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Introduction: The influence of our diet on mental health is of increasing importance in current research. Study results on the gut-brain axis suggest that the gut microbiome can influence mental processes via neuronal, hormonal and immune signaling pathways [1]. The gut microbiome is largely influenced by our diet. Some studies provide evidence that a "Western diet" rich in saturated fat and sugar may promote mental disorders [2]. There is evidence, that dietary behaviour in individuals with Attention Deficit Hyperactivity Disorder (ADHD) is characterized by an increased intake of sugar and saturated fat [3]. So far, it is unclear whether this dietary pattern contributes to ADHD symptoms such as impulsivity. The aim of this study is to investigate the influence of certain macronutrients such as fats and mono/disaccharides on impulsivity in individuals with ADHD. Using our APPetite-mobile-app [4] enabled us to study dietary behaviour and momentary impulsiveness in everyday life of our participants.
Methods: 43 participants with ADHD (mean age 36.0 ± 12.3 years, 21 females) and 186 healthy controls (mean age 28.5 ± 7.7 years, 133 females) without any psychiatric condition were included into the study. Food intake was recorded over a period of three days using the APPetite-mobile-app via a 6 step process: (1) Selection of meal type, (2) Entry of time of meal, (3) Selection of consumed foods and drinks, (4) Specification of consumed amounts, (5) Presentation of reminder for commonly forgotten foods, and (6) Indication of predominant reason for eating. In addition to entering consumed foods in the APPetite-mobile-app, subjects completed an online food log for the last 24 hours (myfood 24) at the beginning of the study. After the data collection period, a detailed analysis of the ingested nutrients was performed for each subject. Trait impulsivity was assessed using the UPPS-P, a self-assessment questionnaire. Momentary impulsiveness was assessed via the mHealth APP by means of the Momentary Impulsiveness scale (MIS). The MIS consists of 4 questions capturing different aspects of impulsivity. The participants were prompted to answer these questions at 8 semi-random times per day between 8 AM and 10 PM. The minimum time between 2 prompts was 1 hour. Thereby participants could not predict the exact time of the next prompt and the assessed situations are a better reflection of the participant’s real life.
Results: ANOVA revealed higher levels of both, trait and momentary impulsivity in individuals with ADHD compared to controls (p < 0,01). After preprocessing of data that was sampled via the mHealth APP is completed, a regression analysis with different macronutrients as predictors and impulsivity as dependent variable will be computed. To assess the association between momentary impulsiveness and dietary intake, generalized linear multilevel modelling will be used. Results of these analyses will be presented.
Collective flow phenomena are a sensitive probe for the properties of extreme QCD matter. However, their interpretation relies on the understanding of the initial conditions e.g. the eccentricity of the nuclear overlap region. HADES [1] provides a large acceptance combined with a high mass-resolution and therefore allows to study di-electron and hadron production in heavy-ion collisions with unprecedented precision. In this contribution, the capability of HADES to study flow harmonics by utilizing multi-particle azimuthal correlation techniques is discussed. Due to the high statistics of seven billion Au+Au collisions at 1.23 AGeV collected in 2012, a systematic study of higher-order flow harmonics, the differentiation between collective and non-flow effects, and as well the multi-differential (pt, rapidity, centrality) analysis is possible.
We show examples of the impact of the Maxwellian averaged capture cross sections determined at n_TOF over the past 20 years on AGB stellar nucleosynthesis models. In particular, we developed an automated procedure to derive MACSs from evaluated data libraries, which are subsequently used as input to stellar models computed by means of the FuNS code. In this contribution, we present a number of s-process abundances obtained using different data libraries as input to stellar models, with a focus on the role of n_TOF data.