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Purpose: Infrared (IR) spectroscopy has the potential for tumor delineation in neurosurgery. Previous research showed that IR spectra of brain tumors are generally characterized by reduced lipid-related and increased protein-related bands. Therefore, we propose the exploitation of these common spectral changes for brain tumor recognition. Methods: Attenuated total reflection IR spectroscopy was performed on fresh specimens of 790 patients within minutes after resection. Using principal component analysis and linear discriminant analysis, a classification model was developed on a subset of glioblastoma (n = 135) and non-neoplastic brain (n = 27) specimens, and then applied to classify the IR spectra of several types of brain tumors. Results The model correctly classified 82% (517/628) of specimens as “tumor” or “non-tumor”, respectively. While the sensitivity was limited for infiltrative glioma, this approach recognized GBM (86%), other types of primary brain tumors (92%) and brain metastases (92%) with high accuracy and all non-tumor samples were correctly identified. Conclusion: The concept of differentiation of brain tumors from non-tumor brain based on a common spectroscopic tumor signature will accelerate clinical translation of infrared spectroscopy and related technologies. The surgeon could use a single instrument to detect a variety of brain tumor types intraoperatively in future clinical settings. Our data suggests that this would be associated with some risk of missing infiltrative regions or tumors, but not with the risk of removing non-tumor brain.

The United Nations established Sustainable Development Goal 6, universal access to safely managed drinking water and sanitation service, as a global goal for 2030. In rural areas, access lags significantly and progress is rarely examined concurrently between developed and developing nations. Therefore, this dissertation focuses on rural water system challenges in a developed nation, the US, and a developing nation, Guatemala. In the US, approximately 250 million Americans receive drinking water from community water systems (CWSs), theoretically safeguarded by the Safe Drinking Water Act (SDWA). There is mounting evidence that racial, ethnic, and socioeconomic disparities persist in US drinking water access and quality, but studies are limited by the exclusion of very small CWSs and a large geographic unit of analysis. A novel geospatial methodology was created to delineate system service areas at the zip code scale in Virginia and assess the influence of demographic characteristics on compliance with the SDWA from 2006 to 2016. Results reveal that monitoring and reporting violations are concentrated in private, rural systems that serve fewer than 500 people, while health-based violations were more likely in non-white communities, specifically those with higher proportions of Black, Native Hawaiian, and other Pacific Islanders. This study was completed in parallel with a household sampling campaign in rural San Rafael Las Flores, Guatemala. In Guatemala, no public access to water system compliance or quality information currently exists. With growing investment in mining industries and recognized naturally occurring arsenic in volcanic geology, citizens are eager for drinking water information. Survey results highlighted dissatisfaction with and distrust in most tap water sources. Consequently, residents regularly buy bottled water or collect water from untreated natural springs. Water quality results indicated that tap water from the central drinking water treatment plant contained higher levels of arsenic and other contaminants, when compared to most other sources. Though the settings are quite different, parallel investigation of rural drinking water system challenges in the US and Guatemala reveal common challenges and lessons. Moving forward, all nations would benefit from standard monitoring of drinking water access, quality, and compliance that allowed for intersectional investigations of environmental health inequities. / Doctor of Philosophy / In 2015, the United Nations established Sustainable Development Goal 6 which establishes safely managed drinking water and sanitation service for all as a global goal. Access to safe drinking water lags significantly in rural areas and can be complicated by intersecting social determinants of health (e.g. race, wealth). Rarely is progress in developed and developing nations examined concurrently, hindering an understanding of commonalities and an exchange of lessons. To this end, my dissertation focuses on rural water system challenges in a developed nation, the United States, and a developing nation, Guatemala. In the US, more than 250 million Americans receive in-home drinking water from one of 53,000 community water systems, with quality theoretically protected by the Safe Drinking Water Act (SDWA). Recent failures, such as the lead crisis in Flint, MI, have cast doubt on the equity and reliability of these utilities, especially in underserved areas. How can we ensure that all US communities receive equal protections under the Safe Drinking Water Act? Using publicly available data and geography, this work estimated service areas to determine whether SDWA violations related to surrounding community socio-demographics and/or system design. Results reveal that monitoring and reporting violations are significantly concentrated in private, rural systems that serve fewer than 500 people, while health-based violations were more likely in non-white communities, specifically those with higher proportions of Black, Native Hawaiian, and other Pacific Islanders. These findings illustrate potential issues of environmental justice within VA and advocate for future research to investigate potential structural causes. This work was completed in tandem with a household sampling campaign in rural San Rafael Las Flores, Guatemala. In Guatemala, there is currently no public access to water system compliance or quality information. With recognized naturally occurring carcinogenic elements in Guatemala's volcanic geology, such as arsenic, and heightened investment in extractive industries such as mining, that can compromise source water quality, citizens are eager for drinking water quality data. Survey results documented widespread dissatisfaction with and distrust in tap water quality. As a consequence, residents regularly buy bottled water or collect water from natural springs. Water quality results showed that tap water sourced from the central drinking water treatment plant contained significantly higher levels of arsenic and other contaminants when compared to most other tap sources. Community participation in long-term water monitoring and infrastructure decisions may help build trust in water sources. Though the regulatory, economic, and cultural settings are quite different, parallel investigation of rural drinking water system challenges in the US and Guatemala reveal common challenges and lessons. Moving forward, high, middle, and low-income nations all benefit from standard monitoring of drinking water access, quality, and compliance that allows for intersectional investigations of environmental health inequities.

Forest stand delineation is an essential task of forest management planning which can be time consuming and exposed to subjectivity. The increasing availability of LiDAR data and multispectral imagery offers an opportunity to improve stand delineation by means of remotely-sensed data. Under these premises, ASTER imagery and low-density LiDAR data have been used to automatically delineate forest stands in several forests of Navarra (Spain) through Object-Based Image Analysis (OBIA). Canopy cover, mean height and the canopy model have been extracted from LiDAR data and, along with VNIR ASTER bands, introduced in OBIA for forest segmentation. The outcome of segmentation has been contrasted, on the one hand, assessing segments’ inner heterogeneity. On the other, OBIA’s segments and existing stand delineations have been compared with a new method of geometrical fitting which has been ad hoc designed for this study. Results suggest that low-density LiDAR and multispectral data, along with OBIA, are a powerful tool for stand delineation. Multispectral images have a limited predicting utility for species differentiation and, in practical terms, they help to discriminate between broad-leaved, conifer and mixed stands. The performance of ASTER data, though, could be improved with higher spatial resolution VNIR imagery, specifically sub-metric VNIR orthophotos. LiDAR data, in contrast, offers a great potential for forest structure depiction. This perspective is connected with the increasingly higher resolution datasets which are to be provided by public institutions and the rapid development of drone technology. Complexity of OBIA may limit the use of this technique for small consulting firms but it is an advisable instrument for companies and institutions involved in major forestry projects. / No

L’hybridation est un processus aux conséquences diverses sur l’évolution des espèces qui est difficile à étudier lorsque les espèces se distinguent mal au niveau morphologique. Afin de comprendre le rôle de l'hybridation dans l’évolution du complexe d’espèces des chênes blancs européen, nous avons utilisé des outils de la génétique des populations pour quantifier les flux de gènes interspécifiques contemporains et étudier le système de reproduction de quatre espèces. Un protocole d'analyse génétique rapide a été développé et des méthodes d’assignations génétiques, permettant de déterminer l’espèce de chaque arbre et d’identifier les hybrides, ont été testées par simulations. Cette méthode a été appliquée en populations naturelles révélant un pourcentage d'hybrides variant de 10 à 30% en fonction des populations et impliquant tous les couples d'espèces. Nous avons montré que les effectifs des espèces dans les parcelles influencent la dynamique d'hybridation et la directionalité de l'introgression. Nous avons étudié le système de reproduction de ces espèces en croisements contrôlées et en forêt pour expliquer le maintien des espèces malgré la présence de flux de gènes interspécifiques. L'existence de plusieurs barrières reproductives contribue à un isolement partiel des espèces qui dépend principalement de barrières pré-reproductives et prézygotiques. Une analyse de paternité pratiquée sur des descendances récoltées en forêt montre que l'hybridation de première génération est rare mais que ces hybrides F1 sont fertiles et se reproduisent principalement avec l'une des espèces parentales, produisant de nombreux rétrocroisements qui expliquent le fort pourcentage d'hybrides observé dans les populations naturelles étudiées. L'hybridation et l'introgression sont donc des processus à l'œuvre chez les chênes qui contribuent à l'évolution du complexe d'espèces. / Hybridisation is a complex process with diverse consequences on species evolution. Hybridisation is difficult to study when species are not clearly morphologically distinguished. Our aim was to study the role of hybridisation in the evolution of the European white oak species complex. We used population genetic tools to quantify contemporary interspecific gene flow and to study the mating system of four oak species. A fast genetic analysis protocol was developed and genetic assignment methods were first tested by simulation and then used to determine the species of each tree and to identify hybrids. These methods revealed that hybrid percentages were between 10 to 30% depending on the natural population studied and that all species pairs were involved. We showed that the census number of species in the stands had an influence on hybridisation dynamics and on introgression direction. We studied the mating system of these species in controlled crosses and in the forest to understand the maintenance of species despite interspecific gene flow. Several reproductive barriers contribute to a partial isolation of species, mostly pre-reproductive and prezygotic. A paternity analysis of maternal progenies sampled in the forest showed that first generation hybridisation was rare but that F1 hybrids were fertile and were mating mostly with one of the two parental species, creating numerous backcrosses that explain the high percentages of hybrids observed in the natural populations studied. Hybridisation and introgression are active processes in oaks and contribute to the evolution of the species complex.

INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume delineation remains one of the greatest sources of error in the radiotherapy delivery process, which can lead to poor tumour control probability and impact clinical outcome. Contouring assessments are performed to ensure high quality of target volume definition in clinical trials but this can be subjective and labour-intensive. This project addresses the hypothesis that computational segmentation techniques, with a given prior, can be used to develop an image-based tumour delineation process for contour assessments. This thesis focuses on the exploration of the segmentation techniques to develop an automated method for generating reference delineations in the setting of advanced lung cancer. The novelty of this project is in the use of the initial clinician outline as a prior for image segmentation. METHODS: Automated segmentation processes were developed for stage II and III non-small cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed segmentation, two active contour approaches (edge- and region-based) and graph-cut applied on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from normal tissues based on texture features was also investigated. RESULTS: 63 cases were used for development and training. Segmentation and classification performance were evaluated on an independent test set of 16 cases. Edge-based active contour segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07, with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation leakages at the mediastinum were observed. In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and 15.5% were achieved using 16- and 8-pixel regions of interest (ROIs) respectively. Higher misclassification errors of 24.7% and 26.9% for 16- and 8-pixel ROIs were obtained in the analysis of the tumour boundary. CONCLUSIONS: Conventional image-based segmentation techniques with the application of priors are useful in automatic segmentation of tumours, although further developments are required to improve their performance. Texture classification can be useful in distinguishing tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more difficult. Future work with deep-learning segmentation approaches need to be explored.

Earthquake triggered landslides cause loss of life, destroy structures, roads, powerlines, and pipelines and therefore they have a direct impact on the social and economic life of the hazard region. The damage and fatalities directly related to strong ground shaking and fault rupture are sometimes exceeded by the damage and fatalities caused by earthquake triggered landslides. Even though future earthquakes can hardly be predicted, the identification of areas that are highly susceptible to landslide hazards is possible. For geographical information systems (GIS) based deterministic slope stability and earthquake-induced landslide analysis, the grid-cell approach has been commonly used in conjunction with the relatively simple infinite slope model. The infinite slope model together with Newmark's displacement analysis has been widely used to create seismic landslide susceptibility maps. The infinite slope model gives reliable results in the case of surficial landslides with depth-length ratios smaller than 0.1. On the other hand, the infinite slope model cannot satisfactorily analyze deep-seated coherent landslides. In reality, coherent landslides are common and these types of landslides are a major cause of property damage and fatalities. In the case of coherent landslides, two- or three-dimensional models are required to accurately analyze both static and dynamic performance of slopes. These models are rarely used in GIS-based landslide hazard zonation because they are numerically expensive compared to one dimensional infinite slope models. Building metamodels based on data obtained from computer experiments and using computationally inexpensive predictions based on these metamodels has been widely used in several engineering applications. With these soft computing methods, design variables are carefully chosen using a design of experiments (DOE) methodology to cover a predetermined range of values and computer experiments are performed at these chosen points. The design variables and the responses from the computer simulations are then combined to construct functional relationships (metamodels) between the inputs and the outputs. In this study, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are used to predict the static and seismic responses of slopes. In order to integrate the soft computing methods with GIS for coherent landslide hazard analysis, an automatic slope profile delineation method from Digital Elevation Models is developed. The integrated framework is evaluated using a case study of the 1989 Loma Prieta, CA earthquake (Mw = 6.9). A seismic landslide hazard analysis is also performed for the same region for a future scenario earthquake (Mw = 7.03) on the San Andreas Fault.

North American populations of white-winged scoters (<i>Melanitta fusca deglandi</i>) have declined markedly over the past several decades. The causes for decline are uncertain, but likely involve a complexity of events occurring on wintering and breeding areas. To gain insight into potential cross-seasonal effects, I delineated Atlantic and Pacific wintering scoter populations and linked them to a shared breeding area using stable isotope analysis of carbon (δ13C) and nitrogen (δ15N) ratios in feathers. By applying this methodology to a marked breeding population at Redberry Lake, Saskatchewan, I assigned females to putative wintering areas and determined: (1) population structure; (2) the extent of winter site philopatry; and, (3) differences in vital rates and other variates in relation to winter origin. Discriminant function analysis of isotopic ratios in feather samples from known wintering locations resulted in classification probabilities of 96% (<i>n</i> = 149) for Pacific and 78% (<i>n</i> = 32) for Atlantic wintering scoters. Using this methodology, I determined that the Redberry Lake breeding population is comprised of approximately 75% Pacific and 25% Atlantic wintering birds, and its members exhibit high degrees of winter region philopatry based on the classification of successive recaptures over three field seasons. Annual variations in population structure, as well as differences in nest initiation dates and blood contaminant loads in relation to winter area suggest seasonal interactions may influence survival and reproductive success of this population. To gain insight into potential seasonal interactions, I examined nest success and female survival in relation to winter area. No significant differences in nest success in relation to winter area were found, but nests that failed before mid-incubation were not sampled. Adult female survival rate for 2000-2004 was estimated as 0.85, with no significant difference detected between wintering areas. This study demonstrated that it is important to link breeding and wintering areas to better understand the factors influencing population dynamics and to effectively address conservation issues.

Energy demands can be particularly high in arctic-nesting birds that face harsh, unpredictable conditions during the breeding season. Consequences of these demands, particularly energy-partitioning during egg laying and incubation, are fundamentally important for arctic nesters. This study investigated differences in breeding strategies between Long-tailed Duck (<i>Clangula hyemalis</i>) and King Eider (<i>Somateria spectabilis</i>) in the central Canadian arctic. The focus was on ecological variables and influences of variation in nutrient resources used during incubation and egg production. Research was done at Karrak Lake, Nunavut, where both species nest sympatrically at relatively high densities, permitting comparative research about breeding strategies.<p>This study used stable-carbon (d13C) and nitrogen (d15N) isotope analysis to investigate origins and allocation of endogenous (stored) and exogenous (external) nutrients used in egg production. Remote temperature sensors were placed in nests to estimate and compare incubation rhythms and gain insight into capital and income incubating strategies of both species. Results suggest that breeding Long-tailed Ducks and King Eiders used a mixed breeding strategy, that is they relied on both exogenous and endogenous resources for reproduction. Close correspondence between d13C and d15N values of egg components and potential diet items indicated that King Eiders allocated exogenous nutrients for egg production (albumen 98.1%, yolk protein 96.8%, whole yolk 98.4%, and yolk lipids 84%). Female King Eiders relied on endogenous nutrients for incubation, as evidenced by high incubation constancy (96%). Conversely, the range of d13C values in components of Long-tailed Duck eggs and d13C values of diet items suggested that although some females allocated endogenous reserves for egg production, most females allocated exogenous resources for egg production (albumen 98.5%, yolk protein 78.3%, whole yolk 84.9%, and yolk lipids 38.3%). Long-tailed Duck females had an 84% incubation constancy, suggesting less reliance on endogenous nutrients for incubation than was estimated for female King Eiders. Knowledge about the relative importance of endogenous reserves and exogenous nutrients for egg production and incubation may help direct management decisions to specific winter/staging and or breeding areas used by King Eiders and Long-tailed Ducks.