159 research outputs found

    Assessing structural differences among genetically improved coastal Douglas-fir using high density airborne laser scanning

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    Tree improvement programs are critical to establishing high yield seed sources while maintaining genetic diversity and developing sustainable plantation forests. Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is commonly used in improvement programs due to its superior strength and stiffness properties. Trials in British Columbia (BC), Canada aim to increase stem volume without sacrificing wood quality. Progeny test trials are essential in assessing the genetic performance via the prediction of breeding values (BVs) for target phenotypes of trees. To evaluate performance of improved stock, and to determine whether yield gains are being met, realized-gain trials are used. In both trials, accurate and timely collection of phenotypic data are critical for estimating and validating BVs with confidence. Currently, phenotypic variables collected focus on yield attributes; namely diameter at breast height, and height. Selection criteria in BC are evolving; branching traits are recognized as having a strong influence on strength and stiffness of Douglas-fir wood, however, they are rarely measured. High-density Airborne Laser Scanning (ALS), as well as Remotely Piloted Aerial Systems Laser Scanning Systems (RPAS-LS) produce three-dimensional point clouds which can be used to characterize individual tree structure. In this dissertation I utilized metrics derived from ALS and RPAS-LS to assess the performance in realized-gain trials, and predict genetic parameters in progeny trials. Additionally, new methods to estimate branch attributes of individual trees for inclusion as selection criteria in tree improvement programs were developed. This dissertation provides an insight into how ALS can be used to model branch attributes, while the ability to analyse trees by plot, individual tree, and individual branch attributes further allows researchers to maximise the value of ALS data. The findings are encouraging; they indicate that branch level metrics can be included as selection criteria in breeding programs, and that ALS-derived BVs are a suitable proxy for ground-based BVs. Given the cost efficiency of ALS, forest geneticists should explore this technology as tool to increase breeding programs’ overall efficiency. Findings from this research can be integrated into large-scale programs for monitoring trees, and identifying trees that display desirable attributes.Forestry, Faculty ofGraduat

    Dry and back again: characterization of desiccation-associated differentiation of leaf tissues in Craterostigma pumilum Hochst

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    Resurrection plants are a polyphyletic group of angiosperms which display true desiccation tolerance - the ability to survive near complete loss of cellular water for extended periods, while recovering metabolic competence upon watering. This is achieved by employing tailored protection behaviours depending on the relative state of (de)hydration. Recent work has raised interest in desiccation associated changes related to tissue destiny in desiccation tolerant vegetative tissues. In this thesis, physiological and transcriptomic techniques were used to characterize such a phenomenon in the homoiochlorophyllous dicot resurrection plant Craterostigma pumilum. Detailed phenotypic observation and pulse-amplitude-modulation fluorometry were used to identify the critical water contents at which key physiological changes occur in leaves of C. pumilum and how this relates to desiccation-associated differentiation between leaf Tip and Base tissues. This was followed by transcriptomic analyses and comparison between these two tissues, to identify potentially key processes involved in desiccation associated tissue differentiation. All findings were then synthesised with existing information reported on for other resurrection plant species to create a theoretical model of desiccation-associated tissue differentiation. This differentiation phenomenon is shown to be transcriptionally initiated during the desiccation commitment stage of the C. pumilum dehydration cycle but is only realised phenotypically during early rehydration and after initial water movement through the leaf tissues. This work provides strong evidence for the existence of desiccation-associated tissue differentiation in C. pumilum and highlights the potential involvement of the phytohormone auxin in the determination of leaf tissue responses to progressive dehydration and anhydrobiosis in resurrection plants

    Exploring the cost-effectiveness of high versus low perioperative fraction of inspired oxygen in the prevention of surgical site infections among abdominal surgery patients in three low- and middle-income countries

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    Background: This study assessed the potential cost-effectiveness of high (80–100%) vs low (21–35%) fraction of inspired oxygen (FiO2) at preventing surgical site infections (SSIs) after abdominal surgery in Nigeria, India, and South Africa. Methods: Decision-analytic models were constructed using best available evidence sourced from unbundled data of an ongoing pilot trial assessing the effectiveness of high FiO2, published literature, and a cost survey in Nigeria, India, and South Africa. Effectiveness was measured as percentage of SSIs at 30 days after surgery, a healthcare perspective was adopted, and costs were reported in US dollars ().Results:HighFiO2maybecosteffective(cheaperandeffective).InNigeria,theaveragecostforhighFiO2was). Results: High FiO2 may be cost-effective (cheaper and effective). In Nigeria, the average cost for high FiO2 was 216 compared with 222forlowFiO2leadingtoa 222 for low FiO2 leading to a −6 (95% confidence interval [CI]: −13to 13 to −1) difference in costs. In India, the average cost for high FiO2 was 184comparedwith184 compared with 195 for low FiO2 leading to a −11(9511 (95% CI: −15 to −6)differenceincosts.InSouthAfrica,theaveragecostforhighFiO2was6) difference in costs. In South Africa, the average cost for high FiO2 was 1164 compared with 1257forlowFiO2leadingtoa 1257 for low FiO2 leading to a −93 (95% CI: −132to 132 to −65) difference in costs. The high FiO2 arm had few SSIs, 7.33% compared with 8.38% for low FiO2, leading to a −1.05 (95% CI: −1.14 to −0.90) percentage point reduction in SSIs. Conclusion: High FiO2 could be cost-effective at preventing SSIs in the three countries but further data from large clinical trials are required to confirm this

    Modelling internal tree attributes for breeding applications in Douglas-fir progeny trials using RPAS-ALS

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    Coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is one of the most commercially important softwood species in North America. In British Columbia, Canada, breeding has increased volume gains between 20 and 30%, while 97% of seedlings come from improved seed sources. Branching traits in particular, have a strong influence on strength and stiffness of Douglas-fir wood; however, they are rarely measured. Remotely Piloted Aerial Systems and Airborne Laser Scanning Systems (RPAS-LS) produce high-density three-dimensional point clouds that can be used for the creation of internal geometric features describing individual tree branching structures. We analyzed a Coastal Douglas-fir progeny test trial located in British Columbia, Canada, and developed a new method to estimate branch attributes from RPAS-LS data for inclusion as selection criteria in tree improvement programs. Branch length, angle, width, and volume were estimated for each tree. Narrow-sense heritability (the proportion of variation due to genetics) and genetic correlations were also estimated. The method extracted branch length with a correlation (r) of 0.93 compared to manual measurements. Using these branch attributes, results then show that branch angle had the highest heritability (0.277), while tree height and branch length had the highest genetic correlation (0.668). These findings are encouraging for forest managers as they indicate that branch level metrics should be considered when selecting trees in breeding programs

    Exploring the cost-effectiveness of high versus low perioperative fraction of inspired oxygen in the prevention of surgical site infections among abdominal surgery patients in three low- and middle-income countries

    No full text

    Evaluation of Physics-Informed Neural Network Solution Accuracy and Efficiency for Modeling Aortic Transvalvular Blood Flow

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    Physics-Informed Neural Networks (PINNs) are a new class of machine learning algorithms that are capable of accurately solving complex partial differential equations (PDEs) without training data. By introducing a new methodology for fluid simulation, PINNs provide the opportunity to address challenges that were previously intractable, such as PDE problems that are ill-posed. PINNs can also solve parameterized problems in a parallel manner, which results in favorable scaling of the associated computational cost. The full potential of the application of PINNs to solving fluid dynamics problems is still unknown, as the method is still in early development: many issues remain to be addressed, such as the numerical stiffness of the training dynamics, the shortage of methods for simulating turbulent flows and the uncertainty surrounding what model hyperparameters perform best. In this paper, we investigated the accuracy and efficiency of PINNs for modeling aortic transvalvular blood flow in the laminar and turbulent regimes, using various techniques from the literature to improve the simulation accuracy of PINNs. Almost no work has been published, to date, on solving turbulent flows using PINNs without training data, as this regime has proved difficult. This paper aims to address this gap in the literature, by providing an illustrative example of such an application. The simulation results are discussed, and compared to results from the Finite Volume Method (FVM). It is shown that PINNs can closely match the FVM solution for laminar flow, with normalized maximum velocity and normalized maximum pressure errors as low as 5.74% and 9.29%, respectively. The simulation of turbulent flow is shown to be a greater challenge, with normalized maximum velocity and normalized maximum pressure errors only as low as 41.8% and 113%, respectively