Hildegard Fantasy

Hildegard of Bingen (1098-1179), a German abbess, composer, mystic, and theologian, was revered as a prophet during her lifetime. Since then, her numerous accomplishments and visionary writings have made her popular both in her native Germany and across the world. Hildegard produced numerous Latin writings, more than any other woman of the Middle Ages, and her more than seventy musical compositions fascinate musicians and listeners to this day. My doctoral thesis is a composition for SATB chorus, orchestra, and soprano solo entitled Hildegard Fantasy, based on the life and music of Hildegard of Bingen. I have written both the music and the text, which is drawn from several sources among Hildegard’s writings and those of scholars investigating her life and work. For the Hildegard Fantasy, I use several of Hildegard’s pieces as part of the three-movement score, which lasts about twenty-five minutes. The composition has a neo-Romantic flavor, employing twentieth-century dissonances and Medieval modes. Hildegard Fantasy is my attempt to create an inspiring musical tribute to encourage performers and audiences to learn more about one of the most fascinating women of all time

The Impact of Unions on Workers Amid the COVID-19 Pandemic

Since the onset of the COVID-19 pandemic in March of 2020, workers in almost every industry have been impacted--often negatively--by impacts of the pandemic, such as decreased working hours and limited accessibility to earning wages. In his 2022 Visiting Scholars Seminar at Purdue University, economic scholar Dr. William Sprigg\u27s highlighted the detriment that the COVID-19 pandemic has had on minority workers, indicating the need for reformation to improve the quality of life for workers across industries. The implementation of unions has the ability to amplify the voices of disabled workers and minority workers to improve working conditions and increase economic productivity

Popcorn N\u27 Picture Books: Promoting Children\u27s Books in Academic Libraries

The educational value of children’s literature is supported by a numerous body of research. Helping children to read, write, develop fluency, critical thinking skills and multicultural awareness are just a few of the essential benefits children’s books provide. During the twentieth and twenty-first centuries, children’s book publishing has risen from a small publishing venture to big business. About 2,000 books were published for children in 1960. By the nineties, this number increased to 5,000 and has continued to rise. The “voluminous body of high-quality literature” published yearly makes selection by librarians difficult. As Bernice Cullinan and Lee Galda note, “Our job as teachers, librarians, and parents is to select the best from the vast array of books.” Another vital aspect of our roles as librarians is creative promotion of new children’s books. While the literature reveals a broad array of ideas and programs for celebrating children’s books in public and school libraries, little has been geared towards academic librarians

Checking Out Facebook.com: The Impact of a Digital Trend on Academic Libraries

While the burgeoning trend in online social networks has gained much attention from the media, few studies in library science have yet to address the topic in depth. This article reports on a survey of 126 academic librarians concerning their perspectives toward Facebook.com, an online network for students. Findings suggest that librarians are overwhelmingly aware of the ‘Facebook phenomenon.’ Those who are most enthusiastic about the potential of online social networking suggested ideas for using Facebook to promote library services and events. Few individuals reported problems or distractions as a result of patrons accessing Facebook in the library. When problems have arisen, strict regulation of access to the site seems unfavorable. While some librarians were excited about the possibilities of Facebook, the majority surveyed appeared to consider Facebook outside the purview of professional librarianship

Food insecurity and maternal depression in rural, low-income families: A longitudinal investigation

Objective: The purpose of the present study was to examine the relationship between household food insecurity and maternal depression in a rural sample to determine whether food insecurity predicted mothers’ depression over time or vice versa. Design: The study employed a prospective design using three waves of data from ‘Rural Families Speak’, a multi-state study of low-income rural families in the USA. Food insecurity was measured using the Core Food Security Module and depression was measured using the Center for Epidemiologic Studies–Depression Scale. A structural equation model was fit to the data using the AMOS software package. Setting: Sixteen states in the USA (California, Indiana, Kentucky, Louisiana, Massachusetts, Maryland, Michigan, Minnesota, Nebraska, New Hampshire, New York, Ohio, Oregon, South Dakota, West Virginia, Wyoming) between 2000 and 2002. Subjects: Subjects included 413 women with at least one child under the age of 13 years living in the home. Results: Findings based on the 184 subjects with complete data indicated that the causal relationship between household food insecurity and depression is bidirectional (P = 0.034 for causation from depression to food insecurity, P = 0.003 for causation from food insecurity to depression, χ2/df = 1.835, root-mean-square error of approximation = 0.068, comparative fit index = 0.989). Findings based on all 413 subjects after imputation of missing values also indicated bidirectionality. Conclusions: The recursive relationship between food insecurity and depression has implications for US nutrition, mental health and poverty policies. The study highlights the need to integrate programs addressing food insecurity and poor mental health for the population of rural, low-income women

Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction

Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy, and develop new therapies for patients. Currently, the predictive results of deep learning (DL)-based models are better than or as good as standard survival methods, they are often disregarded because of their lack of transparency and little interpretability, which is crucial to their adoption in clinical applications. In this paper, we introduce a novel, easily deployable approach, called EXplainable CEnsored Learning (EXCEL), to iteratively exploit critical variables and simultaneously implement (DL) model training based on these variables. First, on a toy dataset, we illustrate the principle of EXCEL; then, we mathematically analyze our proposed method, and we derive and prove tight generalization error bounds; next, on two semi-synthetic datasets, we show that EXCEL has good anti-noise ability and stability; finally, we apply EXCEL to a variety of real-world survival datasets including clinical data and genetic data, demonstrating that EXCEL can effectively identify critical features and achieve performance on par with or better than the original models. It is worth pointing out that EXCEL is flexibly deployed in existing or emerging models for explainable survival data in the presence of right censoring.Comment: 39 page

Food insecurity and maternal depression in rural, low-income families: A longitudinal investigation

Objective: The purpose of the present study was to examine the relationship between household food insecurity and maternal depression in a rural sample to determine whether food insecurity predicted mothers’ depression over time or vice versa. Design: The study employed a prospective design using three waves of data from ‘Rural Families Speak’, a multi-state study of low-income rural families in the USA. Food insecurity was measured using the Core Food Security Module and depression was measured using the Center for Epidemiologic Studies–Depression Scale. A structural equation model was fit to the data using the AMOS software package. Setting: Sixteen states in the USA (California, Indiana, Kentucky, Louisiana, Massachusetts, Maryland, Michigan, Minnesota, Nebraska, New Hampshire, New York, Ohio, Oregon, South Dakota, West Virginia, Wyoming) between 2000 and 2002. Subjects: Subjects included 413 women with at least one child under the age of 13 years living in the home. Results: Findings based on the 184 subjects with complete data indicated that the causal relationship between household food insecurity and depression is bidirectional (P = 0.034 for causation from depression to food insecurity, P = 0.003 for causation from food insecurity to depression, χ2/df = 1.835, root-mean-square error of approximation = 0.068, comparative fit index = 0.989). Findings based on all 413 subjects after imputation of missing values also indicated bidirectionality. Conclusions: The recursive relationship between food insecurity and depression has implications for US nutrition, mental health and poverty policies. The study highlights the need to integrate programs addressing food insecurity and poor mental health for the population of rural, low-income women

Thinking outside the curve, part II: modeling fetal-infant mortality

<p>Abstract</p> <p>Background</p> <p>Greater epidemiologic understanding of the relationships among fetal-infant mortality and its prognostic factors, including birthweight, could have vast public health implications. A key step toward that understanding is a realistic and tractable framework for analyzing birthweight distributions and fetal-infant mortality. The present paper is the second of a two-part series that introduces such a framework.</p> <p>Methods</p> <p>We propose estimating birthweight-specific mortality within each component of a normal mixture model representing a birthweight distribution, the number of components having been determined from the data rather than fixed <it>a priori</it>.</p> <p>Results</p> <p>We address a number of methodological issues related to our proposal, including the construction of confidence intervals for mortality risk at any given birthweight within a component, for odds ratios comparing mortality within two different components from the same population, and for odds ratios comparing mortality within analogous components from two different populations. As an illustration we find that, for a population of white singleton infants, the odds of mortality at 3000 g are an estimated 4.15 times as large in component 2 of a 4-component normal mixture model as in component 4 (95% confidence interval, 2.04 to 8.43). We also outline an extension of our framework through which covariates could be probabilistically related to mixture components. This extension might allow the assertion of approximate correspondences between mixture components and identifiable subpopulations.</p> <p>Conclusions</p> <p>The framework developed in this paper does not require infants from compromised pregnancies to share a common birthweight-specific mortality curve, much less assume the existence of an interval of birthweights over which all infants have the same curve. Hence, the present framework can reveal heterogeneity in mortality that is undetectable via a contaminated normal model or a 2-component normal mixture model.</p