Characterization of hydrophobin proteins at interfaces and in solutions using x rays

Hydrophobins are a group of particularly surface active proteins. The surface activity is demonstrated in the ready adsorption of hydrophobins to hydrophobic/hydrophilic interfaces such as the air/water interface. Adsorbed hydrophobins self-assemble into ordered films, lower the surface tension of water, and stabilize air bubbles and foams. Hydrophobin proteins originate from filamentous fungi. In the fungi the adsorbed hydrophobin films enable the growth of fungal aerial structures, form protective coatings and mediate the attachment of fungi to solid surfaces. This thesis focuses on hydrophobins HFBI, HFBII, and HFBIII from a rot fungus Trichoderma reesei. The self-assembled hydrophobin films were studied both at the air/water interface and on a solid substrate. In particular, using grazing-incidence x-ray diffraction and reflectivity, it was possible to characterize the hydrophobin films directly at the air/water interface. The in situ experiments yielded information on the arrangement of the protein molecules in the films. All the T. reesei hydrophobins were shown to self-assemble into highly crystalline, hexagonally ordered rafts. The thicknesses of these two-dimensional protein crystals were below 30 Å. Similar films were also obtained on silicon substrates. The adsorption of the proteins is likely to be driven by the hydrophobic effect, but the self-assembly into ordered films involves also specific protein-protein interactions. The protein-protein interactions lead to differences in the arrangement of the molecules in the HFBI, HFBII, and HFBIII protein films, as seen in the grazing-incidence x-ray diffraction data. The protein-protein interactions were further probed in solution using small-angle x-ray scattering. Both HFBI and HFBII were shown to form mainly tetramers in aqueous solution. By modifying the solution conditions and thereby the interactions, it was shown that the association was due to the hydrophobic effect. The stable tetrameric assemblies could tolerate heating and changes in pH. The stability of the structure facilitates the persistence of these secreted proteins in the soil.

Nonlinear mixture autoregressive model: economic applications

The aim of this dissertation is to model economic variables by a mixture autoregressive (MAR) model. The MAR model is a generalization of linear autoregressive (AR) model. The MAR -model consists of K linear autoregressive components. At any given point of time one of these autoregressive components is randomly selected to generate a new observation for the time series. The mixture probability can be constant over time or a direct function of a some observable variable. Many economic time series contain properties which cannot be described by linear and stationary time series models. A nonlinear autoregressive model such as MAR model can a plausible alternative in the case of these time series. In this dissertation the MAR model is used to model stock market bubbles and a relationship between inflation and the interest rate. In the case of the inflation rate we arrived at the MAR model where inflation process is less mean reverting in the case of high inflation than in the case of normal inflation. The interest rate move one-for-one with expected inflation. We use the data from the Livingston survey as a proxy for inflation expectations. We have found that survey inflation expectations are not perfectly rational. According to our results information stickiness play an important role in the expectation formation. We also found that survey participants have a tendency to underestimate inflation. A MAR model has also used to model stock market bubbles and crashes. This model has two regimes: the bubble regime and the error correction regime. In the error correction regime price depends on a fundamental factor, the price-dividend ratio, and in the bubble regime, price is independent of fundamentals. In this model a stock market crash is usually caused by a regime switch from a bubble regime to an error-correction regime. According to our empirical results bubbles are related to a low inflation. Our model also imply that bubbles have influences investment return distribution in both short and long run.