Associations of serum 25-hydroxyvitamin D and subjective sleep measures in an arctic population: Insights from the population-based Tromsø Study.

Objective: To investigate the relation between serum 25-hydroxyvitamin D (s-25(OH)D) and subjective sleep measures in an Arctic population (69°N). Methods: Cross-sectional data was collected from 21,083 individuals (aged ≥40 years) participating in the population based Tromsø Study: Tromsø7 (2015-2016). The present study included 20,438 participants, after having excluded respondents missing data on s-25(OH)D (n = 161) and/or subjective sleep measures (including sleep duration, insomnia, and daytime sleepiness)(n = 490). Based on s-25(OH)D (assessed using LC-MS/MS), participants were grouped as deficient (<30 nmol/L), insufficient (30-49.9 nmol/L), sufficient (50-75 nmol/L), or high (>75 nmol/L). Sleep duration was grouped as inadequate (ISD) if < 7 or ≥9 h. Linear and logistic regression were used to calculate unstandardized ß-values and odds ratios [95% confidence intervals]. The analyses were adjusted for season, age, BMI, lifestyle factors and relevant comorbidities. Results: In both men and women, s-25(OH)D was positively associated with sleep duration, and compared to the sufficient s-25(OH)D group, the insufficient s-25(OH)D group reported significantly shorter sleep duration in both sexes. There was an increased odds of ISD in both men and women but adjusted for confounding factors this was only significant in women (1.16 [1.03, 1.32], p = .017). In men, there were no significant associations between s-25(OH)D and the remaining sleep measures. Women in the high s-25(OH)D group had lower ESS-scores (-0.28 [-0.47, -0.08], p = .006), but higher odds of insomnia (1.16 [1.01, 1.33], p = .036) compared to women in the sufficient group. Conclusions: In this Arctic population, a tenuous association was found between s-25(OH)D and subjective sleep measures, predominantly in women.

Spatial quantification of clinical biomarker pharmacokinetics through deep learning-based segmentation and signal-oriented analysis of MSOT data.

Although multispectral optoacoustic tomography (MSOT) significantly evolved over the last several years, there is a lack of quantitative methods for analysing this type of image data. Current analytical methods characterise the MSOT signal in manually defined regions of interest outlining selected tissue areas. These methods demand expert knowledge of the sample anatomy, are time consuming, highly subjective and prone to user bias. Here we present our fully automated open-source MSOT cluster analysis toolkit Mcat that was designed to overcome these shortcomings. It employs a deep learning-based approach for initial image segmentation followed by unsupervised machine learning to identify regions of similar signal kinetics. It provides an objective and automated approach to quantify the pharmacokinetics and extract the biodistribution of biomarkers from MSOT data. We exemplify our generally applicable analysis method by quantifying liver function in a preclinical sepsis model whilst highlighting the advantages of our new approach compared to the severe limitations of existing analysis procedures.

Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network.

In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as in silico and in vitro information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.

Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review.

In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that possess unique methodical features to model dependencies in complex networks, such as forward and backward propagation (inference) of disruptions. BNs have transitioned from an emerging topic to a growing research area in supply chain (SC) resilience and risk analysis. As a result, there is an acute need to review existing literature to ascertain recent developments and uncover future areas of research. Despite the increasing number of publications on BNs in the domain of SC uncertainty, an extensive review on their application to SC risk and resilience is lacking. To address this gap, we analyzed research articles published in peer-reviewed academic journals from 2007 to 2019 using network analysis, visualization-based scientometric analysis, and clustering analysis. Through this study, we contribute to literature by discussing the challenges of current research, and, more importantly, identifying and proposing future research directions. The results of our survey show that further debate on the theory and application of BNs to SC resilience and risk management is a significant area of interest for both academics and practitioners. The applications of BNs, and their conjunction with machine learning algorithms to solve big data SC problems relating to uncertainty and risk, are also discussed.