Identification of tissue optical properties during thermal laser-tissue interactions: An ensemble Kalman filter-based approach.

In this article, we propose a computational framework to estimate the physical properties that govern the thermal response of laser-irradiated tissue. We focus in particular on two quantities, the absorption and scattering coefficients, which describe the optical absorption of light in the tissue and whose knowledge is vital to correctly plan medical laser treatments. To perform the estimation, we utilize an implementation of the ensemble Kalman filter (EnKF), a type of Bayesian filtering algorithm for data assimilation.

Analyzing the effects of observation function selection in ensemble Kalman filtering for epidemic models.

The Ensemble Kalman Filter (EnKF) is a popular sequential data assimilation method that has been increasingly used for parameter estimation and forecast prediction in epidemiological studies. The observation function plays a critical role in the EnKF framework, connecting the unknown system variables with the observed data. Key differences in observed data and modeling assumptions have led to the use of different observation functions in the epidemic modeling literature.

Modeling Microglia Activation and Inflammation-Based Neuroprotectant Strategies During Ischemic Stroke.

Neural inflammation immediately follows the onset of ischemic stroke. During this process, microglial cells can be activated into two different phenotypes: the M1 phenotype, which can worsen brain injury by producing pro-inflammatory cytokines; or the M2 phenotype, which can aid in long term recovery by producing anti-inflammatory cytokines. In this study, we formulate a nonlinear system of differential equations to model the activation of microglia post-ischemic stroke, which includes bidirectional switching between the microglia phenotypes, as well as the interactions between these cells and the cytokines that they produce.

Evaluation of the gut microbiome in association with biological signatures of inflammation in murine polytrauma and shock.

Severe injuries are frequently accompanied by hemorrhagic shock and harbor an increased risk for complications. Local or systemic inflammation after trauma/hemorrhage may lead to a leaky intestinal epithelial barrier and subsequent translocation of gut microbiota, potentially worsening outcomes. To evaluate the extent with which trauma affects the gut microbiota composition, we performed a post hoc analysis of a murine model of polytrauma and hemorrhage.

Practical identifiability and uncertainty quantification of a pulsatile cardiovascular model.

Mathematical models are essential tools to study how the cardiovascular system maintains homeostasis. The utility of such models is limited by the accuracy of their predictions, which can be determined by uncertainty quantification (UQ). A challenge associated with the use of UQ is that many published methods assume that the underlying model is identifiable (e.

Activity of Shoulder Stabilizers and Prime Movers During an Unstable Overhead Press.

Williams, MR Jr, Hendricks, DS, Dannen, MJ, Arnold, AM, and Lawrence, MA. Activity of shoulder stabilizers and prime movers during an unstable overhead press. J Strength Cond Res 34(1): 73-78, 2020-Overhead reaching is a common movement that relies heavily on muscles for dynamic stability.

Uncertainty Quantification in a Patient-Specific One-Dimensional Arterial Network Model: EnKF-Based Inflow Estimator.

Successful clinical use of patient-specific models for cardiovascular dynamics depends on the reliability of the model output in the presence of input uncertainties. For 1D fluid dynamics models of arterial networks, input uncertainties associated with the model output are related to the specification of vessel and network geometry, parameters within the fluid and wall equations, and parameters used to specify inlet and outlet boundary conditions. This study investigates how uncertainty in the flow profile applied at the inlet boundary of a 1D model affects area and pressure predictions at the center of a single vessel.

Astrocytic tracer dynamics estimated from [1-¹¹C]-acetate PET measurements.

We address the problem of estimating the unknown parameters of a model of tracer kinetics from sequences of positron emission tomography (PET) scan data using a statistical sequential algorithm for the inference of magnitudes of dynamic parameters. The method, based on Bayesian statistical inference, is a modification of a recently proposed particle filtering and sequential Monte Carlo algorithm, where instead of preassigning the accuracy in the propagation of each particle, we fix the time step and account for the numerical errors in the innovation term. We apply the algorithm to PET images of [1-¹¹C]-acetate-derived tracer accumulation, estimating the transport rates in a three-compartment model of astrocytic uptake and metabolism of the tracer for a cohort of 18 volunteers from 3 groups, corresponding to healthy control individuals, cirrhotic liver and hepatic encephalopathy patients.