MOLECULAR CHARACTERISATION OF EXPANDED MOUSE HAEMATOPOIETIC STEM CELLS USING A NOVEL IN VITRO REPORTER STRATEGY

Haematopoietic stem cells (HSCs) are responsible for the lifelong maintenance of the blood forming system which produces trillions of blood cells daily. They are able to achieve this because of two defining properties: 1) they can give rise to progeny which eventually form all of the blood cell types in an organism and 2) they can create equally potent daughter cells. This latter property of self-renewal has the potential to be harnessed to create unlimited numbers of HSCs outside the body, which would be highly beneficial to cellular and gene therapy. As a result, decades of research have focused on improving in vitro HSC expansion efficiency with most studies failing to expand functional HSCs in sufficiently large quantities. Recent efforts in mouse HSC biology achieved a more than 200-fold expansion of functional HSCs; however, single cell cultures in these conditions displayed a large amount of heterogeneity. Using a recently generated HSC reporter mouse, I devised a novel in vitro reporter strategy capable of reading out functional HSC activity in vitro and also discovered a previously unreported population of lymphoid cells marked by the reporter (Chapter 3). I showed that the in vitro reporter strategy could be used to screen for molecules that promote HSC expansion and could prospectively identify single-cell derived cultures that contained large numbers of functional HSCs (Chapter 4). This permitted us to undertake gene expression profiling to determine the molecular identity of expanded HSCs using RNA sequencing. Comparing the transcriptome of these cells and the secretome of these heterogeneous clonal cultures, I identified potentially novel regulators for promoting the expansion of HSCs (Chapter 5).Medical Research Counci

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Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87032/1/24410_ftp.pd

Societal benefit assessment: an integrated tool to support sustainable toy design and manufacture

A framework and methodology for assessing the societal benefits of a product was developed based on the assertion that, in order to access future diminishing resources, manufacturers will need to demonstrate both the social and environmental benefits of their products. This paper follows on from this published research and presents an integrated tool to support the implementation of this framework and methodology within the toy industry during the design and development phase. A simulated case study is used to exemplify the application of this tool and to support the concluding discussions

An integrated tool to support sustainable toy design and manufacture

Whilst the importance of considering the positive societal benefits of a product, in addition to other social, economic and environmental factors, has received wider recognition, its definition, concept, and integration into product design are not so well developed and studied. A literature review on sustainable design identified the potential of Social Life-Cycle Assessment as a tool to measure societal benefits of products; however further analysis of sustainable assessment methods highlighted the lack of a coherent definition and method for achieving this. This paper presents a framework for including societal benefits within a product portfolio management process and a prototype tool which aims to support the implementation of the framework within the toy industry, specifically on the societal benefit assessment of the products during the first stage. Finally a simulated case study of three toys is used to exemplify the intended application of this tool and to support the concluding discussions

Fact Check: Analyzing Financial Events from Multilingual News Sources

The explosion in the sheer magnitude and complexity of financial news data in recent years makes it increasingly challenging for investment analysts to extract valuable insights and perform analysis. We propose FactCheck in finance, a web-based news aggregator with deep learning models, to provide analysts with a holistic view of important financial events from multilingual news sources and extract events using an unsupervised clustering method. A web interface is provided to examine the credibility of news articles using a transformer-based fact-checker. The performance of the fact checker is evaluated using a dataset related to merger and acquisition (M\&A) events and is shown to outperform several strong baselines.Comment: Dem

Posterior Regularization on Bayesian Hierarchical Mixture Clustering

Bayesian hierarchical mixture clustering (BHMC) improves on the traditional Bayesian hierarchical clustering by, with regard to the parent-to-child diffusion in the generative process, replacing the conventional Gaussian-to-Gaussian (G2G) kernels with a Hierarchical Dirichlet Process Mixture Model (HDPMM). However, the drawback of the BHMC lies in the possibility of obtaining trees with comparatively high nodal variance in the higher levels (i.e., those closer to the root node). This can be interpreted as that the separation between the nodes, particularly those in the higher levels, might be weak. We attempt to overcome this drawback through a recent inferential framework named posterior regularization, which facilitates a simple manner to impose extra constraints on a Bayesian model to address its weakness. To enhance the separation of clusters, we apply posterior regularization to impose max-margin constraints on the nodes at every level of the hierarchy. In this paper, we illustrate the modeling detail of applying the PR on BHMC and show that this solution achieves the desired improvements over the BHMC model