"Fit Cradle of the Reviving Art": An inquiry into the the Evolving Material Reality of the Murals in the Camposanto of Pisa from the Perspective of their British Reception from the 17th to the 19th century

The Camposanto of Pisa is an extraordinarily complex and evocative monument, which has captured the imagination of pilgrims, both religious and secular, for centuries. The late Medieval and early Renaissance wall paintings that line the perimeter of the portico surrounding a vast inner courtyard, are unparalleled in early Italian art, not only for their striking variety of composition and narrative complexity, but also for the sheer grandeur of their proportion. However, the passage of time has scarred the structure of the Camposanto and inflicted terrible damage on its wall paintings. This thesis explores the material reality of the Camposanto as experienced over three centuries through the eyes of British travelers. In order to situate the Camposanto mural cycle within an historical and cultural context, the first chapter provides an overview of the construction and decoration of the monument. Notably, Giorgio Vasari (1511-1574), the Italian Humanist often recognized as the father of art history, included numerous descriptions of the Camposanto murals in his highly influential text Vite de' più eccellenti pittori, scultori, ed architettori. Accordingly, the second chapter provides an analysis of Vasari’s descriptions and reflects upon the influence that the Renaissance author may have had upon the subsequent British reception of the Camposanto murals. The third chapter utilizes three centuries of travel writing in order to investigate the aesthetic impact of the Camposanto mural cycle upon British tourists from the seventeenth through to the nineteenth century.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.