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Collation Model for Ms. Codex 766: In physicam seu philosophiam na[tura]lem commentarius una cum quaestionibus.
Get PDFCommentary on Aristotle\u27s Physics.https://repository.upenn.edu/sims_models/1129/thumbnail.jp
Penn Library\u27s LJS 443 - [Collection of texts on the calendar]. (Video Orientation)
No full texthttps://repository.upenn.edu/sims_video/1191/thumbnail.jp
Collation Model for Ms. Codex 17: De spiritu et anima ... [etc.].
Get PDFText of treatise De spiritu et anima (f. 1r-56v), erroneously attributed to St. Augustine. This appears to be identical to the treatise De anima et spiritu by an Alcherus of Clairvaux. Also includes an unidentified theological text, erroneously identified in the manuscript as St. Augustine\u27s Soliloquia (f. 56v). There is a table of contents for this work on f. 56v-57v.https://repository.upenn.edu/sims_models/1141/thumbnail.jp
Collation Model for Ms. Codex 1162: Annotata in libellos Aristotelis Parva naturalia appellatos.
Get PDFLecture notes on Aristotle\u27s Parva naturalia, in many cases indicating the date of the lecture. Each section begins with a summary of its argument, followed by running commentary on the Greek text. Greek headings indicate the text currently under consideration. In between De divinatione ex insomniis and De longa et brevi vita, there also appears a commentary on De motu animalium dated to the same time, which employs the same structure as the other sections. However, the heading follows a different pattern, and the whole work is contained within its own three gatherings. Table of contents the title page (f. 1r). A name appears on the title page, but it is scratched out. It appears to be Hubertus Jacobus D... a... The inclusion of Melanchthon among the humanist authorities cited probably means the lectures took place in Lutheran territory. Cover is a single sheet of reused parchment from a liturgical text.https://repository.upenn.edu/sims_models/1151/thumbnail.jp
Collation Model for LJS 379: [Edict concerning Jews in Livorno and Pisa].
Get PDFCopy of an edict inviting foreign merchants, especially Jews, to settle in Livorno and Pisa, and defining their rights and privileges. The original edict was issued from the ducal palace in Florence, dated 10 June 1593, and consists of 44 clauses. The rights and privileges included amnesty for offenses committed previously, freedom from debts incurred elsewhere, free safe conduct in Livorno, the right to conduct business throughout Tuscany, the same rights and privileges in conducting foreign trade as others in Tuscany, protection from extraordinary levies beyond the usual taxes, exemption from the regulations on Jews living in Florence and Siena, the right of Jews to build and maintain a synagogue and cemetery, the right to observe Jewish holidays, ownership of printed books or manuscripts reviewed by the Inquisition, ownership of real property, freedom to practice for Jewish doctors, permission to employ Christian servants, the right of heads of households to bear arms, and freedom from wearing an insignia signifying Jewish identity.https://repository.upenn.edu/sims_models/1119/thumbnail.jp
Aging and Hypertension among the Global Poor—Panel Data Evidence from Malawi
Get PDFBackground: Hypertension has a rapidly growing disease burden among older persons in low-income countries (LICs) that is often inadequately diagnosed and treated. Yet, most LIC research on hypertension is based on cross-sectional data that does not allow inferences about the onset or persistence of hypertension, its correlates, and changes in hypertension as individuals become older.
Data and methods: The Mature Adults Cohort of the Malawi Longitudinal Study of Families and Health (MLSFH-MAC) is used to provide among the first panel analyses of hypertension for older individuals in a sub-Saharan LIC using blood pressure measurements obtained in 2013 and 2017.
Findings: High blood pressure is very common among mature adults aged 45+ in rural Malawi, and hypertension is more prevalent among older as compared to middle-aged respondents. Yet, in panel analyses for 2013-17, we find no increase in the prevalence of hypertension as individuals become older. Hypertension often persists over time, and the onset of hypertension is predicted by factors such as being overweight/obese, or being in poor physical health. Otherwise, however, hypertension has few socioeconomic predictors. There is also no gender differences in the level, onset or persistence in hypertension. While hypertension is associated with several negative health or socioeconomic consequences in longitudinal analyses, cascade-of-care analyses document significant gaps in the diagnosis and treatment of hypertension.
Conclusions: Our findings indicate that hypertension and related high cardiovascular risks are widespread, persistent, and often not diagnosed or treated in this rural sub-Saharan population of older individuals. Prevalence, onset and persistence of hypertension are common across all subgroups-including, importantly, both women and men. While age is an important predictor of hypertension risk, even in middle ages 45-55 years, hypertension is already widespread. Hypertension among adults aged 45+ in Malawi is thus more similar to a generalized epidemic than in high-income countries where cardiovascular risk has strong socioeconomic gradients and untreated hypertension particularly prevalent in vulnerable subsets of older persons
Interview with GAB-on! Relationships Drive Learning
Get PDFhttps://repository.upenn.edu/cpre_multimedia/1000/thumbnail.jp
Multiscale Models of Interfacial Mechanics in Low Dimensional Systems
No full textCrucial thrusts in modern technology from electronic information processing to engineering cellular systems require manipulation and control of materials on smaller and smaller scales to succeed. A simple and successful way to break conventional material property limitations or design multifunctional devices is to interface two different materials together. At small length scales, the surface to bulk ratio of each component material increases, to the point that the interfacial physics can dominate the properties of the engineered system. Simultaneously, the combinatorial space of possible interfaces between materials and/or molecules is far too vast to explore by trial-and-error experimentation alone. Intuitive theoretical models can greatly improve our ability to navigate such large search spaces by providing insight on how two materials are likely to interact. The goal of this thesis is to develop predictive physical models which explain emergent phenomena at material interfaces across multiple length and time scales. A variety of state-of-the-art tools were applied to realize this goal, including analytical mathematics, quantum mechanical simulations, finite element methods, and deep neural networks. At the electron scale, a continuum model parametrized by first-principles simulations was employed to develop design criteria for confined quantum states in lateral heterostructures of two-dimensional materials. At the atomic scale, a chemo-mechanical model incorporating long-range electrostatics was developed to explain synthesizability trends in composite heterostructures of inorganic perovskites and organic molecules. A machine learning graph neural network model was developed and applied to predict the impact of general surface strains on the adsorption energy of small molecule intermediates on catalyst surfaces. Finally, at the microscale, a nonlinear kinetic model was developed to explain how cells acquire and retain memory of the mechanical properties of their surroundings across multiple timescales, which can lead to irreversible adaptation and differentiation. The methods and results presented in this thesis can improve our understanding of physical phenomena arising at interfaces and provide a blueprint for future applications of multiscale computational modeling to science and engineering problems
Computational Imaging Biomarkers for Precision Medicine: Characterizing Heterogeneity in Breast Cancer
No full textIn the United States, 1 in 8 women are diagnosed with breast cancer. Breast tumor heterogeneity is well-established, with intratumor heterogeneity manifesting spatially and temporally. Increased heterogeneity is associated with adverse clinical outcomes. Current critical disease treatment decisions are made on the basis of biomarkers acquired from tissue samples, largely under sampling the heterogeneous disease burden. In order to drive precision medicine treatment strategies for cancer, personalized biomarkers are needed to truly characterize intratumor heterogeneity. Medical imaging can provide anon-invasive, whole tumor sampling of disease burden at the time of diagnosis and allows for longitudinal monitoring of disease progression. The studies outlined in this thesis introduce analytical tools developed through computer vision, bioinformatics, and machine learning and use diagnostic and longitudinal clinical images of breast cancer to develop computational imaging biomarkers characterizing intratumor heterogeneity. Intrinsic imaging phenotypes of spatial heterogeneity, identified in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images at the time of diagnosis, were identified and validated, demonstrating improved prognostic value over conventional histopathologic biomarkers when predicting 10-year recurrence free survival. Intrinsic phenotypes of longitudinal change in spatial heterogeneity in response to neoadjuvant treatment, identified in DCE-MRI were identified and leveraged as prognostic and predictive biomarkers, demonstrating augmented prognostic value when added to conventional histopathologic and personalized molecular biomarkers. To better characterize 4-D spatial and temporal heterogeneity, illuminated through dynamic positron emission tomography imaging, a novel 4-D segmentation algorithm was developed to identify spatially constrained, functionally discrete intratumor sub-regions. Quantifying the identified sub-regions through a novel imaging signature demonstrated the prognostic value of characterizing intratumor heterogeneity when predicting recurrence free survival, demonstrating prognostic improvement over established histopathologic biomarkers and conventional kinetic model derived parameters. Collectively, the studies in this thesis demonstrate the value of leveraging computationalimaging biomarkers to characterize intratumor heterogeneity. Such biomarkers have the potential to be utilized towards precision medicine for cancer care
Predicting Everyday Human Judgment from Natural Language
No full textConstructing a good knowledge representation is an essential step to build a computational model to predict and understand human behaviors. Thus, it has long been a focal interest of many research fields. Recent advances in computer science have made it feasible to derive richer and more robust mental representations of natural objects in a human-like manner compared to those developed by traditional psychometric approaches. In 21 studies involving numerical, category, and social judgments, we test the applicability and the adequacy of knowledge representations trained by the Word2Vec algorithm (Mikolov et al., 2013) on Google News articles to predict human everyday judgments of naturalistic objects. In Chapter 1, we predict human numerical estimation, identify the sources of errors in human estimation, and uncover the psychological underpinnings of human judgment. In Chapter 2, we use the Word2Vec knowledge representations as inputs to the Generalized Context Model (Nosofsky, 1984) to predict human learning performance under different learning environments. In Chapter 3, we combine theories of social inference and recommendation algorithms to predict human predictions about other people. Together, these studies showcase that the Word2Vec knowledge representations well approximate human knowledge and are suitable to serve as inputs to various models to predict human judgments of naturalistic objects and concepts. By integrating the techniques from computational linguistics with models from cognitive science, the three chapters scale up the application of psychological theories to real-world domains and inform behavioral interventions for better judgments and decisions