Guest lectures and seminars - Page 46

Abstract:  The cerebral circulation must ensure continuous blood perfusion of the brain which is the biggest oxygen consumer in the human body. It must also provide mechanisms for adaptability to changing oxygen demand as well as resilience to local blockages. We will look at such mechanisms at the level of the microcirculation where the mechanics of blood flow is dominated by red blood cells. We will find that red blood cells do not only play the role of oxygen carriers, but that they are an important element of blood flow regulation itself. To this end, we will compare results from in vitro studies in microfluidic chips to theoretical and computational models and to in vivo data from mice. We will derive local auto-regulation mechanisms for blood flow and will study how local modifications in the vascular network can modify the global hematocrit distribution. These results will emphasize the relevance of red blood cell mechanics and microvascular network geometry in cerebral blood perfusion.

This talk is part of the Mechanics Lunch Seminar series. Bring-your-own-lunch and lots of questions.

Abstract:  Swimming bacteria, growing cell tissues, molecular motors, and microtubule systems confined to a substrate are examples of active matter films that exhibit long-range nematic (orientational) order. Intrinsic activity in these systems builds mechanical stresses that tend to destroy local nematic order through topological defects, which act as sources of persistent active flows.  The overall evolution and functionality of biological matter is greatly influenced by these orientational defects. Yet, their formation and dynamics are driven by a complex interplay between topological singularities in the nematic order and active flow instabilities, and this is not completely understood. 

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For many real-life phenomena one may assume that the units of observation, typically patients, transition through a set of discrete states on their way towards an absorbing state. The states often constitute various stages of a disease, from perfect health through various stages of dementia for example. Multi-state models are a class of statistical models which allow us to study the time spent in different states, the probability of transitioning between states, and the relationship between these quantities and covariates of interest. In many applications the transition times between states are not observed exactly; instead, the current state of the patients is queried at arbitrary times. The transition times are therefore interval censored, and this makes inference and modelling challenging. Most current approaches are based on the Markov assumption, for example the simplest parametric model available - the time-homogeneous Markov model. Here, we propose a new, general framework for parametric inference with interval censored multi-state data. Our models allow non-Markovian behaviour. I will present the framework and an algorithm for the automatic construction of the likelihood function, along with real-data examples. This talk is based on joint work with Marthe Aastveit and Nils Lid Hjort.