Framework for collaborative intelligence in forecasting day-ahead electricity price

Electricity price forecasting in wholesale markets is an essential asset for deciding bidding strategies and operational schedules. The decision making process is limited if no understanding is given on how and why such electricity price points have been forecast. The present article proposes a novel framework that promotes human–machine collaboration in forecasting day-ahead electricity price in wholesale markets. The framework is based on a new model architecture that uses a plethora of statistical and machine learning models, a wide range of exogenous features, a combination of several time series decomposition methods and a collection of time series characteristics based on signal processing and time series analysis methods. The model architecture is supported by open-source automated machine learning platforms that provide a baseline reference used for comparison purposes. The objective of the framework is not only to provide forecasts, but to promote a human-in-the-loop approach by providing a data story based on a collection of model-agnostic methods aimed at interpreting the mechanisms and behavior of the new model architecture and its predictions. The framework has been applied to the Spanish wholesale market. The forecasting results show good accuracy on mean absolute error (1.859, 95% HDI [0.575, 3.924] EUR (MWh)−1) and mean absolute scaled error (0.378, 95% HDI [0.091, 0.934]). Moreover, the framework demonstrates its human-centric capabilities by providing graphical and numeric explanations that augments understanding on the model and its electricity price point forecasts

Análisis de impacto de alternativas para la financiación de las energías renovables en España

This article analyzes the economic, social and environmental impact of various financing mechanisms for the regulated costs of renewable energies in the electricity sector (RECORE) in Spain. The scenarios analysed, alternative to the current system, in which the costs are transferred in full to the electricity bill of final consumers, are the following: financing through the General State Budgets (PGE scenario), financing through a tax proportional to final energy consumption (Energy scenario) and financed through a CO2 tax in diffuse sectors (CO2 scenario). The study uses a computable general equilibrium (CGE) model and a micro-simulation model that includes detailed information on the 22,000 households included in the Household Budget Survey. The results show that the impact at the macroeconomic level is positive but very small for all the scenarios analyzed and that the changes at the sectoral level or in emissions depend notably on the scenario. All the scenarios favor low-income households since their spending on electricity represents a relatively higher percentage of their income. Although no alternative is better in all the dimensions analyzed, taxes on energy or CO2 favor the energy transition, while the PGE alter native generates more progressive distributional effects.Este trabajo ha sido cofinanciado por Iberdrola, el programa BERC 2018-2021, el Ministerio de Economía y Competitividad a través de la distinción María de Maeztu excelencia acreditación MDM-2017- 0714 y el Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093352-B-I00

Adolescent Verbal Memory as a Psychosis Endophenotype: A Genome-Wide Association Study in an Ancestrally Diverse Sample

Verbal memory impairment is one of the most prominent cognitive deficits in psychosis. However, few studies have investigated the genetic basis of verbal memory in a neurodevelopmental context, and most genome-wide association studies (GWASs) have been conducted in European-ancestry populations. We conducted a GWAS on verbal memory in a maximum of 11,017 participants aged 8.9 to 11.1 years in the Adolescent Brain Cognitive Development Study®, recruited from a diverse population in the United States. Verbal memory was assessed by the Rey Auditory Verbal Learning Test, which included three measures of verbal memory: immediate recall, short-delay recall, and long-delay recall. We adopted a mixed-model approach to perform a joint GWAS of all participants, adjusting for ancestral background and familial relatedness. The inclusion of participants from all ancestries increased the power of the GWAS. Two novel genome-wide significant associations were found for short-delay and long-delay recall verbal memory. In particular, one locus (rs9896243) associated with long-delay recall was mapped to the NSF (N-Ethylmaleimide Sensitive Factor, Vesicle Fusing ATPase) gene, indicating the role of membrane fusion in adolescent verbal memory. Based on the GWAS in the European subset, we estimated the SNP-heritability to be 15% to 29% for the three verbal memory traits. We found that verbal memory was genetically correlated with schizophrenia, providing further evidence supporting verbal memory as an endophenotype for psychosis

The effect of CYP2D6 variation on antipsychotic-induced hyperprolactinaemia: a systematic review and meta-analysis

Hyperprolactinemia is a known adverse drug reaction to antipsychotic treatment. Antipsychotic blood levels are influenced by cytochrome P450 enzymes, primarily CYP2D6. Variation in CYP450 genes may affect the risk of antipsychotic-induced hyperprolactinemia. We undertook a systematic review and meta-analysis to assess whether CYP2D6 functional genetic variants are associated with antipsychotic-induced hyperprolactinemia. The systematic review identified 16 relevant papers, seven of which were suitable for the meta-analysis (n = 303 participants including 134 extreme metabolisers). Participants were classified into four phenotype groups as poor, intermediate, extensive, and ultra-rapid metabolisers. A random effects meta-analysis was used and Cohen’s d calculated as the effect size for each primary study. We found no significant differences in prolactin levels between CYP2D6 metabolic groups. Current evidence does not support using CYP2D6 genotyping to reduce risk of antipsychotic-induced hyperprolactinemia. However, statistical power is limited. Future studies with larger samples and including a range of prolactin-elevating drugs are needed

The influence of CYP2D6 and CYP2C19 genetic variation on diabetes mellitus risk in people taking antidepressants and antipsychotics

CYP2D6 and CYP2C19 enzymes are essential in the metabolism of antidepressants and antipsychotics. Genetic variation in these genes may increase risk of adverse drug reactions. Antidepressants and antipsychotics have previously been associated with risk of diabetes. We examined whether individual genetic differences in CYP2D6 and CYP2C19 contribute to these effects. We identified 31,579 individuals taking antidepressants and 2699 taking antipsychotics within UK Biobank. Participants were classified as poor, intermediate, or normal metabolizers of CYP2D6, and as poor, intermediate, normal, rapid, or ultra-rapid metabolizers of CYP2C19. Risk of diabetes mellitus represented by HbA1c level was examined in relation to the metabolic phenotypes. CYP2D6 poor metabolizers taking paroxetine had higher Hb1Ac than normal metabolizers (mean difference: 2.29 mmol/mol; p < 0.001). Among participants with diabetes who were taking venlafaxine, CYP2D6 poor metabolizers had higher HbA1c levels compared to normal metabolizers (mean differences: 10.15 mmol/mol; p < 0.001. Among participants with diabetes who were taking fluoxetine, CYP2D6 intermediate metabolizers and decreased HbA1c, compared to normal metabolizers (mean difference −7.74 mmol/mol; p = 0.017). We did not observe any relationship between CYP2D6 or CYP2C19 metabolic status and HbA1c levels in participants taking antipsychotic medication. Our results indicate that the impact of genetic variation in CYP2D6 differs depending on diabetes status. Although our findings support existing clinical guidelines, further research is essential to inform pharmacogenetic testing for people taking antidepressants and antipsychotics

Análisis de impacto del Plan Nacional Integrado de Energía y Clima PNIEC 2021-2030 de España

Este artículo recoge un análisis del impacto del borrador del Plan Nacional Integrado de Energía y Clima (PNIEC) 2021-2030 de España, cuyo objetivo central es reducir las emisiones de gases de efecto invernadero (GEI) un 23 por 100 con respecto a 1990. El estudio utiliza varios modelos (DENIO y FASTT-TM5) para abordar los impactos de una forma integrada y multidisciplinar. Los resultados obtenidos muestran que el PNIEC movilizaría 241.000 millones de euros, de los cuales un 80 por 100 provendría de financiación privada. Las medidas del PNIEC reducirían en 67.000 millones de euros la importación de combustibles fósiles, que serían sustituidos por energías renovables autóctonas, y generarían un aumento del PIB del 1,8 por 100 en 2030 y del empleo neto entre 253.000 y 348.000 empleos/año. La reducción de GEI, lleva asociada una importante reducción de emisiones de contaminantes atmosféricos que causan daño a la salud (SO2, NOX, PM2.5, entre otras), lo que supondría una reducción del 27 por 100 de las muertes prematuras. Una conclusión robusta de este trabajo, similar a la de otros estudios recientes (OCDE, 2017; Comisión Europea, 2018; FUNSEAM, 2018 o IRENA, 2019), es que las soluciones para la crisis climática además de urgentes y necesarias, son una oportunidad, si son bien aprovechadas por aquellos países importadores netos de combustibles fósiles y que además disponen de recursos renovables.Los autores/as agradecen al equipo de la Subdirección General de Energías Renovables y Estudios del MITECO, a cargo de la mode-lización energética: Patricia Bañón, Miriam Bueno, Alejandro Fernández, Javier Galar-za, Víctor Marcos y Manuel Pérez. También a Pedro Linares (Universidad P. Comillas), Antxon Olabe (MITECO), Sara Aagesen (MITECO), Hugo Lucas (IDAE) y Eduardo González (OECC) por los comentarios recibi-dos, así como a IDAE y la Oficina Española de Cambio Climático y la Unidad de Inventarios por la información proporcionada. Cualquier error es responsabilidad de los autores. Fi-nalmente, agradecen la cofinanciación del Gobierno Vasco a través del programa BERC 2018-2021 y del Gobierno de España a tra-vés de la acreditación de BC3 como centro María de Maeztu (MDM-2017-0714) y MI-NECO (RTI2018-093352-B-I00)

Working with the police service and homeless services in North West England to reduce alcohol harms: A feasibility study of a tailored Blue Light approach.

Introduction Deaths caused by alcohol are increasing in England and 80 % of people with alcohol use disorders (AUDs) are not in treatment. The Blue Light approach (Alcohol Change UK) is an initiative to support people with AUDs who are not in treatment. This study aimed to tailor the Blue Light approach (combined with alcohol identification and alcohol brief interventions [ABI] training) for police officers and homeless service staff in North West England, and to qualitatively evaluate the feasibility and acceptability of the training. Methods The Blue Light approach was tailored using co-production activities, based on Transdisciplinary Action Research. Full-day and half-day training sessions were delivered to the police (full-day N = 14, half-day N = 54) and homeless service staff (full-day N = 11, half-day N = 32), in local police stations and online (four half-day sessions). Semi-structured interviews (N = 23) were conducted to evaluate implementation and integration, analysing the qualitative data in line with Normalisation Process Theory. Results Four themes were identified, each with two to three sub-themes, reflecting: (i) the importance of training for working practice, (ii) implementation of the interventions, (iii) changes to relationships within and between organizations, and (iv) recommendations for further changes to the training. Differences in findings across the organizations (police versus homeless services) and by training type attended (full-day versus half-day, in-person versus online) are presented. Conclusions There is evidence to suggest that the training has provided worthwhile knowledge and intervention techniques that can become embedded into working practices. Nevertheless, structural barriers were apparent, primarily within the police service, with clear disparities between recognising the value of the training and what is achievable in practice, given the competing demands

Psychosis Endophenotypes:A Gene-Set-Specific Polygenic Risk Score Analysis

Background and Hypothesis:Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes.Study Design:We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score.Study Results:After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: −1.15 µV; 95% CI: −1.70 to −0.59 µV; P = 6 × 10−5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores.Conclusions:Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models

Caring for Those Who Take Care of Others: Developing Systemic and Sustainable Mental Health Support for the Diverse Healthcare Workforce in the United Kingdom

Pressures such as high workload, stretched resources, and financial stress are resulting in healthcare workers experiencing high rates of mental health conditions, high suicide rates, high rates of staff absences from work, and high vacancy rates for certain healthcare professions. All of these factors point to the fact that a systematic and sustainable approach to mental health support at different levels and in different ways is more important than ever. In response, we present a holistic analysis of the mental health and wellbeing needs of healthcare workers across the United Kingdom healthcare ecosystem. We recommend that healthcare organisations should consider the specific circumstances of these staff and develop strategies to counter the negative impact of these factors and help safeguard the mental health of their staff