Investigation of SNARE mediated membrane fusion and its regulation by optimized single molecule method

Neurotransmitter release going through synaptic vesicle cycle is one key step how signal is transported in our brains. The mechanism on molecular level has been under development and debated for decades. Many milestones have been made including, the identification of SNARE as core assembly machinery, the clarification of synaptotagmin as calcium sensor, the recognition of NSF and SNAP as disassembly apparatus, the determination of complexin and SM protein as regulatory protein. However, the sequence of their involvement in synaptic vesicle cycle, the relationship between the structure and psychological function, microscale fusion mechanism and are under further investigation. This puzzle is completing with effort from international groups and our group. Complexin as one regulatory protein, has been found owning both inhibitory and facilitatory function. This dual function adds more complication to identify the role of complexin in membrane fusion. Research groups get either inhibitory or facilitatory function based on the experimental condition, which is contradictory. Also, single molecule FRET mixing assay has been adopted widely as one method to isolate membrane fusion system in vitro to give more detailed information on step-by-step mechanism. One major method in single molecule FRET, content mixing, faces obstacles by slow time scale and low fusion percentage. By looking deeper into complexin function, we optimize content mixing and for the first time we observed complexin showing both inhibitory and facilitatory role in a concentration dependent manner

Novel techniques for measuring the effect of neighbouring bases on mutation and their applications

Understanding factors influencing mutations can improve detection of novel mutations, the diagnostic signatures of disease-causing mutagens, and facilitate the development of more accurate models of genetic divergence. Hypermutability of CpG demonstrates the existence of mutation motifs, sequences of flanking bases that influence point mutation processes. These motifs can also be indicative of specific underlying mutation mechanisms. I developed novel log-linear models for identifying mutation motifs that allow further comparisons of these mutation motifs, and of the complete mutation spectra between samples. Mutation motifs are visualised using a sequence logo type method. In this thesis, I applied the methods to examine each of the possible 12 point mutations in about 13.6 million human germline mutations (inferred from single-nucleotide polymorphisms recorded in the Ensembl database) and about 181,000 melanoma mutations from the COSMIC database. My method recovered the well-known CpG effect, which a conventional motif detection method failed to do. I established that all point mutations have significant and distinct mutation motifs. While the major effects of flanking bases lie within 2 bp of the mutated position, I refute previous reports that the effect magnitude decays monotonically with distance. Comparison between autosomes and X-chromosomes supported a reduced contribution from methylation-induced C to T mutation on the X-chromosome, consistent with a previous prediction. In addition, analyses of malignant melanoma confirmed reported characteristic features of this cancer, such as strand asymmetry of mutation processes. Further, I found that neighbouring influences in malignant melanoma differ significantly from those affecting germline mutations. Interestingly, for C to T mutation, the CpG effect was no longer evident, and was largely substituted by different neighbouring mechanisms. Moreover, the observed neighbouring influence is able to reflect the chemical influences of mutagenic processes after exposure to ultraviolet light. Based on this observation, I hypothesised that information regarding the mechanistic origin of point mutations is present in surrounding DNA sequences, and sequence neighbourhood can be used to identify the mechanistic origin of particular mutations. Machine learning classifiers were developed to assess the above hypothesis and discriminate between N-ethyl-N-nitrosourea (ENU)-induced and spontaneous point mutations in the mouse germline. ENU is a synthetic chemical employed in mutagenesis studies, introducing novel point mutations to genomes. My classification results reveal that a combination of k-mer size and representation of second-order interactions among nucleotides was able to improve classification performance compared to the naive classifier approach. In conclusion, this work demonstrates that neighbouring bases have a profound effect on the occurrence of mutations. The statistical methods reported in this research can be used to examine the role of flanking sequence on mutation processes from polymorphism data, which further enable identification of differences in the operation of mechanisms of mutation between genomic regions, cell types or species. In addition, the machine learning classification results have important implications for modelling context-dependent effects on sequence evolution

Domestic Extension Of Public Diplomacy: Media Competition For Credibility, Dependency And Activation Of Publics

This dissertation connects theories of political communication, public relations and international relations to conceptualize a new model of public diplomacy, where boundaries between distinct types of actors are drawn. It proposes an ecological model and a competition model of public diplomacy. Based on these conceptual models, it empirically supports the academic rationalization of governmental interference in foreign media effects among its domestic citizens: Using a quota sample of 560 survey respondent from mainland China, the empirical part of the dissertation illustrated: 1. Governmental control on foreign media accessibility has significant effects on perceived media credibility and thus dependency on it; 2. Availability of domestic media resource negatively impacts dependency on foreign media; and 3. Foreign media and domestic media, as currently conceptualized, have distinct effects on the psychological activeness of Chinese publics to speak out against social issue

Thermoresponsive Polymers with Aqueous LCST and/or UCST through Postpolymerization Modification

Thermoresponsive polymers with aqueous lower critical solution temperature (LCST) and/or upper critical solution temperature (UCST) are of a wider academic interest and also have promising applications in many areas. However, not many types of polymers with a UCST in water are well known. Postpolymerization modification is a versatile technique for synthesizing functional polymers. Herein, novel thermoresponsive polymers with UCST and/or LCST were prepared through postpolymerization modification. Given that strong intermolecular interactions are necessary in order to realise polymers with aqueous UCSTs, amide-functional (co)polymers were firstly investigated through postpolymerization modification of the activated ester poly(pentafluorophenyl acrylate) (pPFPA) with amines including ammonia and amide derivatives of amino acids. While it was not a promising route toward thermoresponsive polymers with UCST in water, novel (co)polymers with aqueous LCSTs and a remarkable self-association of (co)polymers in the dry state were found. Next, zwitterionic (co)polymers were investigated with the goal of producing polymers with tuneable UCST transitions in water. Initially, different synthetic techniques were compared, and postpolymerization modification of pPFPA with sulfobetaine-functional amines was identified as a very versatile method to tune the UCST behaviour by incorporation of hydrophobic comonomer units. As a consequence, a range of novel UCST systems including such with UCST under physiologically relevant NaCl concentrations were prepared and characterised. In addition to pPFPA, poly(2-vinyl-4,4-dimethylazlactone) (pVDMA) was used as a less investigated platform to prepare thermoresponsive polymers, and a range of novel VDMA-derived (co)polymers with tuneable aqueous LCST behaviour were successfully synthesized through postpolymerization modification of pVDMA with different amines. Based on the previous experience with sulfobetaine-functional amines, in the final project, novel VDMA-derived polymers with aqueous UCST were identified and studied, and copolymers with both LCST and UCST in water were designed and prepared through postpolymerization modification of pVDMA with sulfobetaine-functional amines and tetrahydrofurfurylamine. In summary, these studies have made a strong contribution to the field of novel thermoresponsive polymers and especially the ease of preparing zwitterionic copolymer, and UCST or UCST and LCST is expected to be significant in the wider scientific community

Essays In Asset Pricing

In the first chapter, ``A Unified Theory of the Term Structure and the Beta Anomaly\u27\u27, I propose a novel generalized framework which allows for disentangling agent\u27s risk aversion, elasticity of intertemporal substitution, and the agent\u27s preference for early or late resolution of uncertainty. I apply this framework to a consumption-based asset pricing model in which the representative agent\u27s consumption process is subject to rare but large disasters. The calibrated model matches major asset pricing moments, while higher exposure to systematic risks may lead to lower risk premia. This is consistent with empirical finding, while existing consumption-based asset pricing models fail to deliver. The second chapter, ``A Model of Two Days: Discrete News and Asset Prices\u27\u27, co-authored with Jessica A. Wachter, provides a quantitative model to address the macro-announcement premium. Empirical studies demonstrate striking patterns in stock returns related to scheduled macroeconomic announcements. A large proportion of the total equity premium is realized on days with macroeconomic announcements. The relation between market betas and expected returns is far stronger on announcement days as compared with non-announcement days. Finally, these results hold for fixed-income investments as well as for stocks. We present a model in which agents learn the probability of an adverse economic state on announcement days. We show that the model quantitatively accounts for the empirical findings. Evidence from options data provides support for the model\u27s mechanism

Machine Learning Techniques for Classifying the Mutagenic Origins of Point Mutations

There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include identifying population-specific mutagenesis and resolving distinct mutation signatures in cancer samples. Analyses for these applications assume that mutagenic mechanisms have a distinct relationship with neighboring bases that allows them to be distinguished. Direct support for this assumption is limited to a small number of simple cases, e.g., CpG hypermutability. We have evaluated whether the mechanistic origin of a point mutation can be resolved using only sequence context for a more complicated case. We contrasted single nucleotide variants originating from the multitude of mutagenic processes that normally operate in the mouse germline with those induced by the potent mutagen N-ethyl-N-nitrosourea (ENU). The considerable overlap in the mutation spectra of these two samples make this a challenging problem. Employing a new, robust log-linear modeling method, we demonstrate that neighboring bases contain information regarding point mutation direction that differs between the ENU-induced and spontaneous mutation variant classes. A logistic regression classifier exhibited strong performance at discriminating between the different mutation classes. Concordance between the feature set of the best classifier and information content analyses suggest our results can be generalized to other mutation classification problems. We conclude that machine learning can be used to build a practical classification tool to identify the mutation mechanism for individual genetic variants. Software implementing our approach is freely available under an open-source license