Photophysical Studies of Multilayer Two-Dimensional Covalent Organic Frameworks

This thesis studies the photophysical properties of multilayer two-dimensional (2D) covalent organic frameworks (COFs). COFs are a type of crystalline porous polymer which allow the atomically precise incorporation of organic units to create structures with predesigned skeletons and nanopores. In all COFs the molecules are linked via covalent bonds within the single layers, which is an important characteristic that defines these materials. This leads to a variety of applications; for example, catalysis, gas storage, adsorption and optoelectronics. This thesis focuses on porphyrins to make COFs via a Schiff-base condensation reaction. Porphyrins have been studied for decades and have been shown to have interesting photophysical properties, such as the generation of triplet states and charge states. The synthesis of the COFs was mainly studied using scanning tunnelling microscopy (STM), and supported using Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The photophysical properties of the porphyrin COFs was studied using transient absorption spectroscopy (TAS). The STM results have shown a successful synthesis of 2D-COFs but the synthesis is not reliable nor reproducible which is a large stumbling block. The TAS results have shown a difference when comparing the porphyrins, and then COFs, in solution and as a multilayer film. In a solution the porphyrins exhibit triplet states. However, as a film the porphyrins and COFs exhibit charge states and subsequent bimolecular recombination. The generation of charge states was then compared between the porphyrin film precursor and subsequent COF film and the differences have been expanded upon in the thesis. Future work to improve the synthesis of the COF films and further photophysical studies have also been included

How Ontarians Experience the Law: An Examination on Incidence Rate, Seriousness and Response to Legal Problems

Access to civil justice is a conceptual framework that, at its most basic, claims all people are entitled to have their legal disputes resolved fairly. However, it is currently understood that these ideals are not reflected in the day-to-day realities of ordinary people. Though scholarship has examined ways in which to better allow for meaningful access to civil justice, there is still a need for further quantitative research especially from the Canadian perspective. This paper provides an empirical foundation to this discussion by examining the 2014 Cost of Justice project survey. Specifically, it examines the incidence rate of civil legal problems, responses to legal problems, and costs of legal problems among Ontarians. The paper concludes by situating these findings into the legal consciousness framework so as to understand how Ontarians experience the law and how that may assist in providing meaningful access to justice reforms

Investigations of the Gas-Liquid Multiphase System Involving Macro-Instability in a Baffled Stirred Tank Reactor

Bubble Sauter Mean Diameter (SMD) in gas-liquid multiphase system is of particular interest and the quantification of gas characteristics is still a challenge today. In this contribution, multiphase Computational Fluid Dynamic (CFD) simulations are combined with Population Balance Model (PBM) to investigate the bubble SMD in baffled stirred tank reactor (STR). Hereby, special attention is given to the phenomenon known as the fluid macro-instability (MI), which is a large-scale low-frequency fluid velocity variation in baffled STRs, since the fluid MIs have a dominating influence on the bubble breakage and coalescence processes. The simulations, regarding the fluid velocity, are validated with Laser Doppler Anemometry (LDA) experiments, in which the instant radial velocity is analyzed through Fast Fourier Transform (FFT) spectrum. The frequency peaks of the fluid MIs are found both in the simulation and in the experiment with a high degree of accuracy. After the validation, quantitative predictions of overall bubble SMD with and without MIs are carried out. Due to the accurate prediction of the fluid field, the influence of the fluid MI to bubble SMD is presented. This result provides more adequate information for engineers working in the field of estimating bubble SMDs in baffled STRs

From cryptocurrencies to cryptocourts: blockchain and the financialization of dispute resolution platforms

This paper contributes to emerging discussions of blockchain governance through an analysis of dispute resolution platforms that reimagine justice. We focus specifically on Kleros, a blockchain-enabled dispute resolution platform, that promises to secure, authenticate, and democratize access to justice for the twenty-first century. We advance the concept of cryptocourts whereby jurors, incentivized by accumulating cryptocurrency, rapidly mobilize using principles of on-demand crowdsourcing to resolve disputes. We critique the broader social imaginaries that cryptocourts such as Kleros will result in a more open, trustworthy, transparent, and democratic systems of justice. These platforms instead pose important questions concerning their potential impact on civil dispute resolution practices by embedding it within an economy of cryptocurrency speculation. This ostensibly results in a legal infrastructure founded on principles of financial acquisition that positions jurors as economic agents seeking to profit from disputes, and courts as computational systems that merely authenticate and secure the distribution of evidence and verdicts

Impact of disseminated tumor cells in the bone marrow on survival and disease progression in patients with left‑sided colorectal cancer

Introduction Disseminated tumor cells (DTCs) are a subset of circulating tumor cells that migrate to the bone marrow. Colorectal cancer is a heterogeneous disease depending on the site of the primary tumor. Objectives We aimed to assess the association between the presence of DTCs in the bone marrow and tumor characteristics as well as long‑term treatment outcomes in patients with left‑sided colorectal cancer. Patients and methods This prospective study included 91 patients with left‑sided colorectal cancer (37 with colon cancer and 54 with rectal cancer) treated between 2007 and 2012 in a single tertiary center. Fifteen patients had stage I cancer; 26, stage II; 26, stage III; and 24, stage IV. Overall survival and cancer relapse rates were compared between patients with different cancer stages and DTC status. Results Bone marrow DTCs were identified in 42 patients (46.1%). The prevalence of DTCs was not related to tumor infiltration depth, nodal involvement, distant metastasis, tumor stage, or primary tumor site. The 5‑year overall survival rates were 59.5% and 53% in the DTC‑positive and DTC‑negative groups, respectively (P = 0.19). There was a notable trend favoring survival in patients with DTCs with stage II and III disease (both separately and when combined). The number of metachronous distant metastases was significantly lower in DTC‑positive patients. Conclusions The presence of DTCs in the bone marrow is not associated with primary tumor characteristics and seems to reduce metastasis formation in left‑sided colorectal cancer. There is also a trend for improved overall survival in DTC‑positive patients. These results are intriguing and warrant further confirmation

Prenatal alcohol exposure reduces 5-HT concentration in mouse intestinal muscle and mucosa

The influence of prenatal alcohol exposure on theserotoninergic system in the brain has been well studied, however its influence on the serotoninergic system in the gastrointestinal system remains unknown. The objective of the study was to use a mouse model of prenatal alcohol exposure to investigate the effects on serotonin and its metabolites and precursors in colonic tissue. This study used treatment of mouse breeding harems with 5% ethanol with saccharin via drinking water throughout pregnancy and compared the results with a saccharin control group. Tryptophan, serotonin (5-HT) and 5- hydroxyindoleacetic acid (5-HIAA) concentrations were measured in the longitudinal muscle myenteric plexus (LMMP) and mucosa of intestinal tissue by high- performance liquid chromatography (HPLC). Decreased 5-HT concentrations in mucosa and LMMP (females only) were observed in prenatally exposed mice compared to controls. Increases in mucosal and LMMP tryptophan concentration were only observed in prenatally exposed female mice. In conclusion, prenatal alcohol exposure causes a decrease in conversion of tryptophan to 5-HT in both muscle and mucosa although the effect is more pronounced in females. The observed sex difference may be related tochanges associated with the estrous cycle

Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning

Diagnosis of adverse neonatal outcomes is crucial for preterm survival since it enables doctors to provide timely treatment. Machine learning (ML) algorithms have been demonstrated to be effective in predicting adverse neonatal outcomes. However, most previous ML-based methods have only focused on predicting a single outcome, ignoring the potential correlations between different outcomes, and potentially leading to suboptimal results and overfitting issues. In this work, we first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem. We then propose an MTL framework to jointly predict multiple adverse neonatal outcomes. In particular, the MTL framework contains shared hidden layers and multiple task-specific branches. Extensive experiments have been conducted using Electronic Health Records (EHRs) from 121 preterm neonates. Empirical results demonstrate the effectiveness of the MTL framework. Furthermore, the feature importance is analyzed for each neonatal outcome, providing insights into model interpretability

Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models

Engaging end user groups with machine learning (ML) models can help align the design of predictive systems with people’s needs and expectations. We present a co-design study investigating the benefits and challenges of using computational notebooks to inform ML models with end user groups. We used a computational notebook to engage young adults, carers, and clinicians with an example ML model that predicted health risk in diabetes care. Through codesign workshops and retrospective interviews, we found that participants particularly valued using the interactive data visualisations of the computational notebook to scaffold multidisciplinary learning, anticipate benefits and harms of the example ML model, and create fictional feature importance plots to highlight care needs. Participants also reported challenges, from running code cells to managing information asymmetries and power imbalances. We discuss the potential of leveraging computational notebooks as interactive co-design tools to meet end user needs early in ML model lifecycles

Computational Notebooks as Co-Design Tools:Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models

Engaging end user groups with machine learning (ML) models can help align the design of predictive systems with people's needs and expectations. We present a co-design study investigating the benefits and challenges of using computational notebooks to inform ML models with end user groups. We used a computational notebook to engage young adults, carers, and clinicians with an example ML model that predicted health risk in diabetes care. Through co-design workshops and retrospective interviews, we found that participants particularly valued using the interactive data visualisations of the computational notebook to scaffold multidisciplinary learning, anticipate benefits and harms of the example ML model, and create fictional feature importance plots to highlight care needs. Participants also reported challenges, from running code cells to managing information asymmetries and power imbalances. We discuss the potential of leveraging computational notebooks as interactive co-design tools to meet end user needs early in ML model lifecycles