A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
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Item Effects of Protein Unfolding on Aggregation and Gelation in Lysozyme Solutions(MDPI, 2020-09-02) Nikfarjam, Shakiba; Jouravleva, Elena V.; Anisimov, Mikhail A.; Woehl, Taylor J.In this work, we investigate the role of folding/unfolding equilibrium in protein aggregation and formation of a gel network. Near the neutral pH and at a low buffer ionic strength, the formation of the gel network around unfolding conditions prevents investigations of protein aggregation. In this study, by deploying the fact that in lysozyme solutions the time of folding/unfolding is much shorter than the characteristic time of gelation, we have prevented gelation by rapidly heating the solution up to the unfolding temperature (~80 °C) for a short time (~30 min.) followed by fast cooling to the room temperature. Dynamic light scattering measurements show that if the gelation is prevented, nanosized irreversible aggregates (about 10–15 nm radius) form over a time scale of 10 days. These small aggregates persist and aggregate further into larger aggregates over several weeks. If gelation is not prevented, the nanosized aggregates become the building blocks for the gel network and define its mesh length scale. These results support our previously published conclusion on the nature of mesoscopic aggregates commonly observed in solutions of lysozyme, namely that aggregates do not form from lysozyme monomers in their native folded state. Only with the emergence of a small fraction of unfolded proteins molecules will the aggregates start to appear and grow.Item A META-DATA INFORMED EXPERT JUDGMENT AGGREGATION AND CALIBRATION TECHNIQUE(2016) Feldman, Ellis; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Policy makers use expert judgment opinions elicited from experts as probability distributions, quantiles or point estimates, as inputs to decisions that may have economic or life and death impacts. While challenges in estimating probabilities in general have been studied, research that distinguished between non-probabilistic, i.e., physical, variables and probabilistic variables specifically in the context of meta-data based expert judgment aggregation techniques, and the errors associated with the predictions developed from such variables, was not identified. This research demonstrated that for two combined expert judgment meta-data bases, the distinction between physical and probabilistic variables was significant in terms of the extent of multiplicative error between elicited medians and realized values both before and after aggregation. The distinction also impacts the widths of bounds around aggregated point estimates. The research compared nine methods of aggregating estimates and obtaining calibrated bounds, including ones based on alpha stable distributions, quantile regression, and a Bayesian model. Simple parametric distributions were also fit to the meta-data. Methods were compared against criteria including accuracy, bounds coverage and width, sensitivity to outliers, and complexity. No single method dominated all criteria for either variable type. The research investigated sensitivity of results to level of realized value for a variable, such as infrequent events for probabilistic variables, as well as sensitivity of results to number of experts.