VictorJuiz/thesis huggingface dataset:
The Idealism of Life: Hegel and Kant on the Ontology of Living Individuals
Keywords: Hegel
Kant
Life
Metaphysics

My dissertation, The Idealism of Life: Hegel and Kant on the Ontology of Living Individuals, investigates the significance of the concept of life for Kant’s and Hegel’s respective forms of idealism. In Chapter 1, I argue that Kant’s account of the subjective origin of the a priori forms of cognition requires that when we judge something to be a living individual, we only suppose it to be so (or, in other words, that these judgments do not determine anything in the object being judged). In the remaining chapters, I argue that Hegel’s account of life as objectively real (i.e. rather than a supposition we make) depends on his development of the concept of the individual as self-determining self. I trace this development in the Science of Logic through three stages. In Chapter 2, I argue that any minimal notion of self depends on Hegel’s logic of the Infinite as described in the Doctrine of Being. In Chapter 3, I argue that this minimal account of selfhood is possible only if that self is immanently, rather than externally, determined—that is, that a self cannot be defined from without—by tracing Hegel’s account of ‘Determining Reflection’. In Chapter 4, I show how, for Hegel, the logic of self-determination gives us the resources to describe the concept of individuality, which Hegel develops as the ‘Concept’. In Chapter 5, I conclude that Hegel’s account of life depends on the claim that the ideal relations immanent to it (relations between, e.g., self and other, or organism and organ) both constitute and are constituted by the material determinations of the living thing. This, in turn, suggests that any idealism that attributes ideal forms and material determinations to distinct sources will be unable to describe life as objectively real.
Incorporating Uncertainty into Projections of Landscape Change
Keywords: landscape dynamics
land use
model
spatial
state and transition
woodland caribou

A wide range of spatially-explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. A key challenge facing these models is how to incorporate uncertainty, including the future effects of climate change, into their projections. I present here a general framework, called a state-and-transition simulation model (STSM), for incorporating uncertainty into projections of landscape change. The STSM method divides a landscape into a set of spatial units, and then simulates the discrete state of each spatial unit forward in time as a stochastic process, in response to discrete transitions, using a Monte Carlo approach. The method also allows for any number of continuous stocks to be defined as stochastic processes for each spatial unit, along with continuous flows to move material between these stocks over time. I demonstrate the STSM method using two different applications. The first is a model of land use/land cover (LULC) change for the State of Hawaii. This model explores interactions between possible future changes in LULC, combined with projected shifts in moisture zones due to climate change, in order to generate projections for the future spatial and temporal distribution of LULC across the State. Through the integration of a carbon stock-flow model with the STSM, I further demonstrate how spatially-explicit projections for terrestrial carbon can incorporate uncertainties in projections of LULC change. The second application presents a new approach for incorporating uncertainty into forest management planning, which I demonstrate using two landscapes in the boreal forest of Ontario (Canada). Here I explore the implications of incorporating uncertainties regarding the spatial and temporal variability of wildfire, including the potential effects of climate change, into spatially-explicit projections of timber supply and habitat for woodland caribou. STSMs can be applied to a wide range of landscapes, including questions of both land use change and vegetation dynamics. Because the method is both spatially-explicit and stochastic, it is well suited for characterizing uncertainty in landscape dynamics. STSMs offer a simple yet powerful means for incorporating uncertainty into projections of landscape change.
Aggregate load modeling for Demand Response via the Minkowski sum
Keywords: Demand Response
Electric Power
Flexible Loads
Load Aggregation
Minkowski Sum
Optimal Power Flow

Aggregations of flexible loads can provide several power system services through demand response programs, for example load shifting and curtailment. The capabilities of demand response should therefore be represented in system operators' planning and operational routines. However, incorporating models of every load in an aggregation into these routines could compromise their tractability by adding exorbitant numbers of new variables and constraints. We propose a novel approximation for concisely representing the capabilities of a heterogeneous aggregation of flexible loads. We assume that each load is mathematically described by a convex polytope, i.e., a set of linear constraints. We discuss the polytopic formulation of many classes of loads including deferrable loads, thermostatically controlled loads, and generic energy storage. The set-wise sum of the loads is the Minkowski sum, which is in general computationally intractable. In this thesis, we develop a new outer approximation of the Minkowski sum. The new approximation is applicable for linear constraints, is easily computable, and only uses one variable per time period corresponding to the aggregation's net power usage. We prove that the approximation is exact when applied to deferrable loads without power constraints and loads modelled for two time periods only. Additionally, numerical results indicate that the approximation is accurate for broad classes of loads. We also develop a tightening procedure to further improve upon the accuracy of the approximation. Following, we extend the above approximation to semidefinite constraints and second-order cone constraints. The approximation is extended to loads that do not have matching constraints via the use of the Gershgorin circle theorem. This extension allows for the modelling of loads with apparent power constraints or for stochastic loads modelled with chance constraints. Numerical results are shown for the case where load charging efficiency and total energy demand are taken as stochastic quantities. Finally, we consider the problem of finding inner approximation to load models. We use an ellipsoidal projection technique to find the maximum inscribed ellipsoid for the Minkowski sum of several loads. We perform numerical simulations to illustrate the technique.
Machine Learning for Prediction of Anticonvulsive Drug Treatment Outcomes in Mecp2-Deficient Mice
Keywords: Cross-Frequency Coupling
Epilepsy
High Frequency Oscillations
Machine Learning
Mecp2
Treatment Prediction

Anticonvulsive drug (ACD) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials before a successful treatment can be found. In this thesis we apply a novel approach, using machine learning techniques to predict epilepsy treatment outcomes of commonly used ACDs. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Our work on Mecp2-deficient mice focuses on low frequency oscillations (LFO), high frequency oscillations (HFO) and their interactions through cross-frequency coupling (CFC) to reveal iEEG based biomarkers that track epileptic seizure pathology. Our findings revealed: variability across discharge events using iEEG recordings, progression of longer duration discharges over five developmental time points, and the increased cross-frequency coupling index ICFC of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in discharge events. These results suggest a link between long duration discharges with elevated ICFC to the epileptic seizure pathology. Using the ICFC to label post-treatment outcomes we trained Support Vector Machines (SVM) and Random Forest (RF) machine learning classifiers on time-based, normalized power and CFC comodulogram features to predict the efficacy of ACD treatments. The results indicate that the performance of the comodulogram features yielded better predictions and were further improved when combined with time-based features. Hence, machine learning techniques were able to rank ACDs by estimating likelihood scores for successful treatment outcome. Identifying the most appropriate ACD treatment a priori would reduce the burdens of drug trials and provide patient specific treatment options that could lead to substantial improvements in patient quality of life.
Spatio-Temporal Connectivity in Dynamic Tropical Fragmented Landscapes
Keywords: biodiversity conservation
connectivity metrics
Ecological networks
graph theory
landscape network dynamics
spatial dynamics

Expanding human occupation on the planet reduces and fragments native habitats, threatening biodiversity, ecosystem functioning, and services. Tropical regions have recently been experiencing unprecedented amounts of forest conversion and fragmentation. As tropical forests harbor a large fraction of the worldâ s biodiversity, their loss and fragmentation has spearheaded the sixth mass extinction. Nevertheless, the tropical regions experience unique low intensity land-use and bioclimatic characteristics that result in highly dynamic forested landscapes. These dynamical landscapes, when subjected to the current scenario of intense global change, poses particular challenges for biodiversity conservation in human-modified landscapes. This thesis provides insights towards (i) the development of new metrics to quantify landscape dynamics; (ii) the assessment of the effects of land-use intensification on spatio-temporal dynamics and connectivity; and (iii) the quantification of potential drivers of these changes in the spatial dynamics. In the first part of my thesis, I developed a graph-theoretical method that incorporates the spatial dynamics of the landscape in the evaluation of landscape connectivity. I tested this method using a large set of Atlantic Forest landscapes of Brazil. In the second part of the thesis, I evaluated the effects of different drivers of landscape spatial dynamics, particularly focusing on land-use intensification alongside its economic and social drivers. My results pointed to the fact that land-use intensification reduces spatio-temporal dynamics of landscapes, as a large fraction of the land is locked up into one or a few intensified land cover types and the proportion of land abandoned to native habitat regeneration is low. Taken together, my findings have two broad impacts: (i) the new spatio-temporal indices reveal insights about landscape connectivity missed by purely spatial connectivity indices; and (ii) land-use intensification is happening across the globe, independent of the agricultural commodity that is being produced, reducing spatial dynamicity, which will lead to a decline in connectivity. Therefore, more land should be spared for biodiversity conservation in more highly intensified landscapes. Both findings have direct implications for spatial planning for conservation.
Economic Burden of Chronic Ulcers
Keywords: Chronic Ulcers
Cost-of-illness

Chronic ulcers (pressure ulcer, diabetic foot ulcer and leg ulcer) are an underappreciated yet growing concern to the health care system. These conditions are commonly observed in various health care settings and are associated with an increase in health care utilization and cost. The current understanding of the economic burden of chronic ulcers is limited to short term costs. The aim of this thesis is to expand the knowledge of the cost-of-illness of chronic ulcers by presenting three related studies. The first study systematically reviewed cost-of-illness studies on chronic ulcers. Included studies were heterogeneous, limiting our ability to aggregate and compare results. There was a lack of extensive, long-term studies evaluating attributable cost of chronic ulcers. Thus, the usefulness of cost-of-illness studies for chronic ulcers to policy-makers is limited. In the second study, we evaluated the lifetime cost-of-illness of chronic ulcers using Ontario administrative data. The economic impact of a concurrent pressure ulcer for individuals with a spinal cord injury was explored in the third study. This thesis highlights the substantial economic burden of chronic ulcers. The results presented are only a portion of the total financial burden associated with treating individuals with this condition. Additional efforts by health care policy decision makers, clinicians and patients in targeting preventative interventions will reduce the downstream cost of chronic ulcers freeing up limited health care resources to treat other health concerns.
Voices from the margins: Interest groups and the state in Canadian and British immigration policy-making between 2000 and 2010
Keywords: Immigration
Institutionalism
Interest groups
Migration
Refugees
Temporary foreign workers

Over the decade of the 2000s, governments in Canada and Britain converged in their immigration policies, increasing the ability of short-term and temporary foreign workers to enter their labour markets and restricting refugee entries. This work seeks to find an explanation for the source of these changes and tests an interest-based approach in particular. Did interest groups successfully lobby policy-makers or did politicians act autonomously in each area to implement legislation? By comparing labour and refugee policies within and across countries, it contributes to a growing stream in the political study of immigration which explicitly considers each field in relation to the other in an attempt to specify the sources of policy change. Six cases, two in labour migration and one in refugee legislation in each country, are considered. The research reveals that political actors in Westminster systems are able to set, direct and accomplish their agendas with little input from organized interests in most cases. They are further able to design policy instruments that insulate their decision-making from interest groups. The study, therefore, confirms theoretical expectations. Governments were able to respond to perceived electoral risks â including media pressure and global economic shocks â and reverse policies that interest groups had advanced. The work opens new and emergent questions about the conditions under which organized interests may participate in the making of immigration policy. It further suggests that the ability of strong states to act autonomously can create a democratic deficit that may lead to state power being unshackled from accountability, and undesirable and unexpected policy outcomes. As a result, robust mechanisms are required to allow interest groups a voice in policy processes.
Drama, a Springboard for Writing at the Kindergarten Level
Keywords: Curriculum
Teaching
Learning
Drama
Emergent Literacy
Mantle of the Expert
Scaffolding
Story Drama

This action research venture investigated the influence of drama-based lessons (Story Drama, Mantle of the Expert, and the Zone of Proximal Development) on kindergarten-aged studentsâ motivation to write. This research used qualitative methods to examine, inform and carry out each lesson. I conducted a research study for 12 weeks aiming to answer these research questions: (a) How does participating in dramatic lessons support student engagement in writing? (b) How do students view themselves as writers and how do they feel about writing after participating in dramatic lessons? (c) How does scaffolding lesson content encourage student connections in dramatic retellings of stories and in subsequent writing activities? Moreover, I gathered qualitative data through observations, videos, interviews, and student writing samples. Results show that drama can offer students many of the skills necessary for learning both inside and outside of the curriculum. Furthermore, these approaches to learning have helped the kindergarten-aged children interpret, comprehend, and convey meaning through drama and writing activities. For many of the participating students, drama supported the development of abstract thought and personal connections, as well as deduction and inferential skills. During the drama activities, students were highly engaged, often demonstrating a deep understanding of stories through their actions and improvisations. The enthusiasm that followed as a result of the drama activities often transferred to studentsâ drawings and written work.
Investigation of the Role of AID/APOBEC Cytidine Deaminases in Breast Cancer
Keywords: APOBEC
Breast Cancer
Cancer
Mutagenesis

Abstract The development of cancer from normal cells requires the acquisition of mutations in genomic DNA. Defects in DNA repair contribute to breast cancer predisposition and carcinogenesis in some individuals, including carriers of germline BRCA1 mutations. However, the additional factors responsible for mutagenesis in breast cancer have not been well defined. We hypothesized that AID/APOBEC cytidine deaminases â endogenous enzymes involved in innate and acquired immunity, which deaminate cytosine to uracil â could contribute to this process. Using genetically engineered mouse models, we showed that AID activity is present in mouse mammary epithelial cells and is modulated by hormonal factors, but that the absence of AID or APOBEC3 does not substantially limit the development nor affect the histology of spontaneous tumors in either a BRCA1-deficient or BRCA1-proficient context. In human cancer cell lines, functional genomic studies suggested that APOBEC3F, but not other APOBEC family members, was essential for proliferation and survival. APOBEC3F knockdown using siRNA and shRNA induced apoptosis and cell cycle arrest in a p21-dependent manner, but this was not an on-target effect. Bioinformatic analyses of large human cancer datasets revealed a strong association between APOBEC3B gene expression and proliferation in most solid tumor types. However, leveraging the existence of a germline APOBEC3B deletion polymorphism, we demonstrated that APOBEC3B does not cause this effect, and that its absence is not associated with clinical outcome or other standard clinicopathologic features in breast cancer. Instead, a pattern of immune activation was discovered in tumors arising in APOBEC3B deletion carriers, which may be relevant for the emerging field of cancer immunotherapy. In light of recent observations that a pattern of mutagenesis consistent with APOBEC activity is present in a wide range of cancers, the experimental results and tumour analyses reported here advance the understanding of the role of AID/APOBEC in breast and other cancers and will help to inform ongoing efforts to deduce the functional and therapeutic relevance of this phenomenon.
Quantifying Vascular Markers in Cancer Using Targeted Microbubbles
Keywords: Angiogenesis
High Frequency
Microbubbles
Molecular Imaging
Mouse Embryos
Ultrasound

The growing demand for a personalized approach to treatment of disease has accentuated the importance of molecular imaging. As the characterization and measurement of key biomolecules in vivo, molecular imaging has the potential to dramatically impact all facets of patient care, from early disease detection to diagnosis, staging, treatment monitoring and follow-up. In ultrasound, functional and molecular imaging is possible through the use of microbubbles, a contrast agent that can be transformed into targeting agents that bind to vascular biomarkers of interest. This strategy has been employed by a number of groups to demonstrate the ability of targeted ultrasound contrast imaging to monitor molecular expression in a variety of tumour models and it is suggested that a positive correlation between specific biomarker expression levels and the detected acoustic signal of the bubbles exists. The limitations of traditional tumour models, however, have hindered any concrete demonstrations of quantitative measures of vascular biomarkers using microbubbles. This thesis demonstrates that targeted ultrasound contrast enhanced imaging can provide a quantitative measure of angiogenic biomarker expression on endothelial cells, both in vitro and in a mouse embryo model. New methodologies were first developed to introduce microbubbles into the vasculature of living, isolated, late gestational stage embryos. Non-linear microultrasound imaging was employed to collect basic perfusion parameters in these animals and molecular imaging of endogenously expressed endothelial cell surface markers in the developing mouse was successfully achieved. An endoglin loss of function model was subsequently used to examine the relationship of receptor densities to quantifiable molecular ultrasound levels. This design enabled direct modulation of surface target protein densities. Parallel plate perfusion chambers were used to assess the adhesion of targeted microbubble binding to cultured embryonic mouse endothelial cells, while binding in vivo was evaluated in living transgenic embryos. In both cases, molecular ultrasound was capable of reliably differentiating between molecular genotypes. This work represents the first demonstration of quantitative molecular imaging with ultrasound, a critical first step in establishing the abilities of molecular ultrasound to deliver accurate and specific noninvasive information in patients.