Search results for: Trudie Chalder

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Chalder</title> <meta name="description" content="Search results for: Trudie Chalder"> <meta name="keywords" content="Trudie Chalder"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Trudie Chalder"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Trudie Chalder</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Application of Latent Class Analysis and Self-Organizing Maps for the Prediction of Treatment Outcomes for Chronic Fatigue Syndrome</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ben%20Clapperton">Ben Clapperton</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Stahl"> Daniel Stahl</a>, <a href="https://publications.waset.org/abstracts/search?q=Kimberley%20Goldsmith"> Kimberley Goldsmith</a>, <a href="https://publications.waset.org/abstracts/search?q=Trudie%20Chalder"> Trudie Chalder</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently can't be explained by any underlying medical condition. Although clinical trials support the effectiveness of cognitive behaviour therapy (CBT), the success rate for individual patients is modest. Patients vary in their response and little is known which factors predict or moderate treatment outcomes. The aim of the project is to develop a prediction model from baseline characteristics of patients, such as demographics, clinical and psychological variables, which may predict likely treatment outcome and provide guidance for clinical decision making and help clinicians to recommend the best treatment. The project is aimed at identifying subgroups of patients with similar baseline characteristics that are predictive of treatment effects using modern cluster analyses and data mining machine learning algorithms. The characteristics of these groups will then be used to inform the types of individuals who benefit from a specific treatment. In addition, results will provide a better understanding of for whom the treatment works. The suitability of different clustering methods to identify subgroups and their response to different treatments of CFS patients is compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chronic%20fatigue%20syndrome" title="chronic fatigue syndrome">chronic fatigue syndrome</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20class%20analysis" title=" latent class analysis"> latent class analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20modelling" title=" prediction modelling"> prediction modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20maps" title=" self-organizing maps"> self-organizing maps</a> </p> <a href="https://publications.waset.org/abstracts/90820/application-of-latent-class-analysis-and-self-organizing-maps-for-the-prediction-of-treatment-outcomes-for-chronic-fatigue-syndrome" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90820.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">226</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> A Community-Engaged Approach to Examining Health Outcomes Potentially Related to Exposure to Environmental Contaminants in Yuma, Arizona</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Julie%20A.%20Baldwin">Julie A. Baldwin</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20T.%20Trotter"> Robert T. Trotter</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20Remiker"> Mark Remiker</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Loren%20Buck"> C. Loren Buck</a>, <a href="https://publications.waset.org/abstracts/search?q=Amanda%20Aguirre"> Amanda Aguirre</a>, <a href="https://publications.waset.org/abstracts/search?q=Trudie%20Milner"> Trudie Milner</a>, <a href="https://publications.waset.org/abstracts/search?q=Emma%20Torres"> Emma Torres</a>, <a href="https://publications.waset.org/abstracts/search?q=Frank%20A.%20von%20Hippel"> Frank A. von Hippel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: In the past, there have been concerns about contaminants in the water sources in Yuma, Arizona, including the Colorado River. Prolonged exposure to contaminants, such as perchlorate and heavy metals, can lead to deleterious health effects in humans. This project examined the association between the concentration of environmental contaminants and patient health outcomes in Yuma residents, using a community-engaged approach to data collection. Methods: A community-engaged design allowed community partners and researchers to establish joint research goals, recruit participants, collect data, and formulate strategies for dissemination of findings. Key informant interviews were conducted to evaluate adherence to models of community-based research. Results: The training needs, roles, and expectations of community partners varied based on available resources, prior research experience, and perceived research challenges and ways to address them. Conclusions: Leveraging community-engaged approaches for studies of environmental contamination in marginalized communities can expedite recruitment efforts and stimulate action that can lead to improved community health. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=community%20engaged%20research" title="community engaged research">community engaged research</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20contaminants" title=" environmental contaminants"> environmental contaminants</a>, <a href="https://publications.waset.org/abstracts/search?q=underserved%20populations" title=" underserved populations"> underserved populations</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20equity" title=" health equity"> health equity</a> </p> <a href="https://publications.waset.org/abstracts/114417/a-community-engaged-approach-to-examining-health-outcomes-potentially-related-to-exposure-to-environmental-contaminants-in-yuma-arizona" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/114417.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Syntax and Words as Evolutionary Characters in Comparative Linguistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nancy%20Retzlaff">Nancy Retzlaff</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarah%20J.%20Berkemer"> Sarah J. Berkemer</a>, <a href="https://publications.waset.org/abstracts/search?q=Trudie%20Strauss"> Trudie Strauss</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last couple of decades, the advent of digitalization of any kind of data was probably one of the major advances in all fields of study. This paves the way for also analysing these data even though they might come from disciplines where there was no initial computational necessity to do so. Especially in linguistics, one can find a rather manual tradition. Still when considering studies that involve the history of language families it is hard to overlook the striking similarities to bioinformatics (phylogenetic) approaches. Alignments of words are such a fairly well studied example of an application of bioinformatics methods to historical linguistics. In this paper we will not only consider alignments of strings, i.e., words in this case, but also alignments of syntax trees of selected Indo-European languages. Based on initial, crude alignments, a sophisticated scoring model is trained on both letters and syntactic features. The aim is to gain a better understanding on which features in two languages are related, i.e., most likely to have the same root. Initially, all words in two languages are pre-aligned with a basic scoring model that primarily selects consonants and adjusts them before fitting in the vowels. Mixture models are subsequently used to filter ‘good’ alignments depending on the alignment length and the number of inserted gaps. Using these selected word alignments it is possible to perform tree alignments of the given syntax trees and consequently find sentences that correspond rather well to each other across languages. The syntax alignments are then filtered for meaningful scores—’good’ scores contain evolutionary information and are therefore used to train the sophisticated scoring model. Further iterations of alignments and training steps are performed until the scoring model saturates, i.e., barely changes anymore. A better evaluation of the trained scoring model and its function in containing evolutionary meaningful information will be given. An assessment of sentence alignment compared to possible phrase structure will also be provided. The method described here may have its flaws because of limited prior information. This, however, may offer a good starting point to study languages where only little prior knowledge is available and a detailed, unbiased study is needed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alignments" title="alignments">alignments</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20linguistics" title=" comparative linguistics"> comparative linguistics</a>, <a href="https://publications.waset.org/abstracts/search?q=historical%20linguistics" title=" historical linguistics"> historical linguistics</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20methods" title=" statistical methods"> statistical methods</a> </p> <a href="https://publications.waset.org/abstracts/96237/syntax-and-words-as-evolutionary-characters-in-comparative-linguistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96237.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div 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Search results for: Trudie Chalder