Myalgic Encephalomyelitis Research Group

Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging.IMPORTANCE Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person's age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site's microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases.

The creation of good machine learning models relies on the availability of good training data. In coalition settings, this training data may be obtained from many different coalition partners. However, due to the difference in the trust level of the coalition partners, the value of the information provided by the coalition partners could be questionable. In this paper, we examine the concept of Value of Information, provide a quantitative measure for it, and show how this can be used to determine the policies for information fusion in the training of machine learning models.

Generative policies have been proposed as a mechanism to learn the constraints and preferences of a system—especially complex systems such as the ones found in coalitions—in a given context so that the system can adapt to unexpected changes seamlessly, thus achieving the system goals with minimal human intervention. Generative policies can help a coalition system to be more effective when working in a distributed, continuously transforming environment with a diverse set of members, resources, and tasks. Learning mechanisms based on logic programming, e.g., Inductive Logic Programming (ILP), have several properties that make them suitable and attractive for the creation and adaptation of generative policies, such as the ability to learn a general model from a small number of examples, and being able to incorporate existing background knowledge. ILP has recently been extended with the introduction of systems for Inductive Learning of Answer Set Programs (ILASP) which are capable of supporting automated acquisition of complex knowledge such as constraints, preferences and rule-based models. Motivated by the capabilities of ILASP, we present AGENP, an Answer Set Grammar-based Generative Policy Framework for Autonomous Managed Systems (AMS) that aims to support the creation and evolution of generative policies by leveraging ILASP. We describe the framework components, i.e., inputs, data structures, mechanisms to support the refinement and instantiation of policies, identification of policy violations, monitoring of policies, and policy adaptation according to changes in the AMS and its context. Additionally, we present the main work-flow for the global and local refinement of policies and their adaptation based on Answer Set Programming (ASP) for policy representation and reasoning using ILASP. We then discuss an application of the AGENP framework and present preliminary results.

Non-equilibrium molecular dynamics simulations are widely employed to study transport fluid properties. Observables measured at the atomistic level can serve as inputs for continuum calculations, allowing for improved analysis of phenomena involving multiple scales. In hybrid modelling, uncertainties present in the information transferred across scales can have a significant impact on the final predictions. This work shows the influence of force-field variability on molecular measurements of the shear viscosity of water. In addition, the uncertainty propagation is demonstrated by quantifying the sensitivity of continuum velocity distribution to the particle-based calculations. The uncertainty is modelled with polynomial chaos expansion using a non-intrusive spectral projection strategy. The analysis confirms that low-order polynomial basis are sufficient to calculate the dispersion of observables.