Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.

An exploration of gifted and highly able adolescents' experiences with stress and coping /

This narrative case study explored gifted and highly able adolescents' experiences with stress and coping. Nine students, ages 13-18, at 2 independent schools in southern Ontario, participated. They completed the Adolescent Coping Scale (Frydenberg & Lewis, 1993), and I generated individualized graphs of coping strategies. Participants talked about experiences they perceived as stressful in their academic, personal, social, and familial settings during a 60-90 minute one-on-one audiotaped interview. During the interview, each participant made observations about their own coping strategies profile. The interview was analyzed to identify stressor and coping themes. Participants completed a writing or art task to record perceptions of stress and coping. The 3 data sources were used to craft 9 individual story portraits, from which 5 main stressor themes emerged: issues of time; relationships, emotions, and communication; ethical, moral, and spiritual issues; global issues; and silences, or stressors not talked about in depth. Coping themes were: seeking relaxing activities; having positive attitudes and making wise choices; maintaining relationships with peers and family; understanding the role of faith and moral beliefs; having a supportive environment; knowing your own personality type; being aware of negative coping strategies; and keeping busy and avoiding stressfiil issues. The narratives are important because they present teenagers talking about their socioemotional worlds. The present findings provide empirical groundwork for curriculum development in affective education and highlight the importance of socioemotional development for future research in the area of giftedness and adolescence.

Environmental assessment of Lake Gibson sediments, water quality, and soils of the Niagara Region

In light of the fact that literature on toxicity of heavy metals in non-acidified freshwater systems is sparse, this project was initiated to conduct an environmental assessment of Lake Gibson. Chemistry of soils from adjacent areas and vineyards in the region provide a comparative background database. Water quality determinations were used to identify and highlight areas of environmental concern within the Lake Gibson watershed. A Shelby Corer was used to obtain 66 sediment cores from Lake Gibson. These were sectioned according to lithology and color to yield 298 samples. A suite of 122 soil samples was collected in the region and vicinity of Lake Gibson. All were tested for metals and some for Total Petroleum Hydrocarbons (TPH). Evaluation of the results leads to the following conclusions: 1. Metal concentrations ofAI, Cd, Cu, Cr, Pb, Ni, Fe and Zn in soils from the Niagara Region are well below background limits set by the Ministry of the Environment and Energy (MOEE) for provincial soils. 2. There is a spatial and depth difference for some of the metals within the various soils. The Cr, Ni and Pb contents of soils vary throughout the region (pand in some instances they also exceed the SEL (Severe Effect Level) guideline. In this instance acute toxicity testing of 11 the sediments is required to assess impact on the aquatic biota. 5. Specifically, effluents and discharges from outfalls, roadways, railways and industrial activities are all degrading the local ecosystem. 6. Mineral oil and greases are a major environmental concern in the sediments of Lake Gibson. Ofthe 240 samples tested for TPH, 200 samples exceed the MOEE Open Water Disposal Guideline of 1,500 mg/kg. 7. Four areas within Lake Gibson are especially degraded with respect to TPH. One area is just downstream from the Old WeIland Canal divergence point and waterfall. Other areas of concern are located just south of Beaverdams Road and just west ofthe Ontario Hydro control pipes; south ofthe Village ofBeaverdams. The fourth area of environmental concern and TPH impact is located between Highway 406 and Merrittville Highway.

Perceived parental monitoring, adolescent disclosure, and adolescent depressive symptoms : a longitudinal examination

Parental monitoring has long been stressed as an important parenting practice in reducing adolescent susceptibility to depression. An extensive review by Stattin and Kerr (2000), however, , revealed that researchers had confounded perceptions of parental monitoring (i.e., parental solicitation and control) with parental knowledge, and neglected to consider the role of adolescent willingness to disclose. In the present study, adolescents (N = 1995; 51.3% female) were surveyed at two time points (grade 10 and 11). To disentangle the role of perceived parenting, three central issues were addressed. First, the present study examined whether parental knowledge, adolescent disclosure, and parental monitoring (i.e., parental solicitation and control) in grade 10 predicted adolescent depression in grade 11. Second, the predictive value of adolescent depression in grade lOon parental knowledge, adolescent disclosure, parental solicitation and parental control in grade 11 was considered. Lastly, associations among parental knowledge, adolescent disclosure, parental solicitation and parental control were examined over time. Findings indicated that higher levels of parental knowledge were associated with subsequent lower levels of depressive symptoms, and that depressive symptoms predicted lower levels of parental knowledge over time. Both adolescent willingness to disclose and parental control predicted higher parental knowledge. These findings underscore the role of adolescent and perceived parent contributions to parental knowledge, and highlight the importance of perceived parental knowledge in predicting reduced adolescent susceptibility to depression.

Human Resource Training and National Sport Organization Managers: Examining the Impact of Training on Individual and Organizational Performance

Within sport, a tremendous amount of effort is committed to the on-the-field performance of athletes and coaches, neglecting the off-the-field performance and development of sport managers. This study examines the impact of human resource training on the performance of five Canadian national sport organizations (NSO) and their managers (N=22). Data were collected on three outcome variables (learning, individual performance, organizational performance) and three mediating variables (motivation to transfer, training design, organizational climate) at three time measures (pre-training, post-training1, post-training2). Results indicate that training improves the learning and individual performance of sport managers, as well as the organizational performance of NSOs. Varying relationships were found at each of the three time measures, demonstrating that a progression to training-related performance change exists, while providing support for three levels of analysis (individual, organizational, systemic). Implications and future research directions are discussed and highlight the need for on-going training opportunities for Canadian sport managers.

The Salmon Algorithm - A New Population Based Search Metaheuristic

This thesis introduces the Salmon Algorithm, a search meta-heuristic which can be used for a variety of combinatorial optimization problems. This algorithm is loosely based on the path finding behaviour of salmon swimming upstream to spawn. There are a number of tunable parameters in the algorithm, so experiments were conducted to find the optimum parameter settings for different search spaces. The algorithm was tested on one instance of the Traveling Salesman Problem and found to have superior performance to an Ant Colony Algorithm and a Genetic Algorithm. It was then tested on three coding theory problems - optimal edit codes, optimal Hamming distance codes, and optimal covering codes. The algorithm produced improvements on the best known values for five of six of the test cases using edit codes. It matched the best known results on four out of seven of the Hamming codes as well as three out of three of the covering codes. The results suggest the Salmon Algorithm is competitive with established guided random search techniques, and may be superior in some search spaces.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Identification and characterization of retinoic acid-induced morphological and electrophysiological changes in an invertebrate nervous system

The vitamin A metabolite, retinoic acid (RA) is known to play an important role in the development, patterning and regeneration of nervous tissue, both in the embryo and in the adult. Classically, RA is known to mediate the transcription of target genes through the binding and activation ofits nuclear receptors: the retinoic acid receptors (RARs) and retinoid X receptors (RXRs). Recently, mounting evidence from many animal models has implicated a number of RA-mediated effects operating independently of gene transcription, and thus highlights nove~ nongenornic actions of RA. For example, recent work utilizing cultured neurons from the pond snaa Lymnaea stagnalis, has shown that RA can elicit a regenerative response, growth cone turning, independently of "classical" transcriptional activation While this work illustrates a novel regeneration-inducing effect in culture, it is currently -unknown whether RA also induces regeneration in situ. This study has sought to determine RA's regenerative effucts at the morphological and molecular levels by utilizing an in situ approach focusing on a single identified dopaminergic neuron which possesses a known "mapped" morphology within the CNS. These studies show, for the first time in an invertebrate, that RA can increase neurite outgrowth of dopaminergic cells that have undergone a nerve-crush injury. Utilizing Western blot analysis, it was shown that this effect appears to be independent of any changes in whole CNS expression levels of either the RAR or RXR. Additionally, utilizing immunohistochemistry, to examine protein localization, there does not appear to be any obvious changes in the RXR expression level at the crush site. Changes in cell morphology such as neurity extension are known to be modulated by changes in neuronal firing activity. It has been previously shown that exposure to RA over many days can lead to changes in the electrophysiological properties of cultured Lymnaea neurons; however, no studies have investigated whether short-term exposure to RA can elicit electrophysiological changes and/or changes in firing pattern of neurons in Lymnaea or any other species. The studies performed here show, for the first time in any species, that short-tenn treatment with RA can elicit significant changes in the firing properties of both identified dopaminergic neurons and peptidergic neurons. This effect appears to be independent of protein synthesis, activation of protein kinase A or phospholipase C, and calcium influx but is both dose-dependent and isomer-dependent. These studies provide evidence that the RXR, but not RAR, may be involved, and that intracellular calcium concentrations decrease upon RAexposure with a time course, dose-dependency and isomer-dependency that coincide with the RA-induced electrophysiological changes. Taken together, these studies provide important evidence highlighting RA as a multifunctional molecule, inducing morphological, molecular and electrophysiological changes within the CNS, and highlight the many pathways through which RA may operate to elicit its effects.

Investigating active transportation to and from school : identification of predictors and health benefits

Funding support for this doctoral thesis has been provided by the Canadian Institutes of Health Research-Public Health Agency of Canada, QICSS matching grant, and la Faculté des études supérieures et postdoctorales-Université de Montréal.

Models and algorithms for the capacitated location-routing problem

Le problème de localisation-routage avec capacités (PLRC) apparaît comme un problème clé dans la conception de réseaux de distribution de marchandises. Il généralisele problème de localisation avec capacités (PLC) ainsi que le problème de tournées de véhicules à multiples dépôts (PTVMD), le premier en ajoutant des décisions liées au routage et le deuxième en ajoutant des décisions liées à la localisation des dépôts. Dans cette thèse on dévelope des outils pour résoudre le PLRC à l’aide de la programmation mathématique. Dans le chapitre 3, on introduit trois nouveaux modèles pour le PLRC basés sur des flots de véhicules et des flots de commodités, et on montre comment ceux-ci dominent, en termes de la qualité de la borne inférieure, la formulation originale à deux indices [19]. Des nouvelles inégalités valides ont été dévelopées et ajoutées aux modèles, de même que des inégalités connues. De nouveaux algorithmes de séparation ont aussi été dévelopés qui dans la plupart de cas généralisent ceux trouvés dans la litterature. Les résultats numériques montrent que ces modèles de flot sont en fait utiles pour résoudre des instances de petite à moyenne taille. Dans le chapitre 4, on présente une nouvelle méthode de génération de colonnes basée sur une formulation de partition d’ensemble. Le sous-problème consiste en un problème de plus court chemin avec capacités (PCCC). En particulier, on utilise une relaxation de ce problème dans laquelle il est possible de produire des routes avec des cycles de longueur trois ou plus. Ceci est complété par des nouvelles coupes qui permettent de réduire encore davantage le saut d’intégralité en même temps que de défavoriser l’apparition de cycles dans les routes. Ces résultats suggèrent que cette méthode fournit la meilleure méthode exacte pour le PLRC. Dans le chapitre 5, on introduit une nouvelle méthode heuristique pour le PLRC. Premièrement, on démarre une méthode randomisée de type GRASP pour trouver un premier ensemble de solutions de bonne qualité. Les solutions de cet ensemble sont alors combinées de façon à les améliorer. Finalement, on démarre une méthode de type détruir et réparer basée sur la résolution d’un nouveau modèle de localisation et réaffectation qui généralise le problème de réaffectaction [48].

The effect of lesion size on cortical reorganization in the ipsi and contralesional hemispheres.

Bien que la plasticité ipsilesionnelle suite à un accident vasculo-cérébral (AVC) soit bien établie, la réorganisation du cortex contralésionnel et son effet sur la récupération fonctionnelle restent toujours non élucidés. Les études publiées présentent des points de vue contradictoires sur le rôle du cortex contralésionnel dans la récupération fonctionnelle. La taille de lésion pourrait être le facteur déterminant la réorganisation de ce dernier. Le but principal de cette étude fut donc d’évaluer l’effet des AVC de tailles différentes dans la région caudal forelimb area (CFA) du rat sur la réorganisation physiologique et la récupération comportementale de la main. Suite à une période de récupération spontanée pendant laquelle la performance motrice des deux membres antérieurs fut observée, les cartes motrices bilatérales du CFA et du rostral forelimb area (RFA) furent obtenues. Nous avons trouvé que le volume de lésion était en corrélation avec le niveau de récupération comportementale et l’étendue de la réorganisation des RFA bilatéraux. Aussi, les rats ayant de grandes lésions avaient des plus grandes représentations de la main dans le RFA de l’hémisphère ipsilésionnel et un déficit de fonctionnement plus persistant de la main parétique. Dans l’hémisphère contralésionnel nous avons trouvé que les rats avec des plus grandes représentations de la main dans le RFA avaient des lésions plus grandes et une récupération incomplète de la main parétique. Nos résultats confirment l’effet du volume de lésion sur la réorganisation du cortex contralésionnel et soulignent que le RFA est l’aire motrice la plus influencée dans le cortex contralésionnel.

Analysis and Optimization of Machining Process Using Evolutionary Algorithms

To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Learning disability prediction tool using ANN and ANFIS

Learning Disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively

A Detailed Look at Scale and Translation Invariance in a Hierarchical Neural Model of Visual Object Recognition

The HMAX model has recently been proposed by Riesenhuber & Poggio as a hierarchical model of position- and size-invariant object recognition in visual cortex. It has also turned out to model successfully a number of other properties of the ventral visual stream (the visual pathway thought to be crucial for object recognition in cortex), and particularly of (view-tuned) neurons in macaque inferotemporal cortex, the brain area at the top of the ventral stream. The original modeling study only used ``paperclip'' stimuli, as in the corresponding physiology experiment, and did not explore systematically how model units' invariance properties depended on model parameters. In this study, we aimed at a deeper understanding of the inner workings of HMAX and its performance for various parameter settings and ``natural'' stimulus classes. We examined HMAX responses for different stimulus sizes and positions systematically and found a dependence of model units' responses on stimulus position for which a quantitative description is offered. Interestingly, we find that scale invariance properties of hierarchical neural models are not independent of stimulus class, as opposed to translation invariance, even though both are affine transformations within the image plane.