Relationships Among Racism, Dental Care-Related Fear/Anxiety and Dental Care Utilization Among Black and African American Women in Appalachia

This dissertation is a study of the implications of racism in oral health care settings for dental care-related fear/anxiety, and dental care utilization. One in five adults in the US have experienced discrimination while receiving health care. Even though racism is the most reported type of discrimination in health care, little is known about its impact on dental outcomes. There is a paucity of prior studies measuring experiences of racism in dental settings. The current study proposed the application of Krieger’s Ecosocial Theory of Health Equity to explore relationships among racism in oral health care settings, dental care-related fear/anxiety, and dental care utilization among African American/Black women residing in Appalachia. Data from the Center for Oral Health Research in Appalachia (COHRA) Smile cohort was used in this study. The results of this study indicate racism occurs in oral health settings and is positively related to dental anxiety. Age, education, and income also predicted dental care-related anxiety/fear. Dental care-related fear/anxiety was also found to predict less frequent dental care utilization; experiences of racism in oral health care settings did not moderate the relationship between dental care-related anxiety/fear and dental care utilization. Exploratory analyses revealed geographic location differences in experiences of racism in dental settings and identified dental fear/anxiety as a mediator between experiences of racism in oral health care settings and dental care utilization. Findings suggest Black women desire to be listened to, respected and provided equal services like other people in dental care settings. It also suggests that racism in oral health care settings may trigger dental care-related fear/anxiety among Black women. Clinically assessing and addressing past experiences with racism in dental settings may help reduce dental anxiety/fear among Black women

Change of Sight, Sites of Creativity: The Visual Arts in Albania after Socialism

This dissertation examines Albania’s fine art world after the end of state socialism in 1991. Drawing on two years of anthropological fieldwork (January –August 2006 and January 2010-August 2011) in Tirana, Albania’s capital city, this study investigates how the withdrawal of state support and oversight on the arts, the introduction of a market economy and efforts toward European belonging have been reflected, responded to and challenged in the discourses and practices of aesthetic production. Viewing art as a productive site of social meaning, where people perform and struggle over their identities, their pasts and futures, this dissertation explores the social imaginaries that art is employed to construct as Albania navigates European Union integration and tries to tame its socialist past. Central to this study is analyzing the increasing prevalence of discourses on art’s social relevance, which have crystallized only in recent years. Whereas in 2006, most artists in this study were primarily concerned with producing art for art’s sake and its commodity potential, by 2010 and 2011, informants frequently declared that an important aim of their work was to produce art that could have some relevance for society. Such claims about art’s social relevance are being made in a context where local and transnational cultural flows and processes are complexly negotiated in light of both new and old knowledges. These negotiations are indicative of the cultural politics of the postsocialist transition, where art producers: react to socialist-era perceptions on art and the role of the artist; engage with and respond to the influence of international institutions and foundations; and incorporate the universal vernacular of contemporary art which they infuse with local histories, experiences and subjectivities. Looking at artists are key agents of globalization, this dissertation also examines how Albanian artists negotiate the forces of the local and the global in their work in an effort to illuminate processes of cultural and economic globalization in postsocialism. Lastly, this study focuses on Albanian artists’ recent engagements with the [socialist] past. In their work, the symbols, forms, histories and memories of socialism had been gaining momentum, doing significant cultural work in current battles over remembering, documenting and erasing the past

Modification of butterfat by selective hydrolysis and interesterification by lipase: Process and product characterization

Butterfat was chemically modified via combined hydrolysis and interesterification, catalyzed by a commercial lipase immobilized onto a bundle of hydrophobic hollow fibers. The main goal of this research effort was to engineer butterfat with improved nutritional properties by taking advantage of the sn-1,3 specificity and fatty acid specificity of a lipase in hydrolysis and ester interchange reactions, and concomitantly decrease its level of long-chain saturated fatty acid residues (viz., lauric, myristic, and palmitic acids) and change its melting properties. All reactions were carried out at 40°C in a solvent free system under controlled water activity, and their extent was monitored via chromatographic assays for free fatty acids, esterified fatty acid moieties, and triacylglycerols; the thermal behavior of the modified butterfat was also assessed via calorimetry. Lipase-modified butterfat possesses a wider melting temperature range than regular butterfat. The total saturated triacylglycerols decreased by 2.2%, whereas triacylglycerols with 28–46 acyl carbons (which contained two or three lauric, myristic, or palmitic acid moieties) decreased by 13%. The total monoene triacylglycerols increased by 5.4%, whereas polyene triacylglycerols decreased by 2.9%. The triacylglycerols of interesterified butterfat had ca. 10.9% less lauric, 10.7% less myristic, and 13.6% less palmitic acid residues than those of the original butterfat

High Fidelity Modeling of Cold-Formed Steel Single Lap Shear Screw Fastened Connections

Cold-formed steel connections are commonly fastened using self-tapping self-drilling screws. The behavior of these connections can differ based on the screw manufacturer or the cold-formed steel product used, both of which have a large selection available for use in industry. Because of their popularity and the many possible variations of these connections, researchers have frequently tested screw connections to characterize their behavior. However, repeatedly conducting this type of experiment is time consuming and expensive. Therefore, the purpose of this work was to create finite element models that can successfully predict the behavior of single lap shear screw connections, a common connection type used in cold-formed steel framing. These models were created using the finite element program Abaqus/CAE. To validate these models, test results from Pham and Moen (2015) were used to compare the stiffness, strength, and failure mode of multiple connections. A parametric study is also conducted to determine the influence of contact parameters on the behavior of the model. The results showed that all models consistently had good agreement with the connection stiffness and that most of the models also had good agreement with the peak load and failure mode of the v tests. These results were also compared to the design equations available for screw connections from the American Iron and Steel Institute (AISI). This comparison revealed that the models are more successful at predicting screw connection behavior than AISI, and thus work is required to improve the accuracy of AISI’s design equations. The eventual goal of this work is to develop a procedure to build and validate models without requiring test data. This work continuing in the future can lead to recommendations to improve AISI’s design equations and to implement the behavior of the connections into large cold-formed steel framing models such as diaphragms or shear walls

Effect of cholesterol biosynthesis inhibitor on some biochemical parameters in normal male rats

Endogenous cholesterol acts as a precursor of testosterone and other steroids hormones, this study was conducted to evaluate if there is a counterproductive effect associated with inhibition of cholesterol biosynthesis pathway specially in high doses and the degree of these effects in normal male rats. Forty eight adult Wistar rats divided into four groups, the first is control while the remaining three groups were treated with simvastatin (cholesterol biosynthesis inhibitor) in doses of 25, 50 and 100 mg.kg-1 respectively. Serum samples were observed at the baseline then every fifteen days while tissue samples were observed at day 30 and 60. Results of statistic refered to a significant decrease (p≤ 0.05) in serum total cholesterol and triglycerides (by 24 and 49% ± 3) respectively, also serum testosterone was significantly decreased (by 71% ± 2) in all groups compared to control after thirty and sixty days. The activity of alanine aminotransferase was increased (57% ± 3) versus to aspartate aminotransferase. Liver cholesterol was significantly decreased (by 72% ± 2) while testicular cholesterol was decreased except the group of 100 mg.kg-1 which in turns to elevate (61% ± 4), in addition also there was a decrease in body weight gain percentage neither the weights of liver nor testis was affected. In conclusion, the inhibition of denovo pathway of cholesterol biosynthesis negatively affects testosterone level in addition to cholesterol concentration in the tissues, body weight gain and alanine aminotransferase with no successful compensatory mechanism as related with testosterone level

Vermeidung von Repräsentationsheterogenitäten in realweltlichen Wissensgraphen

Knowledge graphs are repositories providing factual knowledge about entities. They are a great source of knowledge to support modern AI applications for Web search, question answering, digital assistants, and online shopping. The advantages of machine learning techniques and the Web's growth have led to colossal knowledge graphs with billions of facts about hundreds of millions of entities collected from a large variety of sources. While integrating independent knowledge sources promises rich information, it inherently leads to heterogeneities in representation due to a large variety of different conceptualizations. Thus, real-world knowledge graphs are threatened in their overall utility. Due to their sheer size, they are hardly manually curatable anymore. Automatic and semi-automatic methods are needed to cope with these vast knowledge repositories. We first address the general topic of representation heterogeneity by surveying the problem throughout various data-intensive fields: databases, ontologies, and knowledge graphs. Different techniques for automatically resolving heterogeneity issues are presented and discussed, while several open problems are identified. Next, we focus on entity heterogeneity. We show that automatic matching techniques may run into quality problems when working in a multi-knowledge graph scenario due to incorrect transitive identity links. We present four techniques that can be used to improve the quality of arbitrary entity matching tools significantly. Concerning relation heterogeneity, we show that synonymous relations in knowledge graphs pose several difficulties in querying. Therefore, we resolve these heterogeneities with knowledge graph embeddings and by Horn rule mining. All methods detect synonymous relations in knowledge graphs with high quality. Furthermore, we present a novel technique for avoiding heterogeneity issues at query time using implicit knowledge storage. We show that large neural language models are a valuable source of knowledge that is queried similarly to knowledge graphs already solving several heterogeneity issues internally.Wissensgraphen sind eine wichtige Datenquelle von Entitätswissen. Sie unterstützen viele moderne KI-Anwendungen. Dazu gehören unter anderem Websuche, die automatische Beantwortung von Fragen, digitale Assistenten und Online-Shopping. Neue Errungenschaften im maschinellen Lernen und das außerordentliche Wachstum des Internets haben zu riesigen Wissensgraphen geführt. Diese umfassen häufig Milliarden von Fakten über Hunderte von Millionen von Entitäten; häufig aus vielen verschiedenen Quellen. Während die Integration unabhängiger Wissensquellen zu einer großen Informationsvielfalt führen kann, führt sie inhärent zu Heterogenitäten in der Wissensrepräsentation. Diese Heterogenität in den Daten gefährdet den praktischen Nutzen der Wissensgraphen. Durch ihre Größe lassen sich die Wissensgraphen allerdings nicht mehr manuell bereinigen. Dafür werden heutzutage häufig automatische und halbautomatische Methoden benötigt. In dieser Arbeit befassen wir uns mit dem Thema Repräsentationsheterogenität. Wir klassifizieren Heterogenität entlang verschiedener Dimensionen und erläutern Heterogenitätsprobleme in Datenbanken, Ontologien und Wissensgraphen. Weiterhin geben wir einen knappen Überblick über verschiedene Techniken zur automatischen Lösung von Heterogenitätsproblemen. Im nächsten Kapitel beschäftigen wir uns mit Entitätsheterogenität. Wir zeigen Probleme auf, die in einem Multi-Wissensgraphen-Szenario aufgrund von fehlerhaften transitiven Links entstehen. Um diese Probleme zu lösen stellen wir vier Techniken vor, mit denen sich die Qualität beliebiger Entity-Alignment-Tools deutlich verbessern lässt. Wir zeigen, dass Relationsheterogenität in Wissensgraphen zu Problemen bei der Anfragenbeantwortung führen kann. Daher entwickeln wir verschiedene Methoden um synonyme Relationen zu finden. Eine der Methoden arbeitet mit hochdimensionalen Wissensgrapheinbettungen, die andere mit einem Rule Mining Ansatz. Beide Methoden können synonyme Relationen in Wissensgraphen mit hoher Qualität erkennen. Darüber hinaus stellen wir eine neuartige Technik zur Vermeidung von Heterogenitätsproblemen vor, bei der wir eine implizite Wissensrepräsentation verwenden. Wir zeigen, dass große neuronale Sprachmodelle eine wertvolle Wissensquelle sind, die ähnlich wie Wissensgraphen angefragt werden können. Im Sprachmodell selbst werden bereits viele der Heterogenitätsprobleme aufgelöst, so dass eine Anfrage heterogener Wissensgraphen möglich wird