Past Events

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Peg Coleman: Incorporating Food and Gut Microbiota into 21st Century Risk Analysis

The advances of the microbiome revolution of the past decade have deeply challenged our prior understanding of microbes in human systems biology in health and disease. There is zero uncertainty that microbes in the 21st century are now understood as symbionts ‘completing’ the human ‘superorganism‘ or ‘holobiont’ (Homo sapiens plus microbial partners in health) rather than germs that will kill us. Yet, our current frameworks for Risk Analysis exclude our microbial partners in health! This lecture will address microbes in health and disease, focusing on the gut, the gut-lung axis, and the respiratory system, as well as strategies for managing our microbes for health and protection from disease. Recently published case studies are introduced that provide evidence maps on benefit-risk analysis for mammalian milks, both fresh unprocessed (raw) and pasteurized breastmilk and cow milk. Dialogue about potential partners in the work of incorporating food and gut microbiota into 21st century Risk Analysis will close the lecture.

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Georg Schollmeyer

In many applied situations of regression analysis the variables one is actually interested in can not be directly observed or can not be observed in the resolution that is actually needed. This can for instance be due to a censoring or a coarsening of the data, or it can be due to measurement error, etc. In such situations, without further assumptions about the censoring- or coarsening process, or without additional knowledge about the measurement error, the obtained statistical model is usually only partially identified, which means that the underlying true regression parameters can not be estimated consistently. Therefore, within the methodology of partial identification, one does not try to estimate the true parameter, but instead one estimates so-called identification regions, which are subsets of the parameter space that contain all parameters that cannot be excluded with an infinite amount of observed data and the imposed model assumptions. In this talk, I would like to present certain identification regions in the context of (multiple) linear regression for the case where the outcome variable, the covariate(s), or both can only be observed in intervals. After discussing the case of interval-valued outcomes where different identification regions arise due to different imposed model assumptions, I would like to speak about the more difficult case where the covariates (and possibly also the outcomes) are interval-valued.

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