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Background: Every phenotypic trait can be viewed as a “system” in which a group of interconnected componentsfunction synergistically to yield a unified whole. Once a system’s components and their interactions have beendelineated according to biological principles, we can manipulate and engineer functionally relevant components toproduce a desirable system phenotype.Results: We describe a conceptual framework for mapping quantitative trait loci (QTLs) that control complex traitsby treating trait formation as a dynamic system. This framework, called systems mapping, incorporates a system ofdifferential equations that quantifies how alterations of different components lead to the global change of traitdevelopment and function through genes, and provides a quantitative and testable platform for assessing theinterplay between gene action and development. We applied systems mapping to analyze biomass growth data ina mapping population of soybeans and identified specific loci that are responsible for the dynamics of biomasspartitioning to leaves, stem, and roots.Conclusions: We show that systems mapping implemented by design principles of biological systems is quiteversatile for deciphering the genetic machineries for size-shape, structural-functional, sink-source and pleiotropicrelationships underlying plant physiology and development. Systems mapping should enable geneticists to shedlight on the genetic complexity of any biological system in plants and other organisms and predict itsphysiological and pathological states.

Cover systems have been included as a closure and long-term planning strategy for the estimated 1,539 Mt of waste rock at the Antamina Mine (Peru). A cover study was initiated to determine the most suitable type of cover system for the waste rock dumps at Antamina. The purpose of the four cover systems proposed in this study was to reduce net percolation to underlying waste rock via the combination of a low-permeability and a store-and-release cover, thereby limiting weathering and metal leaching from the waste rock. The low-permeability cover works as a barrier to percolation, whereas the store-and-release layer acts as a medium growth for vegetation, accumulates water during rain events and later releases most of it back to the environment through evapotranspiration. Four field-scale cover systems were constructed of native, low permeability materials and topsoil at Antamina. Climate data, runoff and infiltration through the covers were continuously monitored for one year in the field. A numerical model was created with the purpose of predicting the covers systems’ long term performance and the assessment of possible modification(s) to the design. Results after the first year indicate that the proposed cover systems reduced net percolation to the underlying waste rock from 70% (for the control lysimeter with no cover installed) to 53%-63%. No runoff was generated from any of the lysimeters and evapotranspiration is the only mechanism available to reduce net percolation through the cover systems. Materials characteristics and construction methodology were recognized as the potential reasons for the observed performance of the cover systems. Recommendations were given to improve the performance of the cover systems by further reducing the permeability of low-permeability layers and/or decreasing the thickness of the store-and-release layers of the cover systems. The feasibility of these recommendations depends on Antamina’s site specific conditions and remains to be evaluated. Finally, it was recognized that the data available to date is insufficient to draw major conclusions for the performance of each independent cover system. The Antamina cover study will continue over the following years and conclusions presented herein will be verified when new field data becomes available.

Satellite imagery such as Landsat has been in use for decades for many landscape and regional scale mapping applications, but has been too coarse for use in detailed forest inventories where stand level structural and compositional information is desired. Recently available high spatial resolution satellite imagery may be well suited to mapping fine-scale components of ecosystems, however, this remains an area of ongoing research. The first goal of this thesis was to assess the capacity of high spatial resolution satellite imagery to detect the variability in late seral coastal temperate rainforests in British Columbia, Canada. Using an object-based classifier, two hierarchical classification schemes are evaluated: a broad classification based on structural (successional) stage and a finer classification of late seral vegetation associations. The finer-scale classification also incorporates ancillary landscape positional variables (elevation and potential soil moisture) derived from Light Detection and Ranging (LiDAR) data, and the relative contribution of spectral, textural and landscape positional data for this classification is determined. Results indicate that late seral forests can be well distinguished from younger forests using QuickBird spectral and textural data. However, discrimination among late seral forest associations is challenging, especially in the absence of landscape positional variables. Classification accuracies were particularly low for rare forest associations. Given this finding, the objective of the third chapter was to explicitly examine the caveats of using high spatial resolution imagery to map rare classes. Classification accuracy is assessed in several different ways in order to examine the impact on perceived map accuracy. In addition, the effects on habitat extent and configuration resulting from post-classification implementation of a minimum mapping unit are examined. Results indicate that classification accuracies may vary considerably depending on the assessment technique used. Specifically, ignoring the presence of fine-scale heterogeneity in a classification during accuracy assessment falsely lowered the accuracy estimates. Further, post-classification smoothing had a large effect on the spatial pattern of rare classes. These findings suggest that routinely used image classification and assessment techniques can greatly impact mapping of rare classes.

In this thesis, we present a new procedure for mesh adaptation for wakes. The approach starts by tracking the wake centerline with an initial isotropic unstructured mesh. A vertex-centered finite volume method is used, and the velocity field is obtained from solution reconstruction. The velocity data is integrated numerically using an adaptive fourth-order Runge-Kutta method. We insert the wake centerline into the existing unstructured mesh as an internal boundary and use a metric-based anisotropic mesh adaptation to generate anisotropic cells in regions with large second derivatives of flow variables. In the second step, the problem is solved on adapted mesh and a new wake centerline is tracked. We then move the previous wake centerline (which is now a part of adapted mesh) to match the centerline obtained from the adapted mesh. To move the wake centerline, a solid mechanics analogy is used and the linear elasticity equation is solved on the adapted mesh. As a result, the displacement is propagated throughout the mesh and the already adapted regions along the wake centerline are preserved. The process is then followed for subsequent cycles of anisotropic mesh adaptation to obtain a more accurate approximation of the wake centerline. As an alternate strategy for obtaining an anisotropic mesh in the wake, we take the first geometry, together with the captured wake centerline from an unstructured triangular mesh, as an initial geometry to produce a quad dominant mesh, using an advancing layer method. The correctness of the streamline tracking algorithm is verified using an analytical velocity field. The mesh morphing approach is tested using the method of manufactured solutions, demonstrating that the linear finite element solution is second-order accurate. The results of laminar flow test cases for the attached and separated flow are presented and compared with some well-established numerical results in the literature. Our results show that the advancing layer mesh is more efficient in resolving the wake. In the end, one case for turbulent subsonic flow is considered. For turbulent flow, a cell-centered finite volume method is used and we only track the wake centerline at different angles of attack.

The beam-helicity asymmetry was measured, for the first time, in photoproduction of $\pi^{0}\eta$ pairs on carbon, aluminum, and lead, with the A2 experimental setup at MAMI. The results are compared to an earlier measurement on a free proton and to the corresponding theoretical calculations. The Mainz model is used to predict the beam-helicity asymmetry for the nuclear targets. The present results indicate that the photoproduction mechanism for $\pi^{0}\eta$ pairs on nuclei is similar to photoproduction on a free nucleon. This process is dominated by the $D_{33}$ partial wave with the $\eta\Delta(1232)$ intermediate state.

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% (DDB2, PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% (DDB2, CD8A, TALDO1, PCNA, EIF4G2, LCN2, CDKN1A, PRKCH, ENO1, and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

We report a measurement of cross section σðνμ þ nucleus → μ− þ XÞ and the first measurements of the cross section σðν¯μ þ nucleus → μþ þ XÞ and their ratio Rð σðν¯Þ σðνÞ Þ at (anti) neutrino energies below 1.5 GeV. We determine the single momentum bin cross section measurements, averaged over the T2K ν¯=ν-flux, for the detector target material (mainly carbon, oxygen, hydrogen and copper) with phase space restricted laboratory frame kinematics of θμ 500 MeV=c. The results are σðν¯Þ ¼ ð0.900 0.029ðstatÞ 0.088ðsystÞÞ × 10−39 and σðνÞ¼ð2.41 0.022ðstatÞ 0.231ðsystÞÞ × 10−39 in units of cm2=nucleon and Rð σðν¯Þ σðνÞ Þ ¼ 0.373 0.012ðstatÞ 0.015ðsystÞ.

We developed the McGill Wetland Model (MWM) based on the general structure of the Peatland Carbon Simulator (PCARS) and the Canadian Terrestrial Ecosystem Model. Three major changes were made to PCARS: 1. the light use efficiency model of photosynthesis was replaced with a biogeochemical description of photosynthesis; 2. the description of autotrophic respiration was changed to be consistent with the formulation of photosynthesis; and 3. the cohort, multilayer soil respiration model was changed to a simple one box peat decomposition model divided into an oxic and anoxic zones by an effective water table, and a one-year residence time litter pool. MWM was then evaluated by comparing its output to the estimates of net ecosystem production (NEP), gross primary production (GPP) and ecosystem respiration (ER) from 8 years of continuous measurements at the Mer Bleue peatland, a raised ombrotrophic bog located in southern Ontario, Canada (index of agreement [dimensionless]: NEP=0.80, GPP=0.97, ER=0.97; systematic RMSE [g C m?2 d?1]: NEP=0.12, GPP=0.07, ER=0.14; unsystematic RMSE [g C m?2 d?1]: NEP=0.15, GPP=0.27, ER=0.23). Simulated moss NPP approximates what would be expected for a bog peatland, but shrub NPP appears to be underestimated. Sensitivity analysis revealed that the model output did not change greatly due to variations in water table because of offsetting responses in production and respiration, but that even modest temperature increases could lead to converting the bog from a sink to a source of CO2. General weaknesses and further developments of MWM are discussed.

The NA62 experiment reports an investigation of the K+→π+νν¯ mode from a sample of K+ decays collected in 2017 at the CERN SPS. The experiment has achieved a single event sensitivity of (0.389 ± 0.024) × 10−10, corresponding to 2.2 events assuming the Standard Model branching ratio of (8.4 ± 1.0) × 10−11. Two signal candidates are observed with an expected background of 1.5 events. Combined with the result of a similar analysis conducted by NA62 on a smaller data set recorded in 2016, the collaboration now reports an upper limit of 1.78 × 10−10 for the K+→π+νν¯ branching ratio at 90% CL. This, together with the corresponding 68% CL measurement of (0.48−0.48+0.72) × 10−10, are currently the most precise results worldwide, and are able to constrain some New Physics models that predict large enhancements still allowed by previous measurements.