Evaluation Of Autoantibodies To Paraneoplastic Antigens As Early Detection Biomarkers For High-Grade Serous Ovarian Cancer

The majority of ovarian cancer cases are diagnosed at an advanced stage metastatic disease with poor prognosis due to non-specific symptoms and lack of early detection methods. This study evaluates autoantibodies against tumor antigens to identify candidate biomarkers for the early detection of ovarian tumors in high-risk women. Paraneoplastic antigens are associated with autoimmune diseases termed paraneoplastic neurological syndromes (PNSs), which develop when the unregulated immune response against a tumor also targets healthy cells. Notably, a set of antibodies is found in PNS patients with ovarian cancer, identifying highly immunogenic antigens in the tumor. In this dissertation work, we have detected paraneoplastic antibodies present in the sera of patients with high-grade serous ovarian cancer (HGSOC) using line blots, western blots, and ELISA. A panel of five paraneoplastic antigens (HARS, TRIM21, COR, CDR2, CDR2L) along with 2 established tumor antigens (NY-ESO-1, p53) were purified from E. coli for screening on western blot and ELISA. Screening was performed with a patient serum set consisting of: 50 late stage HGSOC, 14 early stage HGSOC, 50 benign ovarian cyst, and 50 healthy volunteer samples. On western blot, the paraneoplastic antigen with the best performance was TRIM21with 35% sensitivity. Combining TRIM21 with p53 and NYESO-1 yielded a sensitivity of 60% with 90% specificity. In the early stage HGSOC sample set, HARS demonstrated 31% sensitivity individually, and 46% sensitivity with 98% specificity when combined with p53 and NYESO-1. The identified markers will were tested in an independent validation serum set consisting of n=150 samples. The work in this dissertation identified the paraneoplastic antigen TRIM21 that can enhance autoantibody biomarker panels for the early detection of HGSOC

Alien Registration- Hurley, Catherine E. (Fort Fairfield, Aroostook County)

https://digitalmaine.com/alien_docs/35950/thumbnail.jp

Statistical Graphics in QUAIL: An Overview

It has been suggested (Wainer, 1989) that the system first proposed by C.S. Peirce to organise knowledge is particularly suited to describing statistical graphics. Peirce felt that all information could be broken down into three different types { monadic information, which describes something in and of itself, dyadic information, which describes a relationship between two things, and triadic information, which describes the relation between two things mediated by a third. We can see how this applies in statistical graphics by considering the scatterplot. There, each case in a dataset is represented in the display by a glyph, which is monadic in nature. The scatterplot is a dyad; by positioning the case glyphs in the plane according to the values each case has on two variates X and Y , any empirical relationship between the variates can be seen. Triadic information is available by linking the scatterplot with another plot, say a dot-plot of a third variate, Z. Colouring the glyphs in the dot-plot within a given range of Z values, causes the corresponding glyphs in the scatterplot to be coloured in the same way. Here the relationship between X and Y is seen mediated by the third variate Z. This description of information is reflected in the design and implementation of our graphics software, which is part of the QUAIL system (Oldford et al). Quail (for QUantitative Analysis in Lisp) is a programming environment for statistical and quantitative computing. It has extensive arithmetical, mathematical, statistical and display facilities. This paper gives a brief overview of the principles underlying the statistical graphics facilities. The original software model was first illustrated in a video (Hurley and Oldford, 1988), and was described in Hurley and Oldford (1991). Perhaps surprisingly, we have not altered the original software model, rather we have extended and enriched its scope over the intervening years. The statistical graphics system in Quail has an object-oriented design, which we outline in Section 2. In the language of Peirce, individual objects have a monadic nature. We provide basic building blocks consisting of simple graphical objects such as point symbols and lines and container objects within which the simple objects are positioned to display relationships, ultimately forming plots. Container objects present dyadic information; Section 3 describes some such objects available in Quail. In Section 4, we outline a few of the ways triadic information is available; generally this involves comparison of plots, and, if the plots are displayed over time, interactive graphics

A New Flexible Dendrogram Seriation Algorithm for Data Visualisation

Seriation is a data analytic tool for obtaining a permutation of a set of objects with the goal of revealing structural information within the set of objects. Seriating variables, cases or categories generally improves visualisations of statistical data, for example, by revealing hidden patterns in data or by making large datasets easier to understand. In this paper we present a new algorithm for seriation based on dendrograms. Dendrogram seriation algorithms rearrange the nodes in a dendrogram in order to obtain a permutation of the leaves (i.e. objects) that optimises a given criterion. Our algorithm is more flexible than currently available seriation algorithms because it allows the user to either choose from a variety of seriation criteria or to input their own criteria. This choice of seriation criteria is an important feature because different criteria are suitable for different visualisation settings. Common seriation criteria include measurements of the path length through a set of objects and measurements of anti-Robinson form in a symmetric matrix. We propose new seriation criteria called lazy path length and banded anti-Robinson form, and demonstrate their effectiveness in a variety of visualisation settings

A New Flexible Dendrogram Seriation Algorithm for Data Visualisation

Seriation is a data analytic tool for obtaining a permutation of a set of objects with the goal of revealing structural information within the set of objects. Seriating variables, cases or categories generally improves visualisations of statistical data, for example, by revealing hidden patterns in data or by making large datasets easier to understand. In this paper we present a new algorithm for seriation based on dendrograms. Dendrogram seriation algorithms rearrange the nodes in a dendrogram in order to obtain a permutation of the leaves (i.e. objects) that optimises a given criterion. Our algorithm is more flexible than currently available seriation algorithms because it allows the user to either choose from a variety of seriation criteria or to input their own criteria. This choice of seriation criteria is an important feature because different criteria are suitable for different visualisation settings. Common seriation criteria include measurements of the path length through a set of objects and measurements of anti-Robinson form in a symmetric matrix. We propose new seriation criteria called lazy path length and banded anti-Robinson form, and demonstrate their effectiveness in a variety of visualisation settings

vivid: An R package for Variable Importance and Variable Interactions Displays for Machine Learning Models

We present vivid, an R package for visualizing variable importance and variable interactions in machine learning models. The package provides a range of displays including heatmap and graph-based displays for viewing variable importance and interaction jointly and partial dependence plots in both a matrix layout and an alternative layout emphasizing important variable subsets. With the intention of increasing a machine learning models' interpretability and making the work applicable to a wider readership, we discuss the design choices behind our implementation by focusing on the package structure and providing an in-depth look at the package functions and key features. We also provide a practical illustration of the software in use on a data set.Comment: 15 pages, 7 figure

Interactive slice visualization for exploring machine learning models

Machine learning models fit complex algorithms to arbitrarily large datasets. These algorithms are well-known to be high on performance and low on interpretability. We use interactive visualization of slices of predictor space to address the interpretability deficit; in effect opening up the black-box of machine learning algorithms, for the purpose of interrogating, explaining, validating and comparing model fits. Slices are specified directly through interaction, or using various touring algorithms designed to visit high-occupancy sections or regions where the model fits have interesting properties. The methods presented here are implemented in the R package \pkg{condvis2}.Comment: 35 pages, 14 figure

Early Childhood Development (ECD) services in the Southern Adelaide Health Service region.

This report documents the findings of a review conducted by the South Australian Community Health Research Unit (SACHRU) at the request of the Southern Adelaide Health Service (SAHS) into the provision of Early Childhood Development services in southern Adelaide. This review was undertaken between June and December 2006 and overseen by a Project Management group consisting of representatives from the SAHS, the primary health services managers, practitioners, an acute service manager and the researchers. The review examined the early childhood services provided by primary health services across the region, the models used, intake procedures and referral pathways. The findings were to be used for future service planning, implementation and resourcing

Families empowered: a strengths based approach. An evaluation of FEAT, Families Empowered to Act Together.

The South Australian Community Health Research Unit undertook an evaluation of the FEAT program to track the progress of a number of families through the Families Empowered to Act Together (FEAT) program and capture the experiences and perspectives of children, families and stakeholders. Interviews were undertaken with carers and children both currently in the program and those recently exited. The evaluation also documents the development of the FEAT model of service and its aims and objectives; relates the operation of FEAT to understandings in the current literature regarding best practice principles and models for family support programs; and identifies other agencies, programs and services that the FEAT program links with in order to meet the needs of referred families. The evaluation adopted an action research framework employing qualitative and quantitative methods, and has encouraged participation by key stakeholders in the research process