Keeping New Teachers: A First Look at the Influences of Induction in the Chicago Public Schools

Examines whether participation in a formal induction program can improve teachers' experiences and job satisfaction, and demonstrates that strong levels of mentoring and support for new teachers greatly improve their desire to continue teaching

Optimised analysis and visualisation of metabolic data using graph theoretical approaches

Since the completion of the Human Genome Project in 2003, it has become increasingly apparent that while genomics has a major role to play in the understanding of human biology, information from other disciplines is necessary to explain the web of interacting signals that allow our bodies to function on a day to day basis and respond to rapid changes in our local environment. One such field, that of metabolomics, focuses on the study of the set of low molecular weight compounds (metabolites) involved in metabolism. Metabolomic studies aim to quantify the concentrations of each of these compounds within a subject under particular conditions, resulting in either information on the physiological effects of a disease or environmental factor (such as a toxin) on the organism, or the identification of metabolites or groups of metabolites that serve as biochemical markers for a state or illness. Whilst metabolite concentrations alone can give great insight into a chosen state, additional information can be obtained by considering the ways in which metabolites interact with each other as parts of a larger system. One method of tackling this problem, metabolic networks, is gaining popularity within the community as it offers a complementary approach to the traditional biological method for studying metabolism, the metabolic pathway. Construction methods are varied; ranging from the mapping of experimental data onto pathway diagrams, through the use of correlation-based techniques, to the analysis of time-series data of metabolic fluxes. However, while many attempts have been made to capture and visualise the complex web of reactions within an organism, few have yet succeeded in showing how they can be used to help identify the metabolites that are most significantly involved in the differences between groups of biological samples. This thesis discusses ways in which graphs may be used to aid researchers in both the visualisation and interpretation of metabolomic datasets, and provide a platform for more automated analysis techniques. To that end, it first presents the background to the relevant topics, metabolomics and graph theory, before moving on to show how metabolic correlation networks can be used to identify and visualise differences in metabolism between groups of subjects. It then introduces Linked Metabolites, a software package that has been developed to help researchers explain differences in metabolism by highlighting relationships between metabolites within the metabolic pathways, and to compile those relationships into directed metabolic graphs suitable for analysis using metrics from graph theory. Finally, the thesis explains how the directed metabolic graphs produced by Linked Metabolites could potentially be used to integrate data gathered from the same sample using different experimental techniques, refining the areas of the underlying biochemistry needing further investigation

Transcription of herpes simplex virus type 2

Summary available: p. i-iv

To blockchain or not to blockchain, these are the questions:A structured analysis of blockchain decision schemes

Blockchain technology has garnered significant attention in recent years, prompting researchers, entrepreneurs, and businesses to seek viable ways to validate the application of blockchain within their specific use cases. Blockchain decision schemes (BDSs) can assist in this decision-making process, offering a potentially more cost-effective alternative to domain experts. Flow chart blockchain decision schemes (FC-BDSs) constitute 77.5 % of all BDSs, and this paper systematically reviews these by standardising and aggregating the most prominent schemes into an open-source package. Central to our approach is the definition of an FC-BDS as a directed acyclic graph (DAG). Upon this mathematical foundation, we engage in a meticulous exploration and analysis of various elements within FC-BDSs. We present an in-depth analysis of the structure of FC-BDSs, exploring features such as vertex count, question categorisation, and outcome distribution. Notably, the majority of FC-BDS questions ask about data and participation (34.1 %) above other domains such as security (18.6 %) and performance (10.8 %). Observations regarding outcomes shows an overall balance in suggesting the usage or avoidance of blockchains; however, there is a discrepancy between the average questions required to reach these outcomes, revealing potential biases within schemes. Further analysis using similarity metrics (based on both structural and semantic features) identifies significant overlaps between FC-BDSs, with some schemes showing over 90 % similarity. These observations could be attributed to a more informal publishing routine for FC-BDSs, and help trace the evolution of FC-BDSs over time. The insights drawn from this research provide valuable insights into the broader BDSs landscape, and stand to make significant strides towards the standardisation of FC-BDSs, thereby promoting a more coherent and effective utilisation of these decision-making tools in the realm of blockchain technology application

Teacher and Principal Responses to the Grow Network: A Preliminary Evaluation of Use in Chicago Public elementary schools

During the 2001-02 school year, Chicago Public Schools officials contracted with the Grow Network to provide supplemental reporting of student test results to parents, teachers, and school administrators. The Grow Network provides a variety of resources, including individualized, printed score reports for parents; classroom-level reports for teachers; and school building reports for principals. CPS engaged the Grow Network as part of its efforts to improve data access and develop greater capacity for "data-driven decision making" in schools