligence and Soft Computing, 2012, 1-9. doi:10.1155/2012/846321Li, X., Sha, J., & Wang, Z. (2016). A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen. Hydrology Research, 48(5), 1214-1225. doi:10.2166/nh.2016.149Charulatha, G., Srinivasalu, S., Uma Maheswari, O., Venugopal, T., & Giridharan, L. (2017). Evaluation of ground water quality contaminants using linear regression and artificial neural network models. Arabian Journal of Geosciences, 10(6). doi:10.1007/s12517-017-2867-6Keller, S., Maier, P., Riese, F., Norra, S., Holbach, A., Börsig, N., … Hinz, S. (2018). Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity. International Journal of Environmental Research and Public Health, 15(9), 1881. doi:10.3390/ijerph15091881Li, X., Sha, J., & Wang, Z.-L. (2017). Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks. Water, 9(7), 524. doi:10.3390/w9070524Yi, H.-S., Park, S., An, K.-G., & Kwak, K.-C. (2018). Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea. International Journal of Environmental Research and Public Health, 15(10), 2078. doi:10.3390/ijerph15102078Nazeer, M., Bilal, M., Alsahli, M., Shahzad, M., & Waqas, A. (2017). Evaluation of Empirical and Machine Learning Algorithms for Estimation of Coastal Water Quality Parameters. ISPRS International Journal of Geo-Information, 6(11), 360. doi:10.3390/ijgi6110360Erena, Domínguez, Aguado, Soria, & García-Galiano. (2019). Monitoring Coastal Lagoon Water Quality Through Remote Sensing: The Mar Menor as a Case Study. Water, 11(7), 1468. doi:10.3390/w11071468García-Oliva, M., Marcos, C., Umgiesser, G., McKiver, W., Ghezzo, M., De Pascalis, F., & Pérez-Ruzafa, A. (2019). Modelling the impact of dredging inlets on the salinity and temperature regimes in coastal lagoons. Ocean & Coastal Management, 180, 104913. doi:10.1016/j.ocecoaman.2019.104913López-Ballesteros, A., Senent-Aparicio, J., Srinivasan, R., & Pérez-Sánchez, J. (2019). Assessing the Impact of Best Management Practices in a Highly Anthropogenic and Ungauged Watershed Using the SWAT Model: A Case Study in the El Beal Watershed (Southeast Spain). Agronomy, 9(10), 576. doi:10.3390/agronomy9100576Senent-Aparicio, J., Pérez-Sánchez, J., García-Aróstegui, J., Bielsa-Artero, A., & Domingo-Pinillos, J. (2015). Evaluating Groundwater Management Sustainability under Limited Data Availability in Semiarid Zones. Water, 7(12), 4305-4322. doi:10.3390/w7084305Navarro, M. C., Pérez-Sirvent, C., Martínez-Sánchez, M. J., Vidal, J., Tovar, P. J., & Bech, J. (2008). Abandoned mine sites as a source of contamination by heavy metals: A case study in a semi-arid zone. Journal of Geochemical Exploration, 96(2-3), 183-193. doi:10.1016/j.gexplo.2007.04.011Conesa, H. M., & Jiménez-Cárceles, F. J. (2007). The Mar Menor lagoon (SE Spain): A singular natural ecosystem threatened by human activities. Marine Pollution Bulletin, 54(7), 839-849. doi:10.1016/j.marpolbul.2007.05.007Domingo-Pinillos, J., Senent-Aparicio, J., García-Aróstegui, J., & Baudron, P. (2018). Long Term Hydrodynamic Effects in a Semi-Arid Mediterranean Multilayer Aquifer: Campo de Cartagena in South-Eastern Spain. Water, 10(10), 1320. doi:10.3390/w10101320Stefanova, A., Hesse, C., & Krysanova, V. (2015). Combined Impacts of Medium Term Socio-Economic Changes and Climate Change on Water Resources in a Managed Mediterranean Catchment. Water, 7(12), 1538-1567. doi:10.3390/w7041538Velasco, J., Lloret, J., Millan, A., Marin, A., Barahona, J., Abellan, P., & Sanchez-Fernandez, D. (2006). Nutrient And Particulate Inputs Into The Mar Menor Lagoon (Se Spain) From An Intensive Agricultural Watershed. Water, Air, and Soil Pollution, 176(1-4), 37-56. doi:10.1007/s11270-006-2859-8García-Oliva, M., Pérez-Ruzafa, Á., Umgiesser, G., McKiver, W., Ghezzo, M., De Pascalis, F., & Marcos, C. (2018). Assessing the Hydrodynamic Response of the Mar Menor Lagoon to Dredging Inlets Interventions through Numerical Modelling. Water, 10(7), 959. doi:10.3390/w10070959Wei, B., Sugiura, N., & Maekawa, T. (2001). Use of artificial neural network in the prediction of algal blooms. Water Research, 35(8), 2022-2028. doi:10.1016/s0043-1354(00)00464-4(2000). Artificial Neural Networks in Hydrology. I: Preliminary Concepts. Journal of Hydrologic Engineering, 5(2), 115-123. doi:10.1061/(asce)1084-0699(2000)5:2(115)Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., & Pulido-Velazquez, D. (2018). A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain. Water, 10(2), 192. doi:10.3390/w10020192Nguyen, V. D., Tan, R. R., Brondial, Y., & Fuchino, T. (2007). Prediction of vapor–liquid equilibrium data for ternary systems using artificial neural networks. Fluid Phase Equilibria, 254(1-2), 188-197. doi:10.1016/j.fluid.2007.03.014Bekkari, N., & Zeddouri, A. (2019). Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant. Management of Environmental Quality: An International Journal, 30(3), 593-608. doi:10.1108/meq-04-2018-0084Zhang, Y., Gao, X., Smith, K., Inial, G., Liu, S., Conil, L. B., & Pan, B. (2019). Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network. Water Research, 164, 114888. doi:10.1016/j.watres.2019.114888Naghibi, S. A., Ahmadi, K., & Daneshi, A. (2017). Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping. Water Resources Management, 31(9), 2761-2775. doi:10.1007/s11269-017-1660-3Kuhn, M. (2008). Building Predictive Models inRUsing thecaretPackage. Journal of Statistical Software, 28(5). doi:10.18637/jss.v028.i05Caret: Classification and Regression Training, R Package Version 6.0-84 https://CRAN.R-project.org/package=caretMaier, H. R., Jain, A., Dandy, G. C., & Sudheer, K. P. (2010). Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software, 25(8), 891-909. doi:10.1016/j.envsoft.2010.02.003Kumar, S., & Bucher, P. (2016). Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features. BMC Bioinformatics, 17(S1). doi:10.1186/s12859-015-0846-zMjalli, F. S., Al-Asheh, S., & Alfadala, H. E. (2007). Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. Journal of Environmental Management, 83(3), 329-338. doi:10.1016/j.jenvman.2006.03.004Palani, S., Liong, S.-Y., & Tkalich, P. (2008). An ANN application for water quality forecasting. Marine Pollution Bulletin, 56(9), 1586-1597. doi:10.1016/j.marpolbul.2008.05.021Kuo, J.-T., Hsieh, M.-H., Lung, W.-S., & She, N. (2007). Using artificial neural network for reservoir eutrophication prediction. Ecological Modelling, 200(1-2), 171-177. doi:10.1016/j.ecolmodel.2006.06.018Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., Pulido-Velazquez, D., & Cecilia, J. (2017). Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain. Water, 9(5), 347. doi:10.3390/w905034

Impact Assessment of Gridded Precipitation Products on Streamflow Simulations over a Poorly Gauged Basin in El Salvador

[EN] In this study, five open access gridded precipitation (GP) products (CFSR, MSWEPv1.1, PERSIANN-CDR, CMORPH, and CHIRPSv2.0) and local climate data were evaluated over the Grande de San Miguel (GSM) River Basin in El Salvador. The main purpose was to identify optional data sources of precipitation for hydrological modelling given that ground-based precipitation gauges in El Salvador are scarce and their data includes important temporal and spatial gaps. Firstly, a direct comparison was made between the precipitation data from the five GP products and from the rain gauges. Secondly, the SWAT model was used to simulate the streamflow regimen based on the precipitation datasets. The analysis of results showed that the models produced correct predictions, and the accuracy increased as models were calibrated to each specific precipitation product. Overall, PERSIANN-CDR produced the best simulation results, including streamflow predictions in the GSM basin, and outperformed other GP products and also the results obtained from data precipitation gauges. The findings of this research support the hydrological modelling based on open-access GP products, particularly when the data from precipitation gauges are scarce and poor.This research was funded by Ministerio de Ciencia e Innovacion of Espana (grant numbers RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5) and by Ramon y Cajal Program (grant number RYC2018-025580-I).Jimeno-Sáez, P.; Blanco-Gómez, P.; Pérez-Sánchez, J.; Cecilia-Canales, JM.; Senent-Aparicio, J. (2021). Impact Assessment of Gridded Precipitation Products on Streamflow Simulations over a Poorly Gauged Basin in El Salvador. Water. 13(18):1-21. https://doi.org/10.3390/w13182497121131

Effects of overliming on the nutritional status of grapevines with special reference to micronutrient content

[EN] Aluminium plays a central role in soil acidity, which is one of the main constraints on grape production in humid, northern temperate viticultural regions. To decrease the acidity of vineyard soil, it is usually amended with alkaline materials that provide conjugate bases to weak acids (liming). However, one practical consideration is the danger of overliming, which has potential implications in terms of yield reduction and decreased bioavailability of several mineral nutrients. The main aim of this study was to evaluate the effects of overliming using dolomitic lime on grapevines growing on acid soil. The effects on the topsoil fertility parameters (0–30 cm), petiole and berry nutrient levels, berry weight and must quality properties were studied in a vineyard planted with Vitis vinifera L. cv. Mencía for three years (2014–2016). Data analysis performed using a mixed model that took into account both random effects (year of sampling) and fixed effects (liming treatments) showed that overliming decreased the manganese content in both leaf and berry tissues. Until now, nothing was known about the effects of overliming on both vine nutritional status and harvest quality properties, thus this study fills an important knowledge gap.SIThe authors are most grateful to Losada Vinos de Finca, S.A., for assisting with this research project

Impacts of swat weather generator statistics from high-resolution datasets on monthly streamflow simulation over Peninsular Spain

[EN] Study region: Peninsular Spain. Study focus: Weather data are the key drivers of hydrological modelling. However, available weather data can present gaps in data sequences and are often limited in their spatial coverage for use in such hydrological models as the Soil and Water Assessment Tool (SWAT). To overcome this limitation, SWAT includes a weather generator algorithm that can complete this data based on long-term weather statistics. This work presents a newly developed weather statistics dataset for Peninsular Spain (PSWG), calculated from national gridded datasets according to the SWAT model format. PSWG provides a higher resolution that stands as a compelling alternative to the statistics calculated from the Climate Forecast System Reanalysis (CFSR) that are available on the SWAT website. New hydrological insights for the region: The dataset has been evaluated using PSWG and CFSR datasets for different data availability scenarios to reconstruct weather series in three watersheds with contrasting weather climates. Results underscore the superiority of the PSWG dataset in reconstructing missing data for hydrological simulations. This approach provides a strong alternative for SWAT applications in Peninsular Spain and the applied methodology can be replicated in other countries that dispose of high-resolution gridded rainfall and temperature datasets.This research was funded by the Ministry of Science, Innovation and Universities of Spain under grants RTC-2017-6389-5 and RTI2018096384BI00. This work has also received funding from the European Union's Horizon 2020 research and innovation programme within the framework of the project SMARTLAGOON under grant agreement No. 101017861. Adrian Lopez Ballesteros was sponsored by the Ministry of Science, Innovation and Universities of Spain under an FPU grant (FPU17/00923) and Jose M. Cecilia under the Ramon y Cajal Program (Grant No. RYC2018025580I) . The authors would like to acknowledge AEMET for the precipitation and air temperature data provided for this work (AEMET grid dataset can be found at http:// www.aemet.es/es/serviciosclimaticos/cambio_climat) .Senent-Aparicio, J.; Jimeno-Sáez, P.; López-Ballesteros, A.; Ginés Giménez, J.; Pérez-Sánchez, J.; Cecilia-Canales, JM.; Srinivasan, R. (2021). Impacts of swat weather generator statistics from high-resolution datasets on monthly streamflow simulation over Peninsular Spain. Journal of Hydrology: Regional Studies. 35:1-15. https://doi.org/10.1016/j.ejrh.2021.100826S1153

Effects of liming on soil properties, leaf tissue cation composition and grape yield in a moderately acid vineyard soil. Influence on must and wine quality

[EN] Aims: Soil acidity decreases soil fertility and grapevine growth. Aluminum toxicity has been recognized as one of the most common causes of reduced grape yields in acid vineyard soils. The main aim of this study was to evaluate the effect of two liming materials, i.e. dolomitic lime and sugar foam, on a vineyard cultivated in an acid soil. Methods and results: The effects were studied in two soil layers (0-30 and 30-60 cm), as well as on leaf nutrient contents, grape yield, and must and wine quality properties, in a vineyard dedicated to Vitis vinifera L. cv. Mencía cultivation. The data management and analysis were carried out using ANOVA. Conclusion: Sugar foam was more efficient than dolomitic limestone as liming material since it induced the highest decrease in soil acidity properties at the same calcium carbonate equivalent dose. Effects of liming on leaf nutrient contents, grape yield, and must and wine quality properties were barely observed. Significance and impact of the study: Until recently, little was known about the effects of liming on both vine nutritional status and must/wine quality properties. Thus, this research fills an important knowledge gap.SIThis work was funded by the “Excelentísima Diputación Provincial de León”. We are specially grateful to “Losada Vinos de Finca, S.A.” for assistance in the research project

(Attenuated) hallucinations join basic symptoms in a transdiagnostic network cluster analysis

Producción CientíficaHallucinations are considered characteristic symptoms of psychosis and part of the ‘psychosis superspectrum’ of the Hierarchical Taxonomy Of Psychopathology (HiTOP) initiative. To gain insight into their psychopathological relevance, we studied their dimensional placement within a single dense transdiagnostic network constituting of basic symptoms as well as of attenuated and frank psychotic, and related symptoms. Newman's modularity analysis was used to detect symptom clusters in an earlier generated network (Jimeno, N., et al., 2020. Main symptomatic treatment targets in suspected and early psychosis: New insights from network analysis. Schizophr. Bull. 46, 884–895. https://doi.org/10.1093/schbul/sbz140). The constituting 86 symptoms were assessed with the Schizophrenia Proneness Instrument, Adult version (SPI-A), the Structured Interview for Psychosis-Risk Syndromes (SIPS), and the Positive And Negative Syndrome Scale (PANSS) in three adult samples of an early detection service: clinical high-risk (n = 203), first-episode psychosis (n = 153), and major depression (n = 104). Three clusters were detected: “subjective disturbances”, “positive symptoms and behaviors”, and “negative and anxious-depressive symptoms”. The predominately attenuated hallucinations of both SIPS and PANSS joined the basic symptoms in “subjective disturbances”, whereas other positive symptoms entered “positive symptoms and behaviors”. Our results underline the importance of insight in separating true psychotic hallucinations from other hallucinatory experiences that, albeit phenomenologically similar are still experienced with some insight, i.e., are present in an attenuated form. We conclude that, strictly, hallucinations held with any degree of insight should not be used to diagnose transition to or presence of frank psychoses and, relatedly, to justify antipsychotic medication.Deutsche Forschungsgemeinschaft (grants KL970/3-1 and KL970/3-2)Koeln Fortune Program / Faculty of Medicine of the University of Cologne (projects 8/2005 and 27/2006)Ministerio de Ciencia e Innovación - Fondo Europeo de Desarrollo Regional (projects PGC2018-098214-A-I00 and DPI2017-84280-R)Unión Europea (grant 602152)German Research Foundation (grant KA 4413/1-1

Viticultural and Biotechnological Strategies to Reduce Alcohol Content in Red Wines

Viticultural and biotechnological strategies are two approaches to deal with higher must sugar levels at harvest time. A wide range of factors could significantly affect sugar accumulation in the grape such as choice of vineyard site, soil composition, irrigation strategy, rootstock, and grape cultivar selection as well as grape yield. In this sense, approaches to canopy management are continually evolving in response to changes in other vineyard management practices; some of these could contribute to reduce soluble sugars on grape berries at harvest time. On the other hand, among possible biotechnological strategies, one of the most relevant is the control of the fermentative process by using selected yeast strains. In this chapter, we will show how some viticultural practices have influenced the accumulation of soluble sugars and other enological parameters in grape berries at harvest time. We will also report how a careful yeast selection and the implementation of different fermentation strategies can also contribute to reduce ethanol content in wines

Quantitative muscle MRI to follow up late onset Pompe patients : a prospective study

Late onset Pompe disease (LOPD) is a slow, progressive disorder characterized by skeletal and respiratory muscle weakness. Enzyme replacement therapy (ERT) slows down the progression of muscle symptoms. Reliable biomarkers are needed to follow up ERT-treated and asymptomatic LOPD patients in clinical practice. In this study, 32 LOPD patients (22 symptomatic and 10 asymptomatic) underwent muscle MRI using 3-point Dixon and were evaluated at the time of the MRI with several motor function tests and patient-reported outcome measures, and again after one year. Muscle MRI showed a significant increase of 1.7% in the fat content of the thigh muscles in symptomatic LOPD patients. In contrast, there were no noteworthy differences between muscle function tests in the same period of time. We did not observe any significant changes either in muscle MRI or in muscle function tests in asymptomatic patients over the year. We conclude that 3-point Dixon muscle MRI is a useful tool for detecting changes in muscle structure in symptomatic LOPD patients and could become part of the current follow-up protocol in daily clinics