Predicting developmental growth stages in soft red winter wheat (Triticum aestivum L.) (SRWW) could improve agronomic management in Kentucky. However, predicting SRWW development is complex due to vernalization requirement and photoperiod sensitivity differences of cultivars. The objectives of this study are to (1) determine ability of Kompetitive Allele Specific PCR (KASP) genotyping to predict phenotype; (2) determine the relative vernalization requirement (RVR) of 50 SRWW cultivars in a greenhouse (GH) assay; and (3) measure growing degree-days (GDD) required by cultivars to reach eight growth stages in a field assay. Fifty SRWW cultivars were characterized with 14 KASP markers for Vrn and Ppd loci. Additionally, cultivars were grown in a GH, vernalized outdoors for three, six, or nine weeks, and moved back into the GH where days to full flower were measured. Cultivars were also seeded into hill plots monthly from October to March at Princeton (2016; 2017) and Lexington, KY (2017) in three field trials. Cumulative GDD to emergence, green-up, pseudo-stem erection, jointing, flag leaf, beginning flower, full flower, and harvest maturity were measured. Field trials and supporting historical wheat development data suggest that prediction of SRWW growth and development is possible using a cumulative GDD scale in Kentucky.

Deux tiers des cancers du sein expriment des récepteurs hormonaux ostrogéniques (tumeur ER-positive) et la croissance de ces tumeurs est stimulée par l’estrogène. Des traitements adjuvant avec des anti-estrogènes, tel que le Tamoxifen et les Inhibiteurs de l’Aromatase peuvent améliorer la survie des patientes atteinte de cancer du sein. Toutefois la thérapie hormonale n’est pas efficace dans toutes les tumeurs mammaires ER-positives. Les tumeurs peuvent présenter avec une résistance intrinsèque ou acquise au Tamoxifen. Présentement, c’est impossible de prédire quelle patiente va bénéficier ou non du Tamoxifen. Des études préliminaires du laboratoire de Dr. Mader, ont identifié le niveau d’expression de 20 gènes, qui peuvent prédire la réponse thérapeutique au Tamoxifen (survie sans récidive). Ces marqueurs, identifié en utilisant une analyse bioinformatique de bases de données publiques de profils d’expression des gènes, sont capables de discriminer quelles patientes vont mieux répondre au Tamoxifen. Le but principal de cette étude est de développer un outil de PCR qui peut évaluer le niveau d’expression de ces 20 gènes prédictif et de tester cette signature de 20 gènes dans une étude rétrospective, en utilisant des tumeurs de cancer du sein en bloc de paraffine, de patients avec une histoire médicale connue. Cet outil aurait donc un impact direct dans la pratique clinique. Des traitements futiles pourraient être éviter et l’indentification de tumeurs ER+ avec peu de chance de répondre à un traitement anti-estrogène amélioré. En conséquence, de la recherche plus appropriée pour les tumeurs résistantes au Tamoxifen, pourront se faire. / Two thirds of breast cancers express the estrogen receptor (ER-positive tumours) and estrogens stimulate growth of these tumours. Adjuvant therapy with anti-estrogens such as Tamoxifen and Aromatase Inhibitors has been shown to increase survival in breast cancer patients. This treatment is, however, not successful in all ER-positive tumours. Tumours can present intrinsic or acquired resistance to Tamoxifen. However, it is currently impossible to predict which patient will benefit from Tamoxifen therapy and which will not. Preliminary studies in Dr. Mader’s lab have identified 20 genes whose expression levels in tumours are able to predict the response to Tamoxifen therapy (disease-free survival). These markers, identified using bioinformatics analysis of published gene expression datasets, were able to discriminate patients that would respond best to Tamoxifen from those that did not. The overall purpose of this study is to develop a PCR kit to monitor expression levels of these 20 genes and to test this 20-gene signature in a retrospective study using paraffin-embedded breast cancer tissues of patients with a known medical history. This tool may thus have a direct impact on clinical practice through the development of markers of therapeutic success for treatment with Tamoxifen and possibly Aromatase Inhibitors. Futile treatments would be avoided thus preventing needless side effects, and improved identification of ER+ tumours with a low chance of success to anti-estrogen therapy. This will facilitate research into more appropriate treatments for hormone resistant tumours.

Deux tiers des cancers du sein expriment des récepteurs hormonaux ostrogéniques (tumeur ER-positive) et la croissance de ces tumeurs est stimulée par l’estrogène. Des traitements adjuvant avec des anti-estrogènes, tel que le Tamoxifen et les Inhibiteurs de l’Aromatase peuvent améliorer la survie des patientes atteinte de cancer du sein. Toutefois la thérapie hormonale n’est pas efficace dans toutes les tumeurs mammaires ER-positives. Les tumeurs peuvent présenter avec une résistance intrinsèque ou acquise au Tamoxifen. Présentement, c’est impossible de prédire quelle patiente va bénéficier ou non du Tamoxifen. Des études préliminaires du laboratoire de Dr. Mader, ont identifié le niveau d’expression de 20 gènes, qui peuvent prédire la réponse thérapeutique au Tamoxifen (survie sans récidive). Ces marqueurs, identifié en utilisant une analyse bioinformatique de bases de données publiques de profils d’expression des gènes, sont capables de discriminer quelles patientes vont mieux répondre au Tamoxifen. Le but principal de cette étude est de développer un outil de PCR qui peut évaluer le niveau d’expression de ces 20 gènes prédictif et de tester cette signature de 20 gènes dans une étude rétrospective, en utilisant des tumeurs de cancer du sein en bloc de paraffine, de patients avec une histoire médicale connue. Cet outil aurait donc un impact direct dans la pratique clinique. Des traitements futiles pourraient être éviter et l’indentification de tumeurs ER+ avec peu de chance de répondre à un traitement anti-estrogène amélioré. En conséquence, de la recherche plus appropriée pour les tumeurs résistantes au Tamoxifen, pourront se faire. / Two thirds of breast cancers express the estrogen receptor (ER-positive tumours) and estrogens stimulate growth of these tumours. Adjuvant therapy with anti-estrogens such as Tamoxifen and Aromatase Inhibitors has been shown to increase survival in breast cancer patients. This treatment is, however, not successful in all ER-positive tumours. Tumours can present intrinsic or acquired resistance to Tamoxifen. However, it is currently impossible to predict which patient will benefit from Tamoxifen therapy and which will not. Preliminary studies in Dr. Mader’s lab have identified 20 genes whose expression levels in tumours are able to predict the response to Tamoxifen therapy (disease-free survival). These markers, identified using bioinformatics analysis of published gene expression datasets, were able to discriminate patients that would respond best to Tamoxifen from those that did not. The overall purpose of this study is to develop a PCR kit to monitor expression levels of these 20 genes and to test this 20-gene signature in a retrospective study using paraffin-embedded breast cancer tissues of patients with a known medical history. This tool may thus have a direct impact on clinical practice through the development of markers of therapeutic success for treatment with Tamoxifen and possibly Aromatase Inhibitors. Futile treatments would be avoided thus preventing needless side effects, and improved identification of ER+ tumours with a low chance of success to anti-estrogen therapy. This will facilitate research into more appropriate treatments for hormone resistant tumours.

This thesis investigates the current state of tool wear prediction models in machining, focusing on their limitations in accurately incorporating the complex dynamics of cutting fluids and their industrial applicability. It proposes a comprehensive evaluation framework to classify and evaluate a wide range of models, including empirical, physical, computational, and data-driven models. The study identifies the key limitations and strengths of each model category. It proposes enhancements by integrating advanced fluid dynamics and predictive modeling techniques to improve tool wear predictions' accuracy and industrial applicability. A structured literature review was conducted to investigate and evaluate existing tool wear models and their integration with cutting fluid dynamics. This review included defining search criteria, selecting relevant studies, and assessing their quality and relevance. The study uses thematic analysis and model evaluation frameworks to classify and evaluate the models, leading to the identification of critical limitations and strengths. The literature review and model evaluation findings revealed that empirical models, while simple and quick to implement, showed moderate accuracy and limited fluid dynamics integration. Physical models provided high accuracy in specific conditions but were computationally intensive. Computational models, particularly those using techniques like Finite Element Analysis (FEA) and Computational Fluid Dynamics(CFD), offered detailed insights and high accuracy but required significant computational resources. Data-driven models demonstrated exceptional predictive capabilities and comprehensive fluid dynamics integration but relied heavily on data availability and quality.  The proposed enhancements include introducing non-linear elements into empirical models, incorporating simplified fluid models or empirical correlations into physical models, exploring reduced-order models (ROMs) or surrogate models for computational models, and developing robust data preprocessing and augmentation techniques for data-driven models. These enhancements aim to improve the accuracy and applicability of tool wear models in industrial machining processes, ultimately contributing to more efficient and cost-effective machining operations. The study emphasizes the importance of a systematic and holistic approach to model evaluation and enhancement. Future research should focus on validating these proposed enhancements through empirical studies and real-world applications, ensuring their relevance and robustness in diverse industrial settings. This research offers significant potential to advance tool wear modeling, providing valuable insights for both academia and industry.