Back to School, Back to Normal? Exploring the Lived Experiences of Childhood Cancer Survivors and their Families Throughout the School Re-Entry Process

This study investigated parent and child perspectives of childhood cancer survivors' return to school. Four specific areas were examined including cognitive and academic concerns, social issues, perceived support, and the impact on siblings. Participants consisted of parents and childhood cancer survivors who were recruited through a regional parent support group. Data was collected during a focus group and interviews. Using a descriptive content analysis, results indicated that participants generally received the necessary resources, however issues such as consistency and having to advocate in order to attain these resources served as barriers for the families.

Sustainability, Fashion Design, and Consumerism: Undergraduate Entry Level Fashion Students' Knowledge, Attitudes, and Habits

This qualitative inquiry explored 7 undergraduate students' attitudes, habits, and knowledge of consumerism, fashion design, and sustainability. The postmodern study employed crystallization as its methodological framework to gain insight into how participants' knowledge is manifested in their daily habits, and used 4 methods of data gathering: semistructured interviews, visual exercises, journal entries, and the researcher's own reflections. Four major themes emerged: Knowledge-Concepts Linked and Fragmented; Dissonance Between Knowledge Versus Attitudes and Consumer Habits; Surrendering to the Unsustainable Structures; Design Process and Caring Attitude. Findings indicate that participants possessed some knowledge of sustainability but lacked a well-rounded understanding of environmental and humanitarian implications of Western consumer society. Findings also reveal a dissonance between participants' knowledge and attitudes-affecting how their knowledge influences their behaviour-and how reflection, creative thinking, and drawing initiate change in participants' underlying attitudes. Recommendations are made to merge a variety of theoretical frameworks into the educational system in order to create curricula that offer a holistic overview and unique insights into sustainability challenges, particularly in specialized areas of the fashion industry.

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.

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.

The National Hockey League (NHL) Entry Draft: An Examination of the Impact of Brand Associations Attached to the Russian Hockey Players

This research was conducted to investigate whether negative brand associations attached to Russian hockey players impact their draft rankings during the National Hockey League (NHL) Entry Draft. A quantitative analysis based on various regression model specifications was used to test whether Russian players were drafted relatively equally to their counterparts in the NHL Entry Draft. The data consisted of the NHL draft picks between 1993 and 2013 and their performance statistics and physical characteristics. The results suggested that Russian players were drafted relatively equal to their counterparts from other countries. Meanwhile, Russian players who played in the CHL before the draft are actually drafted better than Canadians who played in the same league. Hence, the negative brand associations attached to Russians were unlikely to impact their draft rankings. This study redefined the so-called “Russian Factor” from a notion that allegedly damages Russian players’ rankings to one that enhances their rankings.

AutoFACT: An Automatic Functional Annotation and Classification Tool

Affiliation: Centre Robert-Cedergren de l'Université de Montréal en bio-informatique et génomique & Département de biochimie, Université de Montréal

Automatic Optic Disc Boundary Extraction from Color Fundus Images

Efficient optic disc segmentation is an important task in automated retinal screening. For the same reason optic disc detection is fundamental for medical references and is important for the retinal image analysis application. The most difficult problem of optic disc extraction is to locate the region of interest. Moreover it is a time consuming task. This paper tries to overcome this barrier by presenting an automated method for optic disc boundary extraction using Fuzzy C Means combined with thresholding. The discs determined by the new method agree relatively well with those determined by the experts. The present method has been validated on a data set of 110 colour fundus images from DRION database, and has obtained promising results. The performance of the system is evaluated using the difference in horizontal and vertical diameters of the obtained disc boundary and that of the ground truth obtained from two expert ophthalmologists. For the 25 test images selected from the 110 colour fundus images, the Pearson correlation of the ground truth diameters with the detected diameters by the new method are 0.946 and 0.958 and, 0.94 and 0.974 respectively. From the scatter plot, it is shown that the ground truth and detected diameters have a high positive correlation. This computerized analysis of optic disc is very useful for the diagnosis of retinal diseases

GRASS GIS for the distinction of vegetation from buildings using LiDAR altimetric data

La tecnología LiDAR (Light Detection and Ranging), basada en el escaneado del territorio por un telémetro láser aerotransportado, permite la construcción de Modelos Digitales de Superficie (DSM) mediante una simple interpolación, así como de Modelos Digitales del Terreno (DTM) mediante la identificación y eliminación de los objetos existentes en el terreno (edificios, puentes o árboles). El Laboratorio de Geomática del Politécnico de Milán – Campus de Como- desarrolló un algoritmo de filtrado de datos LiDAR basado en la interpolación con splines bilineares y bicúbicas con una regularización de Tychonov en una aproximación de mínimos cuadrados. Sin embargo, en muchos casos son todavía necesarios modelos más refinados y complejos en los cuales se hace obligatorio la diferenciación entre edificios y vegetación. Este puede ser el caso de algunos modelos de prevención de riesgos hidrológicos, donde la vegetación no es necesaria; o la modelización tridimensional de centros urbanos, donde la vegetación es factor problemático. (...)

Overview of coded light projection techniques for automatic 3D profiling

Obtaining automatic 3D profile of objects is one of the most important issues in computer vision. With this information, a large number of applications become feasible: from visual inspection of industrial parts to 3D reconstruction of the environment for mobile robots. In order to achieve 3D data, range finders can be used. Coded structured light approach is one of the most widely used techniques to retrieve 3D information of an unknown surface. An overview of the existing techniques as well as a new classification of patterns for structured light sensors is presented. This kind of systems belong to the group of active triangulation method, which are based on projecting a light pattern and imaging the illuminated scene from one or more points of view. Since the patterns are coded, correspondences between points of the image(s) and points of the projected pattern can be easily found. Once correspondences are found, a classical triangulation strategy between camera(s) and projector device leads to the reconstruction of the surface. Advantages and constraints of the different patterns are discussed

Geostatistics for constrained variables: positive data, compositions and probabilities. Applications to environmental hazard monitoring

Aquesta tesi estudia com estimar la distribució de les variables regionalitzades l'espai mostral i l'escala de les quals admeten una estructura d'espai Euclidià. Apliquem el principi del treball en coordenades: triem una base ortonormal, fem estadística sobre les coordenades de les dades, i apliquem els output a la base per tal de recuperar un resultat en el mateix espai original. Aplicant-ho a les variables regionalitzades, obtenim una aproximació única consistent, que generalitza les conegudes propietats de les tècniques de kriging a diversos espais mostrals: dades reals, positives o composicionals (vectors de components positives amb suma constant) són tractades com casos particulars. D'aquesta manera, es generalitza la geostadística lineal, i s'ofereix solucions a coneguts problemes de la no-lineal, tot adaptant la mesura i els criteris de representativitat (i.e., mitjanes) a les dades tractades. L'estimador per a dades positives coincideix amb una mitjana geomètrica ponderada, equivalent a l'estimació de la mediana, sense cap dels problemes del clàssic kriging lognormal. El cas composicional ofereix solucions equivalents, però a més permet estimar vectors de probabilitat multinomial. Amb una aproximació bayesiana preliminar, el kriging de composicions esdevé també una alternativa consistent al kriging indicador. Aquesta tècnica s'empra per estimar funcions de probabilitat de variables qualsevol, malgrat que sovint ofereix estimacions negatives, cosa que s'evita amb l'alternativa proposada. La utilitat d'aquest conjunt de tècniques es comprova estudiant la contaminació per amoníac a una estació de control automàtic de la qualitat de l'aigua de la conca de la Tordera, i es conclou que només fent servir les tècniques proposades hom pot detectar en quins instants l'amoni es transforma en amoníac en una concentració superior a la legalment permesa.

A general-method for tracking analysis and its application to meteorological data

Recent interest in the validation of general circulation models (GCMs) has been devoted to objective methods. A small number of authors have used the direct synoptic identification of phenomena together with a statistical analysis to perform the objective comparison between various datasets. This paper describes a general method for performing the synoptic identification of phenomena that can be used for an objective analysis of atmospheric, or oceanographic, datasets obtained from numerical models and remote sensing. Methods usually associated with image processing have been used to segment the scene and to identify suitable feature points to represent the phenomena of interest. This is performed for each time level. A technique from dynamic scene analysis is then used to link the feature points to form trajectories. The method is fully automatic and should be applicable to a wide range of geophysical fields. An example will be shown of results obtained from this method using data obtained from a run of the Universities Global Atmospheric Modelling Project GCM.

Detection of flood extent in urban areas using high resolution TerraSAR-X data

Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. An investigation of the ability of high resolution TerraSAR-X data to detect flooded regions in urban areas is described. An important application for this would be the calibration and validation of the flood extent predicted by an urban flood inundation model. To date, research on such models has been hampered by lack of suitable distributed validation data. The study uses a 3m resolution TerraSAR-X image of a 1-in-150 year flood near Tewkesbury, UK, in 2007, for which contemporaneous aerial photography exists for validation. The DLR SETES SAR simulator was used in conjunction with airborne LiDAR data to estimate regions of the TerraSAR-X image in which water would not be visible due to radar shadow or layover caused by buildings and taller vegetation, and these regions were masked out in the flood detection process. A semi-automatic algorithm for the detection of floodwater was developed, based on a hybrid approach. Flooding in rural areas adjacent to the urban areas was detected using an active contour model (snake) region-growing algorithm seeded using the un-flooded river channel network, which was applied to the TerraSAR-X image fused with the LiDAR DTM to ensure the smooth variation of heights along the reach. A simpler region-growing approach was used in the urban areas, which was initialized using knowledge of the flood waterline in the rural areas. Seed pixels having low backscatter were identified in the urban areas using supervised classification based on training areas for water taken from the rural flood, and non-water taken from the higher urban areas. Seed pixels were required to have heights less than a spatially-varying height threshold determined from nearby rural waterline heights. Seed pixels were clustered into urban flood regions based on their close proximity, rather than requiring that all pixels in the region should have low backscatter. This approach was taken because it appeared that urban water backscatter values were corrupted in some pixels, perhaps due to contributions from side-lobes of strong reflectors nearby. The TerraSAR-X urban flood extent was validated using the flood extent visible in the aerial photos. It turned out that 76% of the urban water pixels visible to TerraSAR-X were correctly detected, with an associated false positive rate of 25%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 58% and 19% respectively. These findings indicate that TerraSAR-X is capable of providing useful data for the calibration and validation of urban flood inundation models.

Tumour necrosis factor alpha induces rapid reduction in AMPA receptor-mediated calcium entry in motor neurones by increasing cell surface expression of the GluR2 subunit: relevance to neurodegeneration

The AMPA receptor (AMPAR) subunit GluR2, which regulates excitotoxicity and the inflammatory cytokine tumour necrosis factor alpha (TNF alpha) have both been implicated in motor neurone vulnerability in Amyotrophic Lateral Sclerosis/Motor Neurone Disease. TNF alpha has been reported to increase cell surface expression of AMPAR subunits to increase synaptic strength and enhance excitotoxicity, but whether this mechanism occurs in motor neurones is unknown. We used primary cultures of mouse motor neurones and cortical neurones to examine the interaction between TNF alpha receptor activation, GluR2 availability, AMPAR-mediated calcium entry and susceptibility to excitotoxicity. Short exposure to a physiologically relevant concentration of TNFalpha (10 ng/ml, 15 min) caused a marked redistribution of both GluR1 and GluR2 to the cell surface as determined by cell surface biotinylation and immunofluorescence. Using Fura-2 AM microfluorimetry we showed that exposure to TNFalpha caused a rapid reduction in the peak amplitude of AMPA-mediated calcium entry in a PI3-kinase and p38 kinase-dependent manner, consistent with increased insertion of GluR2-containing AMPAR into the plasma membrane. This resulted in a protection of motor neurones against kainate-induced cell death. Our data therefore, suggests that TNF alpha acts primarily as a physiological regulator of synaptic activity in motor neurones rather than a pathological drive in ALS

Automated pre-processing strategies for species occurrence data used in biodiversity modelling

To construct Biodiversity richness maps from Environmental Niche Models (ENMs) of thousands of species is time consuming. A separate species occurrence data pre-processing phase enables the experimenter to control test AUC score variance due to species dataset size. Besides, removing duplicate occurrences and points with missing environmental data, we discuss the need for coordinate precision, wide dispersion, temporal and synonymity filters. After species data filtering, the final task of a pre-processing phase should be the automatic generation of species occurrence datasets which can then be directly ’plugged-in’ to the ENM. A software application capable of carrying out all these tasks will be a valuable time-saver particularly for large scale biodiversity studies.

Cell entry by enveloped viruses: redox considerations for HIV and SARS-coronavirus

For enveloped viruses, genome entry into the target cell involves two major steps: virion binding to the cell-surface receptor and fusion of the virion and cell membranes. Virus-cell membrane fusion is mediated by the virus envelope complex, and its fusogenicity is the result of an active virus-cell interaction process that induces conformation changes within the envelope. For some viruses, such as influenza, exposure to an acidic milieu within the cell during the early steps of infection triggers the necessary structural changes. However, for other pathogens which are not exposed to such environmental stress, activation of fusogenicity can result from precise thiol/disulfide rearrangements mediated by either an endogenous redox autocatalytic isomerase or a cell-associated oxidoreductase. Study of the activation of HIV envelope fusogenicity has revealed new knowledge about how redox changes within a viral envelope trigger fusion. We discuss these findings and their implication for anti-HIV therapy. In addition, to compare and contrast the situation outlined for HIV with an enveloped virus that can fuse with the cell plasma membrane independent of the redox status of its envelope protein, we review parallel data obtained on SARS coronavirus entry.