Quality Improvement in the Department of Family and Community Medicine at the University of Kentucky

The purpose of this capstone project is to analyze the quality improvement data that was collected in order to increase the pediatric population at the University of Kentucky Department of Family and Community Medicine clinic at Turfland. There were three different patient populations that were surveyed: reproductive aged women between 18 and 40, parents of the pediatric population that the clinic saw between July 2014 and June 2015, and pregnant women between July 2014 and June 2015. The results show that continuity of care and not being able to see the provider of choice were some of the weaknesses that were brought up

Comparative life cycle assessment (LCA) and life cycle cost analysis (LCCA) of precast and cast-in-place buildings in United States

2020 Spring.Includes bibliographical references.Precast construction is one of the growing construction methods for buildings across United States. Many tools have been used to assess environmental and economic impacts of the buildings. LCA and LCCA are one of the most widely used tools to evaluate the environmental and economic impacts of the buildings for their complete life cycle. The research aims to understand the life cycle environment impacts and costs over the complete life cycle for precast and cast-in-place building system. Cradle-to-grave approach was used to develop a framework for assessing the these impacts for precast and cast-in-place building systems constructed in United States through Open LCA software and NIST handbook for LCCA. The environmental impacts and costs associated with the four phases (raw material extraction and manufacturing, installation/construction, operation and demolition) of a precast building in United States were calculated and compared to cast-in-place building system. The research findings implicated that precast using sandwich panel building system had 21% lower life cycle costs (LCC) compared to cast-in-place building system. The construction phase and operation phase also had 38 % and 24% lower LCC compared to cast-in-place building systems. Additionally, lower life cycle environmental impacts towards nine environmental impact indicators were recorded for precast building systems. This study concluded that precast methodology has lower life cycle environmental and economic impacts than cast-in-place and is more sustainable construction method. The developed framework for LCA and LCCA could be applied to all concrete construction projects across the world and could be used as platform for conducting future LCA and LCCA studies as well. The research can also be used by practitioners to understand the phase-wise and total life cycle environmental and economic impacts of precast and further investigate to reduce these impacts

Understanding discrimination in academic collaboration networks

Diversity in backgrounds, ideas, and beliefs plays an important role in the scientific research community yet researchers belonging to minority groups are often discriminated against in academic collaborations. Here I replicate game-theoretic models originally described in a 2018 publication by Hannah Rubin and Cailin O'Connor in an attempt to reproduce their findings. Findings from these replicated models followed the same trends reported by Rubin and O'Connor including an increased likelihood of discrimination associated with smaller minority group sizes as well as a decrease in researchers working with out-group partners as a result of discriminatory norms in collaborative research networks. I then build on these base models and their findings to propose future model extensions that will provide insight into both the short-term and long-term impacts of discrimination faced by minority researchers within and beyond collaboration networks.Includes bibliographical references

Sequentially Programmed Magnetic Field Therapy in the Management of Recurrent Anaplastic Astrocytoma: A Case Report and Literature Review

Background: Anaplastic astrocytomas are progressive brain tumors with a tendency to infiltrate the surrounding tissue. Recurrence is very common, with recurrent tumors being extremely refractory to existing therapies. Case Presentation: A 33-year-old woman presented with a history of an unprovoked fall, followed by seizures. An MRI scan revealed a mass in the fronto-temporo-parietal region of the brain, suggesting a primary tumor. She underwent craniotomy and surgical debulking of the tumor. The histology of the tumor tissue revealed an anaplastic astrocytoma. Follow-up MRI scans indicated the presence of a residual, rapidly progressing tumor. A 6-week course of fractionated radiation and concurrent chemotherapy with Temodar® (temozolomide capsules) did not stop tumor progression. Intervention: Due to the failure of conventional therapies in preventing rapid disease progression, the patient volunteered to undergo a 28-day course of Sequentially Programmed Magnetic Field (SPMF) therapy. Results: Immediate post-therapy MRI scan showed a cessation of tumor growth, and follow-up imaging at 6, 12, 24 and 36 months revealed a gradual but steady decrease in the size of the tumor. The patient reported an alleviation of clinical symptoms and a subjective improvement in the quality of life at 6, 12, 24 and 36 months following SPMF therapy. Conclusion: The remarkable improvement of this patient suggests that SPMF therapy may be a valuable option for anaplastic astrocytoma, especially in recurrent and rapidly progressing tumors

Lifelong learning of concepts in CRAFT

La planification à des niveaux d’abstraction plus élevés est essentielle lorsqu’il s’agit de résoudre des tâches à long horizon avec des complexités hiérarchiques. Pour planifier avec succès à un niveau d’abstraction donné, un agent doit comprendre le fonctionnement de l’environnement à ce niveau particulier. Cette compréhension peut être implicite en termes de politiques, de fonctions de valeur et de modèles, ou elle peut être définie explicitement. Dans ce travail, nous introduisons les concepts comme un moyen de représenter et d’accumuler explicitement des informations sur l’environnement. Les concepts sont définis en termes de transition d’état et des conditions requises pour que cette transition ait lieu. La simplicité de cette définition offre flexibilité et contrôle sur le processus d’apprentissage. Étant donné que les concepts sont de nature hautement interprétable, il est facile d’encoder les connaissances antérieures et d’intervenir au cours du processus d’apprentissage si nécessaire. Cette définition facilite également le transfert de concepts entre différents domaines. Les concepts, à un niveau d’abstraction donné, sont intimement liés aux compétences, ou actions temporellement abstraites. Toutes les transitions d’état suffisamment importantes pour être représentées par un concept se produisent après l’exécution réussie d’une compétence. En exploitant cette relation, nous introduisons un cadre qui facilite l’apprentissage tout au long de la vie et le raffinement des concepts à différents niveaux d’abstraction. Le cadre comporte trois volets: Le sytème 1 segmente un flux d’expérience (par exemple une démonstration) en une séquence de compétences. Cette segmentation peut se faire à différents niveaux d’abstraction. Le sytème 2 analyse ces segments pour affiner et mettre à niveau son ensemble de concepts, lorsqu’applicable. Le sytème 3 utilise les concepts disponibles pour générer un graphe de dépendance de sous-tâches. Ce graphe peut être utilisé pour planifier à différents niveaux d’abstraction. Nous démontrons l’applicabilité de ce cadre dans l’environnement hiérarchique 2D CRAFT. Nous effectuons des expériences pour explorer comment les concepts peuvent être appris de différents flux d’expérience et comment la qualité de la base de concepts affecte l’optimalité du plan général. Dans les tâches avec des dépendances de sous-tâches complexes, où la plupart des algorithmes ne parviennent pas à se généraliser ou prennent un temps impraticable à converger, nous démontrons que les concepts peuvent être utilisés pour simplifier considérablement la planification. Ce cadre peut également être utilisé pour comprendre l’intention d’une démonstration donnée en termes de concepts. Cela permet à l’agent de répliquer facilement la démonstration dans différents environnements. Nous montrons que cette méthode d’imitation est beaucoup plus robuste aux changements de configuration de l’environnement que les méthodes traditionnelles. Dans notre formulation du problème, nous faisons deux hypothèses: 1) que nous avons accès à un ensemble de compétences suffisamment exhaustif, et 2) que notre agent a accès à des environnements de pratique, qui peuvent être utilisés pour affiner les concepts en cas de besoin. L’objectif de ce travail est d’explorer l’aspect pratique des concepts d’apprentissage comme moyen d’améliorer la compréhension de l’environnement. Dans l’ensemble, nous démontrons que les concepts d’apprentissagePlanning at higher levels of abstraction is critical when it comes to solving long horizon tasks with hierarchical complexities. To plan successfully at a given level of abstraction, an agent must have an understanding of how the environment functions at that particular level. This understanding may be implicit in terms of policies, value functions, and world models, or it can be defined explicitly. In this work, we introduce concepts as a means to explicitly represent and accumulate information about the environment. Concepts are defined in terms of a state transition and the conditions required for that transition to take place. The simplicity of this definition offers flexibility and control over the learning process. Since concepts are highly interpretable in nature, it is easy to encode prior knowledge and intervene during the learning process if necessary. This definition also makes it relatively straightforward to transfer concepts across different domains wherever applicable. Concepts, at a given level of abstraction, are intricately linked to skills, or temporally abstracted actions. All the state transitions significant enough to be represented by a concept occur only after the successful execution of a skill. Exploiting this relationship, we introduce a framework that aids in lifelong learning and refining of concepts across different levels of abstraction. The framework has three components: - System 1 segments a stream of experience (e.g. a demonstration) into a sequence of skills. This segmentation can be done at different levels of abstraction. - System 2 analyses these segments to refine and upgrade its set of concepts, whenever applicable. - System 3 utilises the available concepts to generate a sub-task dependency graph. This graph can be used for planning at different levels of abstraction We demonstrate the applicability of this framework in the 2D hierarchical environment CRAFT. We perform experiments to explore how concepts can be learned from different streams of experience, and how the quality of the concept base affects the optimality of the overall plan. In tasks with complex sub-task dependencies, where most algorithms fail to generalise or take an impractical amount of time to converge, we demonstrate that concepts can be used to significantly simplify planning. This framework can also be used to understand the intention of a given demonstration in terms of concepts. This makes it easy for the agent to replicate a demonstration in different environments. We show that this method of imitation is much more robust to changes in the environment configurations than traditional methods. In our problem formulation, we make two assumptions: 1) that we have access to a sufficiently exhaustive set of skills, and 2) that our agent has access to practice environments, which can be used to refine concepts when needed. The objective behind this work is to explore the practicality of learning concepts as a means to improve one’s understanding about the environment. Overall, we demonstrate that learning concepts can be a light-weight yet efficient way to increase the capability of a system

Ectopic pancreatic islets in Splenic hilum and peripancreatic fat

The presence of pancreatic islets alone in the peripancreatic region and splenic hilum is an uncommon occurrence. Herein, we describe their presence in this rare location

Diagnosis, Treatment, and Outcomes of Antibody-Mediated Rejection in Kidney Transplantation

Antibody mediated rejection remains an important barrier to optimal long-term outcomes after kidney transplantation. Donor specific antibody, while not the formidable barrier to transplantation it once was, remains a major risk factor for antibody mediated rejection and its consequences of premature graft failure. Recent advances in understanding of the cellular and molecular mechanisms of antibody production and antibody-mediated injury have led to refinements in diagnostic techniques, and have paved the way for the development of novel therapies to treat rejection and prolong allograft function. The purpose of this chapter is to review the current level at which we understand the pathophysiology of antibody mediated rejection, describe the current diagnostic criteria for antibody mediated rejection, and discuss available and emerging treatments as well as their outcomes

The “All of Us” Research Program

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GSGFormer: Generative Social Graph Transformer for Multimodal Pedestrian Trajectory Prediction

Pedestrian trajectory prediction, vital for selfdriving cars and socially-aware robots, is complicated due to intricate interactions between pedestrians, their environment, and other Vulnerable Road Users. This paper presents GSGFormer, an innovative generative model adept at predicting pedestrian trajectories by considering these complex interactions and offering a plethora of potential modal behaviors. We incorporate a heterogeneous graph neural network to capture interactions between pedestrians, semantic maps, and potential destinations. The Transformer module extracts temporal features, while our novel CVAE-Residual-GMM module promotes diverse behavioral modality generation. Through evaluations on multiple public datasets, GSGFormer not only outperforms leading methods with ample data but also remains competitive when data is limited