Community Recreation and Leisure Activities in Adults with Developmental Disabilities who have Recently Relocated to Community-based Residences in Ontario

In 2009, the Ontario Government closed the last three remaining large-scale institutions for people with Developmental Disabilities (DD). The purpose of this study is to examine the community-based recreation and leisure activities of 87 adults with DD who have recently moved into the community. Study 1 provided a descriptive insight into the community recreation and leisure activities, and revealed that people with DD engage in low levels of community activities, however are reported to have the desire to engage more often. Staff reported that people with DD do not have the opportunities to engage in their preferable activities. Study 2 investigated the prbspective predictors of the number and frequency of community, recreation and leisure activities and found that a higher level of functioning predicted a greater number of community activities ([beta] = .26, P < .05), while both a higher level of functioning ([beta] = .38,p < .001) and greater preference ([beta] = .23. p < .05) predicted more frequent access to community activities. Future research and the implications of the findings for clinical practice and policy development were discussed.

The Effects of Body Posture on Emotion Perception: A Developmental and Theoretical Analysis

Previously, studies investigating emotional face perception - regardless of whether they involved adults or children - presented participants with static photos of faces in isolation. In the natural world, faces are rarely encountered in isolation. In the few studies that have presented faces in context, the perception of emotional facial expressions is altered when paired with an incongruent context. For both adults and 8- year-old children, reaction times increase and accuracy decreases when facial expressions are presented in an incongruent context depicting a similar emotion (e.g., sad face on a fear body) compared to when presented in a congruent context (e.g., sad face on a sad body; Meeren, van Heijnsbergen, & de Gelder, 2005; Mondloch, 2012). This effect is called a congruency effect and does not exist for dissimilar emotions (e.g., happy and sad; Mondloch, 2012). Two models characterize similarity between emotional expressions differently; the emotional seed model bases similarity on physical features, whereas the dimensional model bases similarity on underlying dimensions of valence an . arousal. Study 1 investigated the emergence of an adult-like pattern of congruency effects in pre-school aged children. Using a child-friendly sorting task, we identified the youngest age at which children could accurately sort isolated facial expressions and body postures and then measured whether an incongruent context disrupted the perception of emotional facial expressions. Six-year-old children showed congruency effects for sad/fear but 4-year-old children did not for sad/happy. This pattern of congruency effects is consistent with both models and indicates that an adult-like pattern exists at the youngest age children can reliably sort emotional expressions in isolation. In Study 2, we compared the two models to determine their predictive abilities. The two models make different predictions about the size of congruency effects for three emotions: sad, anger, and fear. The emotional seed model predicts larger congruency effects when sad is paired with either anger or fear compared to when anger and fear are paired with each other. The dimensional model predicts larger congruency effects when anger and fear are paired together compared to when either is paired with sad. In both a speeded and unspeeded task the results failed to support either model, but the pattern of results indicated fearful bodies have a special effect. Fearful bodies reduced accuracy, increased reaction times more than any other posture, and shifted the pattern of errors. To determine whether the results were specific to bodies, we ran the reverse task to determine if faces could disrupt the perception of body postures. This experiment did not produce congruency effects, meaning faces do not influence the perception of body postures. In the final experiment, participants performed a flanker task to determine whether the effect of fearful bodies was specific to faces or whether fearful bodies would also produce a larger effect in an unrelated task in which faces were absent. Reaction times did not differ across trials, meaning fearful bodies' large effect is specific to situations with faces. Collectively, these studies provide novel insights, both developmentally and theoretically, into how emotional faces are perceived in context.

Social Economy and Developmental Disabilities: An Employment Option that Promotes a Form of Authentic Work and Fosters Social Inclusion

Typical employment options for people with developmental disabilities are insufficient. Most employment opportunities that are community-based provide typical workplace and geographical inclusion but tend not to support social inclusion and "belonging". This study explored the innovative employment alternative of social businesses and considered this form of employment for persons with a developmental disability as a viable avenue for meaningful work and social inclusion. A total of six business partners with a developmental disability were interviewed; two partners from three separate worker owned businesses. The partners' descriptions of their job and their workplace composed the interpretative findings. The social businesses provided an avenue for this group of people who tend to be segregated in isolated workshops or marginalized in mainstream work environments and who feel a sense of being "outsiders" to participate in meaningful work in community settings. This group of partners described their job as authentic "work" and discussed the many skills and the work ethic learned from their employment opportunity. In addition to the instrumental aspects of the job, the partners also discussed the group autonomy and self-determination of being their own "bosses". The partners confidently expressed feeling valued, understood in the context of others with similar life experiences, attached to the workplace and connected to a larger community as important outcomes of their businesses. These criteria of social inclusion (Hall, 2010) were complemented by teamwork, friendship and ultimately, with a feeling of being genuine "insiders". Replication of this innovative employment model would be recommended for groups of marginalized people with DD in other geographic areas.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

The Social Enterprise as an Alternative Work Option for Persons with Developmental Disabilities: A Case Study of Staff Perspectives at Common Ground Co-operative

Traditional employment options for persons with developmental disabilities are lacking. Employment options available for persons with developmental disabilities are reflective of the medical and social model perspectives of disability; with segregated and supported employment reinforcing the idea that persons with developmental disabilities are incapable and competitive employment missing the necessary accommodations for persons to be successful. This study examined social enterprises as an alternative employment option that can balance both medical and social model perspectives by accommodating for weaknesses or limitations and recognizing the strengths and capabilities of persons with developmental disabilities in the workplace. Moreover, this study is part of a broader case study which is examining the nature and impacts of a social enterprise, known as Common Ground Co-operative (CGC), which supports five social purpose businesses that are owned and operated by persons with developmental disabilities. This study is part of the Social Business and Marginalized Social Groups Community-University Research Alliance. To date, a case study has been written describing the nature and impacts of CGC and its related businesses from the perspectives of the Partners, board members, funders and staff (Owen, Readhead, Bishop, Hope & Campbell, in press & Readhead, 2012). The current study used a descriptive case study approach to provide a detailed account of the perceptions and opinions of CGC staff members who support each of the Partners in the five related businesses. Staff members were chosen for the focus of this study because of the integral role that they play in the successful outcomes of the persons they support. This study was conducted in two phases. In the first phase five staff members were interviewed. During this stage of interviews, several themes were presented which needed to be examined in further detail, specifically staff stress and burnout and duty of care for business Partners versus the promotion of their autonomy. A second phase of interviews was then conducted with one individual participant and a focus group of seven. During both interview phases, Staff participants described an employment model that creates a non-judgemental environment for the business Partners that promotes their strengths, accommodates for their limitations, provides educational opportunities and places the responsibility for the businesses on the persons with developmental disabilities cultivating equality and promoting independence. Staff described the nature of their role including risk factors for stress, the protective factors that buffer stress, and the challenges associated with balancing many role demands. Issues related to the replication of this social enterprise model are described.

Passive Solar Building Design Using Genetic Programming

Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition, window and volume based objectives are tried. To examine the impact of environment on designs, experiments are run on five different geographic locations. Also, both single floor models and multi-floor models are examined in this research. According to the experiments, solutions from the experiments were consistent with respect to materials, sizes, and appearance, and satisfied problem constraints in all instances.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

ARTERIAL STIFFNESS IN CHILDREN WITH AND WITHOUT DEVELOPMENTAL COORDINATION DISORDER

The purpose of this study was to determine whether children with potential developmental coordination disorder (p-DCD) demonstrate increased arterial stiffness and thickness compared to age and school matched controls (mean age 14.7 yrs). We also assessed whether these measures differed by sex. Compliance, distensibility, and intima-media thickness (IMT) were measured at the common carotid artery for 28 children with p-DCD and 47 controls. ECG-R-wave-toe pulse wave velocity (PWV) was also measured for 29 children with p-DCD and 45 controls. We found that compared to controls males with p-DCD had significantly higher PWV (3.8±0.2 vs. 4.1±0.3, p=0.001) and lower distensibility (0.82± 0.19 vs. 0.70± 0.17, p=0.034) while females showed no significant differences (p=0.523 and p=0.123 respectively). As a result, it is apparent that sex differences exist with respect to arterial health within this population and that children with p-DCD may be more likely to develop cardiovascular disease later in life.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Isoflavone exposure throughout suckling results in improved adult bone health in mice

Exposure to isoflavones (ISO), abundant in soy protein infant formula, for the first 5 days of life results in higher bone mineral density (BMD),greater trabecular connectivity and higher fracture load of lumbar vertebrae (LV) at adulthood. The effect of lengthening the duration of exposure to ISO on bone development has not been studied. This study determined if providing ISO for the first 21 days of life, which more closely mimics the duration that infants are fed soy protein formula, results in higher BMD, improved bone structure and greater strength in femurs and LV than a 5-day protocol. Female CD-1 mice were randomized to subcutaneous injections of ISO (7 Q1 mg kg/body weight/day) or corn oil from postnatal day 1 to 21. BMD, structure and strength were measured at the femur and LV at 4 months of age, representing young Q2 adulthood. At the LV, exposure to ISO resulted in higher (P,0.05) BMD, trabecular connectivity and fracture load compared with control (CON). Exposure to ISO also resulted in higher (P,0.05) whole femur BMD, higher (P,0.05) bone volume/total volume and Q3 lower (P,0.05) trabecular separation at the femur neck, as well as greater (P,0.05) fracture load at femur midpoint and femur neck compared with the CON group. Exposure to ISO throughout suckling has favorable effects on LV outcomes, and, unlike previous studies using 5-day exposure to ISO, femur outcomes are also improved. Duration of exposure should be considered when using the CD-1 mouse to model the effect of early life exposure of infants to ISO.

The Association of Energy Intake with Body Mass in Children With and Without Probable Developmental Coordination Disorder

Objective To determine if there is an association between energy intake (EI) and overweight or obesity status (OWOB) in children with and without probable developmental coordination disorder (p-DCD). Methods 1905 children were included. The Bruininks-Oseretsky Test of Motor Proficiency was used to assess p-DCD, body mass index for OWOB, and the Harvard Food Frequency Questionnaire for EI. Comparative tests and logistic regressions were performed. Results Reported EI was similar between p-DCD and non-DCD children among boys (2291 vs. 2281 kcal/day, p=0.917), but much lower in p-DCD compared to non-DCD girls (1745 vs.. 2068 kcal/day, p=0.007). EI was negatively associated with OWOB in girls only (OR: 0.82 (0.68, 0.98)). Conclusions Girls with p-DCD have a lower reported EI compared to their non-DCD peers. EI is negatively associated with OWOB in girls with p-DCD. Future research is needed to assess longitudinally the potential impact of EI on OWOB in this population.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Inverse Illumination Design with Genetic Programming

Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.