Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model

Irradiation of the tumour site during treatment for cancer with external-beam ionising radiation results in a complex and dynamic series of effects in both the tumour itself and the normal tissue which surrounds it. The development of a spectral model of the effect of each exposure and interaction mode between these tissues would enable label free assessment of the effect of radiotherapeutic treatment in practice. In this study Fourier transform Infrared microspectroscopic imaging was employed to analyse an in-vitro model of radiotherapeutic treatment for prostate cancer, in which a normal cell line (PNT1A) was exposed to low-dose X-ray radiation from the scattered treatment beam, and also to irradiated cell culture medium (ICCM) from a cancer cell line exposed to a treatment relevant dose (2 Gy). Various exposure modes were studied and reference was made to previously acquired data on cellular survival and DNA double strand break damage. Spectral analysis with manifold methods, linear spectral fitting, non-linear classification and non-linear regression approaches were found to accurately segregate spectra on irradiation type and provide a comprehensive set of spectral markers which differentiate on irradiation mode and cell fate. The study demonstrates that high dose irradiation, low-dose scatter irradiation and radiation-induced bystander exposure (RIBE) signalling each produce differential effects on the cell which are observable through spectroscopic analysis

The Vehicle, Spring 2010

Table of Contents ForgettingRashelle McNairpage 34 MuseMary Lieskepage 35 My CompulsionAshton Tembypage 38 MemoryKate Vandermeerpage 41 Killmercialize MeGreg Petersonpage 42 PenJake Smithpage 46 GrassKate Vandermeerpage 48 Character CreationMary Lieskepage 52 Ring Around TheKim Hunter-Perkinspage 54 The Great Cursive ScareJake Smithpage 55 OpiateDoug Urbanskipage 61 What Happens to Little Girls...Jennifer O\u27Neilpage 63 Poetry Sunny DaysRyan Poolpage 2 AtlantisDoug Urbanskipage 4 Garbage CityKate Vandermeerpage 6 Fat Girl ThongsKim Hunter-Perkinspage 7 MercilessRosalia Pecorapage 19 ChemistryMary Lieskepage 20 He-Who-Stopped-TalkingJustin Sudkamppage 22 In Which Iris Contemplates a Barren EarthSean Slatterypage 24 At the Bottom of the WorldNick Canadaypage 27 Dogma: Mush!Scott Maypage 28 ThiefMary Lieskepage 29 Prose Coming HomeDoug Urbanskipage 8 DodoDan Davispage 31 The Poet in the PedestrianScott Maypage 37 Toxic RainJacob Swansonpage 40 What\u27s Your Greatest Fear?Justine Fittonpage 43 Soul VoiceHolly Thomaspage 49 Conversations with a SniperKim Hunter-Perkinspage 56 LettersDaniel Paquinpage 65 Art San Marcos, MexicoKate Vandermeercover Contemplation of the World\u27s EndNicholas Giffordpage 18 Little Lady SitsSarah Hadwigerpage 26 MoodAlycia Rockeypage 30 Four Ducks in a RowMegan Mathypage 36 The Daily EasternBen Tillerypage 39 BirdsAlycia Rockeypage 45 March of the BugsMegan Mathypage 47 Mexico Work ExperienceKate Vandermeerpage 53 Feather and JewelsAlycia Rockeypage 60 The ForgottenMegan Mathypage 64 Special Features Fall 2009-Spring 2010 Vehicle Award Winnerspage 1 James K. Johnson Creative Writing Awardpage 74 Kim Hunter-PerkinsWinning Entries (Poetry)page 75 Clint WalkerWinning Entry (Fiction)page 86 Faculty Spotlight: Professor Jason Brownpage 99 About the Contributorspage 106 About the Editorspage 110https://thekeep.eiu.edu/vehicle/1093/thumbnail.jp

The Florey Adelaide Male Ageing Study (FAMAS): Design, procedures & participants

<p>Abstract</p> <p>Background</p> <p>The Florey Adelaide Male Ageing Study (FAMAS) examines the reproductive, physical and psychological health, and health service utilisation of the ageing male in Australia. We describe the rationale for the study, the methods used participant response rates, representativeness and attrition to date.</p> <p>Methods</p> <p>FAMAS is a longitudinal study involving approximately 1200 randomly selected men, aged 35–80 years and living in the north – west regions of Adelaide. Respondents were excluded at screening if they were considered incapable of participating because of immobility, language, or an inability to undertake the study procedures. Following a telephone call to randomly selected households, eligible participants were invited to attend a baseline clinic measuring a variety of biomedical and socio-demographic factors. Beginning in 2002, these clinics are scheduled to reoccur every five years. Follow-up questionnaires are completed annually. Participants are also invited to participate in sub-studies with selected collaborators.</p> <p>Results</p> <p>Of those eligible to participate, 45.1% ultimately attended a clinic. Non-responders were more likely to live alone, be current smokers, have a higheevalence of self-reported diabetes and stroke, and lower levels of hypercholesterolemia. Comparisons with the Census 2001 data showed that participants matched the population for most key demographics, although younger groups and never married men were under-represented and elderly participants were over-represented. To date, there has been an annual loss to follow-up of just over 1%.</p> <p>Conclusion</p> <p>FAMAS allows a detailed investigation into the effects of bio-psychosocial and behavioural factors on the health and ageing of a largely representative group of Australian men.</p

Combining Statistical and Relational Methods for Learning in Hypertext Domains

. We present a new approach to learning hypertext classifiers that combines a statistical text-learning method with a relational rule learner. This approach is well suited to learning in hypertext domains because its statistical component allows it to characterize text in terms of word frequencies, whereas its relational component is able to describe how neighboring documents are related to each other by hyperlinks that connect them. We evaluate our approach by applying it to tasks that involve learning definitions for (i) classes of pages, (ii) particular relations that exist between pairs of pages, and (iii) locating a particular class of information in the internal structure of pages. Our experiments demonstrate that this new approach is able to learn more accurate classifiers than either of its constituent methods alone. 1 Introduction In recent years there has been a great deal of interest in applying machinelearning methods to a variety of problems in classifying and extracting ..

Discovering Test Set Regularities in Relational Domains

Machine learning typically involves discovering regularities in a training set, then applying these learned regularities to classify objects in a test set. In this paper we present an approach to discovering additional regularities in the test set, and show that in relational domains such test set regularities can be used to improve classification accuracy beyond that achieved using the training set alone. For example, we have previously shown how FOIL, a relational learner, can learn to classify Web pages by discovering training set regularities in the words occurring on target pages, and on other pages related by hyperlinks. Here we show how the classification accuracy of FOIL on this task can be improved by discovering additional regularities on the test set pages that must be classified. Our approach can be seen as an extension to Kleinberg&apos;s Hubs and Authorities algorithm that analyzes hyperlink relations among Web pages. We present evidence that this new algor..

A Study of Approaches to Hypertext Categorization

. Hypertext poses new research challenges for text classification. Hyperlinks, HTML tags, category labels distributed over linked documents, and meta data extracted from related web sites all provide rich information for classifying hypertext documents. How to appropriately represent that information and automatically learn statistical patterns for solving hypertext classification problems is an open question. This paper seeks a principled approach to providing the answers. Specifically, we define five hypertext regularities which may (or may not) hold in a particular application domain, and whose presence (or absence) may significantly influence the optimal design of a classifier. Using three hypertext datasets and three well-known learning algorithms (Naive Bayes, Nearest Neighbor, and First Order Inductive Learner), we examine these regularities in different domains, and compare alternative ways to exploit them. Our experimental results suggest that a naive use of linked pages, such as treating the words in the linked neighborhood of a page as local to that page, can be more harmful than helpful when the linked neighborhood is highly &quot;noisy&quot;. This is especially true if the classifier is not sufficiently robust in discriminating informative words from noisy ones. It is also evident in our results that extracting meta data (when available) from related web sites can be extremely useful for improving classification accuracy. Finally, the relative performance of the classifiers being tested provides insights into their strengths and limitations for solving classification problems involving diverse and often noisy web pages. Keywords: hypertext classification, machine learning, web mining 1

Knowledge Engineering Requirements in Derivational Analogy

TCD-CS-96-10A major advantage in using a case-based approach to developing knowledge-based systems is that it can be applied to problems where a strong domain theory may be difficult to determine. However the development of case-based reasoning (CBR) systems that set out to support a sophisticated case adaptation process does require a strong domain model. The Derivational Analogy (DA) approach to CBR is a case in point. In DA the case representation contains a trace of the reasoning process involved in producing the solution for that case. In the adaptation process this reasoning trace is reinstantiated in the context of the new target case; this requires a strong domain model and the encoding of problem solving knowledge. In this paper we analyse this issue using as an example a CBR system called CoBRA that assists with the modelling tasks in numerical simulation