The chemical properties of calcium phosphate cements (CPCs) are very similar to the mineral phase of bone. CPCs are, consequently, very effective substrates (scaffolds) for tissue engineering; bone and stem cells attach readily, and can proliferate and differentiate to form new bone tissue. Unlike other CPCs that may remain largely unchanged in the body for years, such as hydroxyapatite, dicalcium phosphates are remodelled by the body and rapidly converted to new bone. Unfortunately, the dicalcium phosphates are also typically too weak to support load bearing in the human body. Our laboratory has recently developed a novel, high strength brushite CPC, (hsCPC), which can reach 10-50 fold higher failure strength than many commercially available CPCs. The aim of this thesis was to investigate the physical, chemical and biological performance of hsCPCs in physiologically relevant model of drug release, load bearing, osteoconductivity, and as a scaffold for bone tissue engineering. Multiple CPCs were compared in a model of screw augmentation to determine whether the physical properties of the cement, such as bulk strength and porosity, affected orthopedic screw holding strength. In an in vitro model of bone regeneration stem cells were grown on macroporous scaffolds that were fabricated from hsCPC. Drug releasing scaffolds were fabricated to examine whether the low porosity of hsCPC impeded drug release during a 4 week incubation period. The biological activity of an incorporated drug, Rebamipide, was examined after acute and chronic incubation periods. In the drug release study it was noted that the biological response to hsCPC was significantly better than tissue culture grade polystyrene, even in groups without drug. The mechanism underlying this biological response was further investigated by testing the effect of pyrophosphate, a common cement additive, on bone cell proliferation and differentiation. This thesis concludes that a high strength cement can produce significant improvement in screw augmentation strength, if there is sufficient cortical bone near the augmentation site. The hsCPC is also cytocompatible, and can support bone and stem cell proliferation and differentiation. hsCPC scaffolds stimulated osteogenic gene expression comparable to native bone scaffolds. hsCPC scaffolds are also capable of delivering drug for up to 4 weeks, in vitro. Finally, a cement additive, pyrophosphate, stimulated differentiation, but not proliferation of bone cells.

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. (authors' abstract)

No description available.

Quality control is an important part of any production line. It can be done manually but is most efficient if automated. Inspecting qualitycan include many different processes but this thesisisfocusedon the visual inspection for cracks and scratches. The best way of doingthis at the time of writing is with the help of Artificial Intelligence (AI), more specifically Deep Learning (DL).However, these need a training datasetbeforehand to train on and for some smaller companies, this mightnotbean option. This study triesto find an alternative visual inspection method,that does notrelyon atrained deep learning modelfor when trainingdata is severely limited. Our method is to use edge detection algorithmsin combination with a template to find any edge that doesn’t belong. These include scratches, cracks, or misaligned stickers. These anomalies arethen highlighted in the original picture to show where the defect is. Since deep learningis stateof the art ofvisual inspection, it is expected to outperform template matching when sufficiently trained.To find where this occurs,the accuracy of template matching iscompared to the accuracy of adeep learning modelat different training levels. The deep learning modelisto be trained onimage augmenteddatasets of size: 6, 12, 24, 48, 84, 126, 180, 210, 315, and 423. Both template matching and the deep learning modelwas tested on the samebalanceddataset of size 216. Half of the dataset was images of scratched units,and the other half was of unscratched units. This gave a baseline of 50% where anything under would be worse thanjust guessing. Template matching achieved an accuracy of 88%, and the deep learning modelaccuracyrose from 51% to 100%as the training setincreased. This makes template matching have better accuracy then AI trained on dataset of 84imagesor smaller. But a deep learning modeltrained on 126 images doesstart to outperform template matching. Template matching did perform well where no data was available and training adeep learning modelis no option. But unlike a deep learning model, template matching would not need retraining to find other kinds of surface defects. Template matching could also be used to find for example, misplaced stickers. Due to the use of a template, any edge that doesnot match isdetected.  The ways to train deep learning modelis highly customizable to the users need. Due to resourceand knowledge restrictions, a deep dive into this subject was not conducted.For template matching, only Canny edge detection was used whenmeasuringaccuracy. Other edge detection methodssuch as, Sobel, and Prewitt was ruledoutearlier in this study.

The early discovery of gastrointestinal (GI) disorders can significantly decrease the fatality rate of severe afflictions. Video capsule endoscopy (VCE) is a technique that produces an eight hour long recording of the GI tract that needs to be manually reviewed. This has led to the demand for AI-based solutions, but unfortunately, the lack of labeled data has been a major obstacle. In 2020 the Kvasir-Capsule dataset was produced which is the largest labeled dataset of GI abnormalities to date, but challenges still exist.The dataset suffers from unbalanced and very similar data created from labeled video frames. To avoid specialization to the specific data the creators of the set constructed an official split which is encouraged to use for testing. This study evaluates the use of transfer learning, Data augmentation and binary classification to detect GI abnormalities. The performance of machine learning (ML) classification is explored, with and without official split-based testing. For the performance evaluation, a specific focus will be on achieving a low rate of false negatives. The proposition behind this is that the most important aspect of an automated detection system for GI abnormalities is a low miss rate of possible lethal abnormalities. The results from the controlled experiments conducted in this study clearly show the importance of using official split-based testing. The difference in performance between a model trained and tested on the same set and a model that uses official split-based testing is significant. This enforces that without the use of official split-based testing the model will not produce reliable and generalizable results. When using official split-based testing the performance is improved compared to the initial baseline that is presented with the Kvasir-Capsule set. Some experiments in the study produced results with as low as a 1.56% rate of false negatives but with the cost of lowered performance for the normal class.

Joint models for the association of a longitudinal binary and a longitudinal continuous process are proposed for situations in which their association is of direct interest. The models are parameterized such that the dependence between the two processes is characterized by unconstrained regression coefficients. Bayesian variable selection techniques are used to parsimoniously model these coefficients. A Markov chain Monte Carlo (MCMC) sampling algorithm is developed for sampling from the posterior distribution, using data augmentation steps to handle missing data. Several technical issues are addressed to implement the MCMC algorithm efficiently. The models are motivated by, and are used for, the analysis of a smoking cessation clinical trial in which an important question of interest was the effect of the (exercise) treatment on the relationship between smoking cessation and weight gain.

In der Arbeit wird ein neuartiges Lernverfahren für Gauß-Mischverteilungen entwickelt. Es basiert auf der Technik der Markov-Chain Monte-Carlo Verfahren und ist in der Lage, in einem Zuge die Größe der Mischverteilung sowie deren Parameter zu bestimmen. Das Verfahren zeichnet sich sowohl durch eine gute Anpassung an die Trainingsdaten als auch durch eine gute Generalisierungsleistung aus. Ausgehend von einer Beschreibung der stochastischen Grundlagen und einer Analyse der Probleme, die beim Lernen von Gauß-Mischverteilungen auftreten, wird in der Abeit das neue Lernverfahren schrittweise entwickelt und seine Eigenschaften untersucht. Ein experimenteller Vergleich mit bekannten Lernverfahren für Gauß-Mischverteilungen weist die Eignung des neuen Verfahrens auch empirisch nach.

Differences in water dynamics between deciduous aspen (Populus tremuloides) and co-occurring evergreen conifer species in the Northern Rocky Mountains result from complex physical and biological interactions. A comprehensive evaluation of individual water transfer mechanisms was used to elucidate the relative importance of several components of the hydro logic cycles of aspen and conifer, and determine which water transfer mechanisms have potential to cause differences in net water yield. Adjacent aspen and conifer stands were monitored to determine snow accumulation and ablation (snow survey), soil moisture recharge (capacitance probes), snowpack sublimation (sublimation pan), transpiration period (thermal dissipation probes), and evapotranspiration (soil water content). Snow accumulation was 34 and 44% higher in aspen during springs of 2005 and 2006, respectively. Ablation rates in aspen (9.58 mm day-1 ) were nearly double that of conifer (4.9 mm day-1). When changes in soil moisture (due to over winter snowmelt) were combined with snow accumulation in 2006, aspen had greater potential (42-83%) for runoff and groundwater recharge. Snowpack sublimation during the ablation period was not different between open, aspen, and conifer sites and comprised -1). Summertime ET rate was higher in an aspen stand (3.6 mm day-1) than in an adjacent conifer stand (2.7 mm day-1) amounting to ~126 mm more water lost over the growing season, but largely reflected post-ablation differences in stored soil water. The net effects of these water transfer processes could result in more watershed water yield from aspen than conifer forests. However, the difference in water yield between these two forests will largely depend on the fate of snow lost from the conifer canopy. Snow intercepted by conifer branches can be removed by the processes of sublimation (reduces water yield) and redistribution (does not affect water yield). Future studies should focus on partitioning the ratio of sublimation to redistribution to predict hydro logic response of vegetation conversions for water yield augmentation in snow-dominated watersheds.

No description available.

Modifications, alterations, and enhancements to the body have been a practice forvarious ethnic, religious, and cultural groups across the globe for centuries. Over recent years, augmentations to the buttocks have become increasingly popular amongst women in Black and Brown communities. It is necessary to examine the health concerns, medical neglect, and societal influences that have contributed to the alarming increase of health complications and fatalities within these communities as a result of the procedures. Specifically, the administration of illegal injectable substances to the buttocks and the “Brazilian Butt Lift” procedure are in need of serious ethical focus and concern. In addition to the harmful health effects that result from these procedures, it is also important to recognize relevant societal and structural factors that intersect this rising issue. To fully comprehend the magnitude of the ongoing concerns, the history of buttock augmenting will be thoroughly discussed, other serious contributing factors, accessibility to adequate healthcare, and health knowledge. Cosmetic enhancements should come from a place of both empowerment and wellbeing, however the current conditions in the industry as it pertains to marginalized communities reflect otherwise. There are several contextual aspects as to why the ramifications of these procedures are so detrimental and it is my intention to highlight these issues, discuss the ethical implications, implore further research efforts, and then provide possible solutions to help address this growing issue. / Urban Bioethics