Hedonic (happiness) and eudaimonic (meaning in life) well-being are negatively related to depressive symptoms. Genetic variants play a role in this association, reflected in substantial genetic correlations. We investigated the overlap and differences between well-being and depressive symptoms, using results of Genome-Wide Association studies (GWAS) in UK Biobank. Subtracting GWAS summary statistics of depressive symptoms from those of happiness and meaning in life, we obtained GWASs of respectively “pure” happiness (neffective = 216,497) and “pure” meaning (neffective = 102,300). For both, we identified one genome-wide significant SNP (rs1078141 and rs79520962, respectively). After subtraction, SNP heritability reduced from 6.3% to 3.3% for pure happiness and from 6.2% to 4.2% for pure meaning. The genetic correlation between the well-being measures reduced from 0.78 to 0.65. Pure happiness and pure meaning became genetically unrelated to traits strongly associated with depressive symptoms, including loneliness, and psychiatric disorders. For other traits, including ADHD, educational attainment, and smoking, the genetic correlations of well-being versus pure well-being changed substantially. GWAS-by-subtraction allowed us to investigate the genetic variance of well-being unrelated to depressive symptoms. Genetic correlations with different traits led to new insights about this unique part of well-being. Our results can be used as a starting point to test causal relationships with other variables, and design future well-being interventions.
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The genetic contribution to psychiatric disorders is observed through the increased rates of disorders in the relatives of those diagnosed with disorders. These increased rates are observed to be nonspecific; for example, children of those with schizophrenia have increased rates of schizophrenia but also a broad range of other psychiatric diagnoses. While many factors contribute to risk, epidemiological evidence suggests that the genetic contribution carries the highest risk burden. The patterns of inheritance are consistent with a polygenic architecture of many contributing risk loci. The genetic studies of the past decade have provided empirical evidence identifying thousands of DNA variants associated with psychiatric disorders. Here, we describe how these latest results are consistent with observations from epidemiology. We provide an R tool (CHARRGe) to calculate genetic parameters from epidemiological parameters and vice versa. We discuss how the single nucleotide polymorphism–based estimates of heritability and genetic correlation relate to those estimated from family records.
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Assigning gates to flights considering physical, operational, and temporal constraints is known as the Gate Assignment Problem. This article proposes the novelty of coupling a commercial stand and gate allocation software with an off-the-grid optimization algorithm. The software provides the assignment costs, verifies constraints and restrictions of an airport, and provides an initial allocation solution. The gate assignment problem was solved using a genetic algorithm. To improve the robustness of the allocation results, delays and early arrivals are predicted using a random forest regressor, a machine learning technique and in turn they are considered by the optimization algorithm. Weather data and schedules were obtained from Zurich International Airport. Results showed that the combination of the techniques result in more efficient and robust solutions with higher degree of applicability than the one possible with the sole use of them independently.
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Research studies and recruitment processes often rely on psychometric instruments to profile respondents with regards to their ethical orientation. Completing such questionnaires can be tedious and is prone to self-presentation bias. Noting how video games often expose players to complex plots, filled with dilemmas and morally dubious options, the opportunity emerges to evaluate player’s moral orientation by analysing their in-game behaviour. In order to explore the feasibility of such an approach, we examine how users’ moral judgment correlates with choices they make in non-linear narratives, frequently present in video games. An interactive narrative presenting several moral dilemmas was created. An initial user study (N = 80) revealed only weak correlations between the users’ choices and their ethical inclinations in all ethical scales. However, by training a genetic algorithm on this data set to quantify the influence of each branch on recognising moral inclination we found a strong positive correlation between choice behaviour and self-reported ethical inclinations on a second independent group of participants (N = 20). The contribution of this work is to demonstrate how genetic algorithms can be applied in interactive stories to profile users’ ethical stance.
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Objective: The aim of this study was to assess the relationship between frailty syndrome and the nutritional status of older patients. Material and methods: This cross-sectional study was conducted in a sample of 120 patients hospitalized at the Geriatric Clinic between January 2017 and May 2017. The research tools were the Frailty Instrument of the Survey of Health, Ageing and Retirement in Europe (SHARE-FI), including relevant anthropometric measurements and muscle strength measurement, and the Mini Nutritional Assessment (MNA). All the calculations were performed using the Statistica 10.0 program. The p-values lower than 0.05 were considered as statistically significant. Results: The mean age of the participants was 71 years (SD=9.03). Most participants were from urban areas. More than half of the participants (53.3%) were women. Based on the SHARE-FI, the frailty syndrome was found in 33.3% of the participants. The mean value in the MNA scale was 24.4 points (SD=3.4). The frailty syndrome was significantly correlated to gender (p<0.025), financial status (p=0.036) and MNA (p<0.01) score. A statistically significant difference was observed between gender (p=0.026), financial status (p=0.016), place of living (p=0.046) and MNA score. Conclusion: This study confirmed significant correlations between the frailty syndrome and the nutritional status of older adults. In terms of prevention and clinical application, it seems important to control the nutritional status of older people and the frailty syndrome. The above-mentioned scales should be used to evaluate patients, analyze the risk and plan the intervention for that group of patients.
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BackgroundIdentifying modifiable factors associated with well-being is of increased interest for public policy guidance. Developments in record linkage make it possible to identify what contributes to well-being from a myriad of factors. To this end, we link two large-scale data resources; the Geoscience and Health Cohort Consortium, a collection of geo-data, and the Netherlands Twin Register, which holds population-based well-being data.ObjectiveWe perform an Environment-Wide Association Study (EnWAS), where we examine 139 neighbourhood-level environmental exposures in relation to well-being.MethodsFirst, we performed a generalized estimation equation regression (N = 11,975) to test for the effects of environmental exposures on well-being. Second, to account for multicollinearity amongst exposures, we performed principal component regression. Finally, using a genetically informative design, we examined whether environmental exposure is driven by genetic predisposition for well-being.ResultsWe identified 21 environmental factors that were associated with well-being in the domains: housing stock, income, core neighbourhood characteristics, livability, and socioeconomic status. Of these associations, socioeconomic status and safety are indicated as the most important factors to explain differences in well-being. No evidence of gene-environment correlation was found.SignificanceThese observed associations, especially neighbourhood safety, could be informative for policy makers and provide public policy guidance to improve well-being. Our results show that linking databases is a fruitful exercise to identify determinants of mental health that would remain unknown by a more unilateral approach.
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In this study we use aggregated weighted scores of environmental effects to study environmental influences on well-being and happiness. To this end, we split a sample of Netherlands Twin Register (NTR) participants into a training (N =4857) and test (N =2077) sample. In the training sample, we use elastic net regression to estimate effect sizes for associations between life satisfaction and two sets of environmental variables: one based on self- report socioenvironmental data, and one based on objective physical environmental data. Based on these effect sizes, we create two poly-environmental scores (PES-S and PES-O, for self-reports and objective data respectively). In the test sample, we perform association analyses between different measures of well-being and the two PESs. We find that the PES-S explains ~36% of the variance in well-being, while the PES-O does not significantly contribute to the model. Variance in other well-being measures (i.e., different life satisfaction domains, subjective happiness, quality of life, flourishing, psychological well-being, self-rated health, depressive problems, and loneliness) are explained to varying extents by the PESs, ranging from 6.36% (self-rated health) to 36.66% (loneliness). These predictive values did not change during the COVID-19 pandemic (N =3214). Validating the PES-S in the UK biobank (N =40,614), we find that the UK biobank PES-S explains about ~12% of the variance in happiness. Lastly, we examine if there is any indication for gene-environment correlation (rGE), the phenomenon where one’s genetic predisposition influences exposure to the environment, by associating the PESs with polygenic scores (PGS) in a sample of Netherlands Twin Register (NTR) and UK Biobank participants. While the PES and PGS were not correlated in the NTR sample, they were correlated in the larger UK biobank sample, indicating the potential presence of rGE. We discuss several limitations pertaining to our dataset, such as a potential influence of common method bias, and reflect on how PESs might be used in future research.
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AimsGenetic hypertrophic cardiomyopathy (HCM) is caused by mutations in sarcomere protein-encoding genes (i.e. genotype-positive HCM). In an increasing number of patients, HCM occurs in the absence of a mutation (i.e. genotype-negative HCM). Mitochondrial dysfunction is thought to be a key driver of pathological remodelling in HCM. Reports of mitochondrial respiratory function and specific disease-modifying treatment options in patients with HCM are scarce.Methods and resultsRespirometry was performed on septal myectomy tissue from patients with HCM (n = 59) to evaluate oxidative phosphorylation and fatty acid oxidation. Mitochondrial dysfunction was most notably reflected by impaired NADH-linked respiration. In genotype-negative patients, but not genotype-positive patients, NADH-linked respiration was markedly depressed in patients with an indexed septal thickness ≥10 compared with <10. Mitochondrial dysfunction was not explained by reduced abundance or fragmentation of mitochondria, as evaluated by transmission electron microscopy. Rather, improper organization of mitochondria relative to myofibrils (expressed as a percentage of disorganized mitochondria) was strongly associated with mitochondrial dysfunction. Pre-incubation with the cardiolipin-stabilizing drug elamipretide and raising mitochondrial NAD+ levels both boosted NADH-linked respiration.ConclusionMitochondrial dysfunction is explained by cardiomyocyte architecture disruption and is linked to septal hypertrophy in genotype-negative HCM. Despite severe myocardial remodelling mitochondria were responsive to treatments aimed at restoring respiratory function, eliciting the mitochondria as a drug target to prevent and ameliorate cardiac disease in HCM. Mitochondria-targeting therapy may particularly benefit genotype-negative patients with HCM, given the tight link between mitochondrial impairment and septal thickening in this subpopulation.
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IntroductionOver time, surrogacy has become more broadly available to a variety of people (e.g. male same-sex couples or transgender women). Whether the wider public supports surrogacy, and what contributes to such support remains unclear. This study investigated what demographic and surrogacy arrangement-based (which people participate in the arrangement) factors shape attitudes towards surrogacy.MethodA representative sample of Dutch adults (N = 1,074) reported their attitudes on four (out of 30) randomly assigned vignettes in 2023. Each vignette described a surrogacy family with variations in sexuality and gender of parents, the social and genetic bonds between the parents, the surrogate, and the oocyte donor, and was followed by an attitude questionnaire (6 items). Multilevel regression analyses were conducted with attitudes as the dependent variable and demographic factors (gender, Dutch background, age, education, sexual orientation, urbanisation, and religiosity) and arrangement-based factors (parental composition, genetic and social bonds with the surrogate, and oocyte donors).ResultsParticipants held fairly positive attitudes towards surrogacy. People identifying as women, with only having a Dutch background, who were younger, more highly educated, non-heterosexual, or less religious were more likely to have positive attitudes. Participants had more positive attitudes if surrogacy arrangements entailed cis-man cis-woman parents compared to cis-man cis-man or transgender parents, and when there was no social bond between parents and oocyte donor.ConclusionsAttitudes are influenced by both demographic and arrangement-based factors. Based on these findings, families can be informed of fairly positive reactions they might encounter from their environment.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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