An Analysis of the Linguistic Features of The Minister’s Black Veil from the Perspective of Literary Pragmatics

The Minister’s Black Veil is one of the most classic short stories written by American romantic writer Nathaniel Hawthorne (1804-1864), From the perspective of literary pragmatics, this paper analyzes the language features of the novel, such as words and sentences, grammar, semantic ambiguity, rhetoric and conversational implicature based on cooperative principle, so as to explore the superb writing style and literary art of the novel, better understand and appreciate this literary work, and provide a new perspective and reference for the study of British and American literature Direction

Synthesis and Evaluation of New Antimicrobial Agents and Novel Organic Fluorophores

As a result of the evolution of antimicrobial resistant bacteria there is a pressing need for novel classes of antibiotics. This project aimed, in part, to design and synthesise a new family of pyrazole ligands, their zinc(II) complexes and to evaluate their antmicrobial activity. The overall goal was to improve the antimicrobial activity of both ligand and metal salt through the formation of the corresponding zinc complex. The first pyrazole ligands synthesized were unable to coordinate with zinc(II) salts. We believe this was due to steric hindrance generated by the relatively large pyrazaole ligands. Several smaller pyrazole ligands were synthesized, some of which successfully complexed with zinc suggesting that steric hindrance had indeed been a factor for the first set of ligands. A family of zinc complexes were generated employing the family of smaller pyrazole liagnds. All the pyrazole-zinc complexes, pyrazole ligands and zinc salts were evaluated for activity against S.aureus and E.coli. Generally, the susceptibility test results showed that most pyrazole ligands did not exhibit potent activity against either E. coli or S. aureus. All of the zinc complexes exhibited good antibacterial activity against both E. coli and S. aureus at 100 μg/mL. As we had expected the zinc complexes greatly improved the antibacterial activity of the pyrazole ligands. However, most zinc complexes were as active as the zinc salts alone indicating that the addition of the pyrazole ligands did not improve the activity of the zinc salts. Organosulfones are widely used in the field of pharmaceuticals and ploymers. Traditional methodologies for synthesizing biphenyl sulfones include Suzuki-Miyaura coupling, Friedel-Crafts and other multistep reactions. These methodologies require catalysts, solvents and some require long reaction times. We have developed a novel solvent-free methodology for synthesizing fluorescent biphenyl sulfones with a relatively short reaction time and in good yields. This methodology exploits an interesting electrocyclisation of bissulfonyl trienes. A new family of substituted biphenyl sulfones resulted. UV-absorption, emission and excitation spectra were generated for the family of biphenyl sulfones and their photophysical characterisitics studied (e.g. Stokes shift, quantum yield, molar extinction coefficients). The biphenyl sulfones with NO2 substituents did not exhibit fluorescence due to the strong electron withdrawing nature of the NO2 group. Other substituted biphenyl sulfones exhibited highly solvatochromic emissions, probably via twisted intramolecular charge transfer (TICT) states. The biphenyl N,N-dimethyl-4'-(phenylsulfonyl)-[1,1'-biphenyl]-4-amine was found to show a very high quantum yield in toluene and dichloromethane (Φ close to 0.9); large Stokes shifts and high molar extinction coefficient in ethylene glycol (ε > 80 000 M-1cm-1). The HOMO-LUMO gaps of a family biphenyl sulfones were plotted against their Stokes shifts (in chloroform) giving an excellent linear correlation (R2 = 0.9978). These results suggested that the underlying photophysical processes are similar for all our biphenyl sulfones, where stronger electron donating groups generate smaller HOMO-LUMO gaps and red-shifted emissions, as compared to weaker electron donating groups

Statistical Learning Methods for Personalized Medicine

The theme of this dissertation is to develop simple and interpretable individualized treatment rules (ITRs) using statistical learning methods to assist personalized decision making in clinical practice. Considerable heterogeneity in treatment response is observed among individuals with mental disorders. Administering an individualized treatment rule according to patient-specific characteristics offers an opportunity to tailor treatment strategies to improve response. Black-box machine learning methods for estimating ITRs may produce treatment rules that have optimal benefit but lack transparency and interpretability. Barriers to implementing personalized treatments in clinical psychiatry include a lack of evidence-based, clinically interpretable, individualized treatment rules, a lack of diagnostic measure to evaluate candidate ITRs, a lack of power to detect treatment modifiers from a single study, and a lack of reproducibility of treatment rules estimated from single studies. This dissertation contains three parts to tackle these barriers: (1) methods to estimate the best linear ITR with guaranteed performance among the class of linear rules; (2) a tree-based method to improve the performance of a linear ITR fitted from the overall sample and identify subgroups with a large benefit; and (3) an integrative learning combining information across trials to provide an integrative ITR with improved efficiency and reproducibility. In the first part of the dissertation, we propose a machine learning method to estimate optimal linear individualized treatment rules for data collected from single stage randomized controlled trials (RCTs). In clinical practice, an informative and practically useful treatment rule should be simple and transparent. However, because simple rules are likely to be far from optimal, effective methods to construct such rules must guarantee performance, in terms of yielding the best clinical outcome (highest reward) among the class of simple rules under consideration. Furthermore, it is important to evaluate the benefit of the derived rules on the whole sample and in pre-specified subgroups (e.g., vulnerable patients). To achieve both goals, we propose a robust machine learn- ing algorithm replacing zero-one loss with an authentic approximation loss (ramp loss) for value maximization, referred to as the asymptotically best linear O-learning (ABLO), which estimates a linear treatment rule that is guaranteed to achieve optimal reward among the class of all linear rules. We then develop a diagnostic measure and inference procedure to evaluate the benefit of the obtained rule and compare it with the rules estimated by other methods. We provide theoretical justification for the proposed method and its inference procedure, and we demonstrate via simulations its superior performance when compared to existing methods. Lastly, we apply the proposed method to the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial on major depressive disorder (MDD) and show that the estimated optimal linear rule provides a large benefit for mildly depressed and severely depressed patients but manifests a lack-of-fit for moderately depressed patients. The second part of the dissertation is motivated by the results of real data analysis in the first part, where the global linear rule estimated by ABLO from the overall sample performs inadequately on the subgroup of moderately depressed patients. Therefore, we aim to derive a simple and interpretable piece-wise linear ITR to maintain certain optimality that leads to improved benefit in subgroups of patients, as well as the overall sample. In this work, we propose a tree-based robust learning method to estimate optimal piece-wise linear ITRs and identify subgroups of patients with a large benefit. We achieve these goals by simultaneously identifying qualitative and quantitative interactions through a tree model, referred to as the composite interaction tree (CITree). We show that it has improved performance compared to existing methods on both overall sample and subgroups via extensive simulation studies. Lastly, we fit CITree to Research Evaluating the Value of Augmenting Medication with Psychotherapy (REVAMP) trial for treating major depressive disorders, where we identified both qualitative and quantitative interactions and subgroups of patients with a large benefit. The third part deals with the difficulties in the low power of identifying ITRs and replicating ITRs due to small sample sizes of single randomized controlled trials. In this work, a novel integrative learning method is developed to synthesize evidence across trials and provide an integrative ITR that improves efficiency and reproducibility. Our method does not require all studies to collect a common set of variables and thus allows information to be combined from ITRs identified from randomized controlled trials with heterogeneous sets of baseline covariates collected from different domains with different resolution. Based on the research goal, the integrative learning can be used to enhance a high-resolution ITR by borrowing information from coarsened ITRs or improve the coarsened ITR from a high-resolution ITR. With a simple modification, the proposed integrative learning can also be applied to improve the estimation of ITRs for studies with blockwise missing feature variables. We conduct extensive simulation studies to show that our method has improved performance compared to existing methods where only single-trial ITRs are used to learn personalized treatment rules. Lastly, we apply the proposed method to RCTs of major depressive disorder and other comorbid mental disorders. We found that by combining information from two studies, the integrated ITR has a greater benefit and improved efficiency compared to single-trial rules or universal non-personalized treatment rule