Kinetics of an Inverse Temperature Transition Process and Its Application on Supported Lipid Bilayer

This dissertation focuses on the study of inverse temperature transition processes of the poly(N-isopropylacrylamide) (PNIPAM) and the elastin-like polypeptides (ELPs). A novel temperature jump microfluidic system is introduced and this system shows the ability to measure the kinetics of the PNIPAM and the ELPs collapse without a heat transfer problem. The conformational change of the ELPs during the phase transition process is utilized as a nanoscale protein filter to modulate ligandreceptor binding events on supported lipid bilayers (SLBs). This research study is divided into three main parts. The first part is the development of the temperature jump microfluidics. The kinetics of PNIPAM collapse is used as a model system to show the capability of this new device to measure millisecond time scale phase transition processes. The effects of salts on the kinetics of PNIPAM collapse are also shown in this part. To our knowledge, this is the first study which shows the effects of salts on PNIPAM collapse kinetics. The second part of this research is the application of the novel temperature jump microfluidics. The hydrophobic collapse of ELPs composed of identical sequence but different chain length is investigated. By controlling the molecular weight of the ELPs, the thermodynamic contributions from intermolecular hydrophobic interactions, and intramolecular hydrophobic interactions could be calculated individually for this unique system. The third part is the application of the phase transition property of ELPs. The ELPs are conjugated on the surface of the SLBs as a nanoscale protein filter. The conformation of the ELPs can be modulated by ionic strength of the buffer solution or ambient temperature. The ELPs conjugated SLBs platform showed the ability to block IgG binding to biotin conjugated on the SLBs when the ELPs were in the extended coil state and open the access for protein to bind to biotin in compact globule conformation

A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.