Woven Suburbia Project: Retrofitting Suburban Neighborhoods with Ecological, Social, and Agricultural Corridors

38 pages. A thesis presented to the Department of Landscape Architecture and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree of Bachelor of Arts, Spring 2016.This thesis presents the landscape architecture design, Woven Suburbia Project. The Woven Suburbia Project implements ecological, social, and agricultural vegetation corridors into existing suburban neighborhoods. Corridors are composed of retrofitted individual residents' front yards and street edges, and are designed through the collaboration of a landscape architect and the suburban residents. Woven Suburbia Project responds to the rapid building methods of Post-World War II suburban development by implementing design elements that support community, local production, and environmental restoration. The design also takes inspiration from three contemporary design movements that work to bring ecological landscape systems into existing urban and suburban development. Ten steps to create a suburban corridor are outlined for a landscape architect to follow, so that this framework can be replicated for suburbs across the country. The redesign of La Costa Knolls, in San Diego, California will demonstrate how to execute the ten steps of the Woven Suburbia Project

Impact of antibacterials on subsequent resistance and clinical outcomes in adult patients with viral pneumonia: An opportunity for stewardship

INTRODUCTION: Respiratory viruses are increasingly recognized as significant etiologies of pneumonia among hospitalized patients. Advanced technologies using multiplex molecular assays and polymerase-chain reaction increase the ability to identify viral pathogens and may ultimately impact antibacterial use. METHOD: This was a single-center retrospective cohort study to evaluate the impact of antibacterials in viral pneumonia on clinical outcomes and subsequent multidrug-resistant organism (MDRO) infections/colonization. Patients admitted from March 2013 to November 2014 with positive respiratory viral panels (RVP) and radiographic findings of pneumonia were included. Patients transferred from an outside hospital or not still hospitalized 72 hours after the RVP report date were excluded. Patients were categorized based on exposure to systemic antibacterials: less than 3 days representing short-course therapy and 3 to 10 days being long-course therapy. RESULTS: A total of 174 patients (long-course, n = 67; short-course, n = 28; mixed bacterial-viral infection, n = 79) were included with most being immunocompromised (56.3 %) with active malignancy the primary etiology (69.4 %). Rhinovirus/Enterovirus (23 %), Influenza (19 %), and Parainfluenza (15.5 %) were the viruses most commonly identified. A total of 13 different systemic antibacterials were used as empiric therapy in the 95 patients with pure viral infection for a total of 466 days-of-therapy. Vancomycin (50.7 %), cefepime (40.3 %), azithromycin (40.3 %), meropenem (23.9 %), and linezolid (20.9 %) were most frequently used. In-hospital mortality did not differ between patients with viral pneumonia in the short-course and long-course groups. Subsequent infection/colonization with a MDRO was more frequent in the long-course group compared to the short-course group (53.2 vs 21.1 %; P = 0.027). CONCLUSION: This study found that long-course antibacterial use in the setting of viral pneumonia had no impact on clinical outcomes but increased the incidence of subsequent MDRO infection/colonization

Epidemiology, co-infections, and outcomes of viral pneumonia in adults an observational cohort study

Advanced technologies using polymerase-chain reaction have allowed for increased recognition of viral respiratory infections including pneumonia. Co-infections have been described for several respiratory viruses, especially with influenza. Outcomes of viral pneumonia, including cases with co-infections, have not been well described. This was observational cohort study conducted to describe hospitalized patients with viral pneumonia including co-infections, clinical outcomes, and predictors of mortality. Patients admitted from March 2013 to November 2014 with a positive respiratory virus panel (RVP) and radiographic findings of pneumonia within 48 h of the index RVP were included. Co-respiratory infection (CRI) was defined as any organism identification from a respiratory specimen within 3 days of the index RVP. Predictors of in-hospital mortality on univariate analysis were evaluated in a multivariate model. Of 284 patients with viral pneumonia, a majority (51.8%) were immunocompromised. A total of 84 patients (29.6%) were found to have a CRI with 48 (57.6%) having a bacterial CRI. Viral CRI with HSV, CMV, or both occurred in 28 patients (33.3%). Fungal (16.7%) and other CRIs (7.1%) were less common. Many patients required mechanical ventilation (54%) and vasopressor support (36%). Overall in-hospital mortality was high (23.2%) and readmissions were common with several patients re-hospitalized within 30 (21.1%) and 90 days (36.7%) of discharge. Predictors of in-hospital mortality on multivariate regression included severity of illness factors, stem-cell transplant, and identification of multiple respiratory viruses. In conclusion, hospital mortality is high among adult patients with viral pneumonia and patients with multiple respiratory viruses identified may be at a higher risk

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

“STEP EASILY INTO THE DREAMER’S TALE”: HEALING TRAUMA THROUGH THE MAGICAL IN THE WORKS OF GABRIEL GARCÍA MÁRQUEZ AND TONI MORRISON

(Statement of Responsibility) by Shelby Meyers(Thesis) Thesis (B.A.) -- New College of Florida, 2019RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE(Bibliography) Includes bibliographical references.This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.Faculty Sponsor: Dimino, Andre