The Charlatan's Trial: An ltalian Surgeon in the Court of King Philip II, 1576-1577

NoFecha 2014-09-15.--Fujitsu ScanSnap iX500.--Unidad técnica IHMC.--Archivo depósito IHMC.--Solo se permite el uso con fines educativos y de investigació

Spain and the Scientific Revolution: Historiographical Questions and Conjectures

Sociedad Estatal de Conmemoraciones Culturales. Instituto Universitario de Historia, Simancas. Ministerio de Educación y CienciaN

Infectious Fear: The Rhetoric of Pestilence in Middle English Didactic Texts on Death

This article examines literary references to bubonic plague in a sample of late fourteenth- and fifteenth-century English texts that are didactic in tone and address the theme of death, including Geoffrey Chaucer’s “The Pardoner’s Tale”, John Lydgate’s “Danse Macabre” and the anonymous The Castle of Perseverance and “A Disputation between the Body and Worms”. Although there have been broad surveys of bubonic plague in Middle English literature, as well as studies of isolated texts, this article is the first to examine the role of pestilence in a group of texts linked by theme and authorial intention. It contributes to current understanding of the disease in late medieval literature and culture, showing how authors utilised the idea of pestilence as a frightening cause of sudden death and as a form of rhetoric serving to encourage readers to reflect on mortality, the spiritual health of the soul and the prospect of salvation. Whereas previous research has shown that doctors, priests and writers interpreted the pestilence as a divine punishment for sin, this study demonstrates how that belief could be exploited for rhetorical purposes. The rhetoric of pestilence emerges as a powerful contemplative tool urging readers to practise self-examination, penitence and a more active, strategic approach to death

Conduct disorder in girls: neighborhoods, family characteristics, and parenting behaviors

<p>Abstract</p> <p>Background</p> <p>Little is known about the social context of girls with conduct disorder (CD), a question of increasing importance to clinicians and researchers. The purpose of this study was to examine the associations between three social context domains (neighborhood, family characteristics, and parenting behaviors) and CD in adolescent girls, additionally testing for race moderation effects. We predicted that disadvantaged neighborhoods, family characteristics such as parental marital status, and parenting behaviors such as negative discipline would characterize girls with CD. We also hypothesized that parenting behaviors would mediate the associations between neighborhood and family characteristics and CD.</p> <p>Methods</p> <p>We recruited 93 15–17 year-old girls from the community and used a structured psychiatric interview to assign participants to a CD group (n = 52) or a demographically matched group with no psychiatric disorder (n = 41). Each girl and parent also filled out questionnaires about neighborhood, family characteristics, and parenting behaviors.</p> <p>Results</p> <p>Neighborhood quality was not associated with CD in girls. Some family characteristics (parental antisociality) and parenting behaviors (levels of family activities and negative discipline) were characteristic of girls with CD, but notll. There was no moderation by race. Our hypothesis that the association between family characteristics and CD would be mediated by parenting behaviors was not supported.</p> <p>Conclusion</p> <p>This study expanded upon previous research by investigating multiple social context domains in girls with CD and by selecting a comparison group who were not different in age, social class, or race. When these factors are thus controlled, CD in adolescent girls is not significantly associated with neighborhood, but is associated with some family characteristics and some types of parental behaviors. However, the mechanisms underlying these relationships need to be further investigated. We discuss possible explanations for our findings and suggest directions for future research.</p

Comment letters to the National Commission on Commission on Fraudulent Financial Reporting, 1987 (Treadway Commission) Vol. 2

https://egrove.olemiss.edu/aicpa_sop/1662/thumbnail.jp

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages