Relations between social support, appraisal and coping and both positive and negative outcomes for children of a parent with multiple sclerosis and comparisons with children of healthy parents

Objective: To examine adjustment in children of a parent with multiple sclerosis within a stress and coping framework and compare them with those who have 'healthy' parents. Subjects: A total of 193 participants between 10 and 25 years completed questionnaires; 48 youngsters who had a parent with multiple sclerosis and 145 youngsters who reported that they did not have a parent with an illness or disability. Method: A questionnaire survey methodology was used. Variable sets included caregiving context (e.g. additional parental illness, family responsibilities, parental functional impairment, choice in helping), social support (network size, satisfaction), stress appraisal, coping (problem solving, seeking support, acceptance, wishful thinking, denial), and positive (life satisfaction, positive affect, benefits) and negative (distress, health) adjustment outcomes. Results: Caregiving context variables significantly correlated with poorer adjustment in children of a parent with multiple sclerosis included additional parental illness, higher family responsibilities, parental functional impairment and unpredictability of the parent's multiple sclerosis, and less choice in helping. As predicted, better adjustment in children of a parent with multiple sclerosis was related to higher levels of social support, lower stress appraisals, greater reliance on approach coping strategies (problem solving, seeking support and acceptance) and less reliance on avoidant coping (wishful thinking and denial). Compared with children of 'healthy' parents, children of a parent with multiple sclerosis reported greater family responsibilities, less reliance on problem solving and seeking social support coping, higher somatization and lower life satisfaction and positive affect. Conclusions: Findings delineate the key impacts of young caregiving and support a stress and coping model of adjustment in children of a parent with multiple sclerosis.

Rule extraction from technology IPOs in the US stock market

Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.

Hidden in plain sight:low-literacy adults in a developed country overcoming social and educational challenges through mobile learning support tools

Illiteracy is often associated with people in developing countries. However, an estimated 50 % of adults in a developed country such as Canada lack the literacy skills required to cope with the challenges of today's society; for them, tasks such as reading, understanding, basic arithmetic, and using everyday items are a challenge. Many community-based organizations offer resources and support for these adults, yet overall functional literacy rates are not improving. This is due to a wide range of factors, such as poor retention of adult learners in literacy programs, obstacles in transferring the acquired skills from the classroom to the real life, personal attitudes toward learning, and the stigma of functional illiteracy. In our research we examined the opportunities afforded by personal mobile devices in providing learning and functional support to low-literacy adults. We present the findings of an exploratory study aimed at investigating the reception and adoption of a technological solution for adult learners. ALEX© is a mobile application designed for use both in the classroom and in daily life in order to help low-literacy adults become increasingly literate and independent. Such a solution complements literacy programs by increasing users' motivation and interest in learning, and raising their confidence levels both in their education pursuits and in facing the challenges of their daily lives. We also reflect on the challenges we faced in designing and conducting our research with two user groups (adults enrolled in literacy classes and in an essential skills program) and contrast the educational impact and attitudes toward such technology between these. Our conclusions present the lessons learned from our evaluations and the impact of the studies' specific challenges on the outcome and uptake of such mobile assistive technologies in providing practical support to low-literacy adults in conjunction with literacy and essential skills training. © 2013 Her Majesty the Queen in Right of Canada.

PDNAsite:identification of DNA-binding site from protein sequence by incorporating spatial and sequence context

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community.

The risk of re-intervention after endovascular aortic aneurysm repair

This thesis studies survival analysis techniques dealing with censoring to produce predictive tools that predict the risk of endovascular aortic aneurysm repair (EVAR) re-intervention. Censoring indicates that some patients do not continue follow up, so their outcome class is unknown. Methods dealing with censoring have drawbacks and cannot handle the high censoring of the two EVAR datasets collected. Therefore, this thesis presents a new solution to high censoring by modifying an approach that was incapable of differentiating between risks groups of aortic complications. Feature selection (FS) becomes complicated with censoring. Most survival FS methods depends on Cox's model, however machine learning classifiers (MLC) are preferred. Few methods adopted MLC to perform survival FS, but they cannot be used with high censoring. This thesis proposes two FS methods which use MLC to evaluate features. The two FS methods use the new solution to deal with censoring. They combine factor analysis with greedy stepwise FS search which allows eliminated features to enter the FS process. The first FS method searches for the best neural networks' configuration and subset of features. The second approach combines support vector machines, neural networks, and K nearest neighbor classifiers using simple and weighted majority voting to construct a multiple classifier system (MCS) for improving the performance of individual classifiers. It presents a new hybrid FS process by using MCS as a wrapper method and merging it with the iterated feature ranking filter method to further reduce the features. The proposed techniques outperformed FS methods based on Cox's model such as; Akaike and Bayesian information criteria, and least absolute shrinkage and selector operator in the log-rank test's p-values, sensitivity, and concordance. This proves that the proposed techniques are more powerful in correctly predicting the risk of re-intervention. Consequently, they enable doctors to set patients’ appropriate future observation plan.

Multidimensional classification and diagnosis of leukemia in flow cytometry

This research is to establish new optimization methods for pattern recognition and classification of different white blood cells in actual patient data to enhance the process of diagnosis. Beckman-Coulter Corporation supplied flow cytometry data of numerous patients that are used as training sets to exploit the different physiological characteristics of the different samples provided. The methods of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used as promising pattern classification techniques to identify different white blood cell samples and provide information to medical doctors in the form of diagnostic references for the specific disease states, leukemia. The obtained results prove that when a neural network classifier is well configured and trained with cross-validation, it can perform better than support vector classifiers alone for this type of data. Furthermore, a new unsupervised learning algorithm---Density based Adaptive Window Clustering algorithm (DAWC) was designed to process large volumes of data for finding location of high data cluster in real-time. It reduces the computational load to ∼O(N) number of computations, and thus making the algorithm more attractive and faster than current hierarchical algorithms.

A large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling

With the exponential increasing demands and uses of GIS data visualization system, such as urban planning, environment and climate change monitoring, weather simulation, hydrographic gauge and so forth, the geospatial vector and raster data visualization research, application and technology has become prevalent. However, we observe that current web GIS techniques are merely suitable for static vector and raster data where no dynamic overlaying layers. While it is desirable to enable visual explorations of large-scale dynamic vector and raster geospatial data in a web environment, improving the performance between backend datasets and the vector and raster applications remains a challenging technical issue. This dissertation is to implement these challenging and unimplemented areas: how to provide a large-scale dynamic vector and raster data visualization service with dynamic overlaying layers accessible from various client devices through a standard web browser, and how to make the large-scale dynamic vector and raster data visualization service as rapid as the static one. To accomplish these, a large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling and a comprehensive performance improvement solution are proposed, designed and implemented. They include: the quadtree-based indexing and parallel map tiling, the Legend String, the vector data visualization with dynamic layers overlaying, the vector data time series visualization, the algorithm of vector data rendering, the algorithm of raster data re-projection, the algorithm for elimination of superfluous level of detail, the algorithm for vector data gridding and re-grouping and the cluster servers side vector and raster data caching.

Dynamic classifiers for neonatal brain monitoring

Brain injury due to lack of oxygen or impaired blood flow around the time of birth, may cause long term neurological dysfunction or death in severe cases. The treatments need to be initiated as soon as possible and tailored according to the nature of the injury to achieve best outcomes. The Electroencephalogram (EEG) currently provides the best insight into neurological activities. However, its interpretation presents formidable challenge for the neurophsiologists. Moreover, such expertise is not widely available particularly around the clock in a typical busy Neonatal Intensive Care Unit (NICU). Therefore, an automated computerized system for detecting and grading the severity of brain injuries could be of great help for medical staff to diagnose and then initiate on-time treatments. In this study, automated systems for detection of neonatal seizures and grading the severity of Hypoxic-Ischemic Encephalopathy (HIE) using EEG and Heart Rate (HR) signals are presented. It is well known that there is a lot of contextual and temporal information present in the EEG and HR signals if examined at longer time scale. The systems developed in the past, exploited this information either at very early stage of the system without any intelligent block or at very later stage where presence of such information is much reduced. This work has particularly focused on the development of a system that can incorporate the contextual information at the middle (classifier) level. This is achieved by using dynamic classifiers that are able to process the sequences of feature vectors rather than only one feature vector at a time.

The intersectorial approach in providing guidance and school support. The perspective of counsellors, tutors and principals

The provision of guidance and educational support requires joint work and the collaboration of different professionals and institutions, especially when we face complex problems that require a high level of specialization and the combination of knowledge from different areas. The research has aimed to examine the proximity of the institutional system of guidance and support to school in nine Autonomous Communities, to the intersectorial approach of counselling. We present the results of a descriptive study using the survey method, which allows knowing the opinions of counsellors, tutors and principals of Primary and Secondary Compulsory Education about the collaboration with the local public services (social, health, education, and employment) in the specialized support to students and schools. The final sample consisted of 9732 subjects who were selected from a random sampling proportional to the size of the subpopulations of each Autonomous Community. Results indicate how, in general terms, there is collaboration among the school and the local public services, although not as frequently as it would be desirable. In the same way, the professionals that were interviewed believe that the collaboration with social and educational services is quite adequate, but the assessment is not as positive when health and employment services are analysed. Finally, taking into account the different professionals considered, tutors from both educational stages are the ones that show a higher degree of satisfaction with the collaboration between the school and the local public services, except in the case of social services.

A monster that lives in our lives - experiences of caregivers of people with motor neuron disease and identifying avenues for support.

Background A developing body of evidence has provided valuable insight into the experiences of caregivers of people with motor neuron disease; however, understandings of how best to support caregivers remain limited.

Aim This study sought to understand concepts related to the motor neuron disease caregiver experience which could inform the development of supportive interventions.

Design A qualitative thematic analysis of a one-off semistructured interview with caregivers was undertaken.

Setting/participants Caregivers of people with motor neuron disease were recruited from a progressive neurological diseases clinic in Melbourne, Australia.

Results 15 caregivers participated. Three key themes were identified: (1) The Thief: the experience of loss and grief across varied facets of life; (2) The Labyrinth: finding ways to address ever changing challenges as the disease progressed; (3) Defying fate: being resilient and hopeful as caregivers tried to make the most of the time remaining.

Conclusions Caregivers are in need of more guidance and support to cope with experiences of loss and to adapt to changeable care giving duties associated with disease progression. Therapeutic interventions which target these experiences of loss and change are worth investigation.

Exploring Relationships Between Vector-Borne Diseases and Landscape Architecture: Aedes aegypti, Aedes albopictus and Landscape Architecture

Thesis (Master's)--University of Washington, 2016-06

IoT Based Pain Management using Physiological Parameters

The continuous technology evaluation is benefiting our lives to a great extent. The evolution of Internet of things and deployment of wireless sensor networks is making it possible to have more connectivity between people and devices used extensively in our daily lives. Almost every discipline of daily life including health sector, transportation, agriculture etc. is benefiting from these technologies. There is a great potential of research and refinement of health sector as the current system is very often dependent on manual evaluations conducted by the clinicians. There is no automatic system for patient health monitoring and assessment which results to incomplete and less reliable heath information. Internet of things has a great potential to benefit health care applications by automated and remote assessment, monitoring and identification of diseases. Acute pain is the main cause of people visiting to hospitals. An automatic pain detection system based on internet of things with wireless devices can make the assessment and redemption significantly more efficient. The contribution of this research work is proposing pain assessment method based on physiological parameters. The physiological parameters chosen for this study are heart rate, electrocardiography, breathing rate and galvanic skin response. As a first step, the relation between these physiological parameters and acute pain experienced by the test persons is evaluated. The electrocardiography data collected from the test persons is analyzed to extract interbeat intervals. This evaluation clearly demonstrates specific patterns and trends in these parameters as a consequence of pain. This parametric behavior is then used to assess and identify the pain intensity by implementing machine learning algorithms. Support vector machines are used for classifying these parameters influenced by different pain intensities and classification results are achieved. The classification results with good accuracy rates between two and three levels of pain intensities shows clear indication of pain and the feasibility of this pain assessment method. An improved approach on the basis of this research work can be implemented by using both physiological parameters and electromyography data of facial muscles for classification.

Vector Graphics: From PostScript and Flash to SVG

The XML-based specification for Scalable Vector Graphics (SVG), sponsored by the World Wide Web consortium, allows for compact and descriptive vector graphics for the Web. SVG s domain of discourse is that of graphic primitives whose optional attributes express line thickness, fill patterns, text size and so on. These primitives have very different properties from those of traditional document components (e.g. sections, paragraphs etc.) that XML is normally called upon to express. This paper describes a set of three tools for creating SVG, either from first principles or via the conversion of existing formats. The ab initio generation of SVG is effected from a server-side CGI script, using a PERL library of drawing functions; later sections highlight the problems of converting Adobe PostScript and Macromedia s Shockwave format (SWF) into SVG.