A Case Report of Acute Heart Failure Due to Infective Aortic Endocarditis Diagnosed by Point-of-care Ultrasound

Introduction: Infective endocarditis (IE) is a life-threatening condition with significant morbidity and mortality, and can require surgical repair.Case Report: A 36-year-old man presented to the emergency department for worsening dyspnea and chest pain. Point-of-care echocardiography demonstrated a mobile oscillating mass on the aortic valve with poor approximation of the valve leaflets, suggesting aortic valve insufficiency secondary to IE as the cause of acute heart failure. The patient underwent emergent aortic valve replacement within 24 hours.Discussion: While point-of-care echocardiography has been well documented in identifying tricuspid vegetations, aorticvalve involvement and subsequent heart failure is less well described. Earlier recognition of aortic valve vegetations and insufficiency can expedite surgical intervention, with decreased complication rates linked to earlier antimicrobial therapy.Conclusion: This case report highlights the ability of point-of-care ultrasound to identify aortic vegetations, allowing for the earlier diagnosis and therapy

Cotton seedling diseases : answers to frequently asked questions

"This publication supersedes MU publication G4254."--Page 2."What are cotton seedling diseases and what causes them? Several different, normally harmless, microscopic organisms that live on organic matter in the soil can attack cotton seedling roots in the spring. These organisms are called fungi. The ones most commonly found attacking cotton in Missouri are named Pythium, Fusarium, Rhizoctonia and Thielaviopsis. A plant may be attacked by one of these or by several at the same time. Each of these organisms causes a different disease, and the symptoms are different for each. However, they are collectively known as seedling diseases. The organisms that cause seedling diseases are present in most soils. Once established, they remain there indefinitely. They produce structures that enable them to survive in the soil from year to year. Seedling diseases become worse when the soil is cool and most especially when the soil is wet. These conditions do not develop in Missouri every year. Because of yearly variations in weather, the severity of cotton seedling diseases also varies. Cotton seedling diseases cause more yield loss than any other disease in Missouri."--First page.Reviewed by Bradley Wilson, Division of Plant Science

Fleeing to Fault Zones: Incorporating Syrian Refugees into Earthquake Risk Analysis along the East Anatolian and Dead Sea Rift Fault Zones

The influx of millions of Syrian refugees into Turkey has rapidly changed the population distribution along the Dead Sea Rift and East Anatolian Fault zones. In contrast to other countries in the Middle East where refugees are accommodated in camp environments, the majority of displaced individuals in Turkey are integrated into local cities, towns, and villages—placing stress on urban settings and increasing potential exposure to strong earthquake shaking. Yet, displaced populations are not traditionally captured in data sources used in earthquake risk analysis or loss estimations. Accordingly, this study presents a district-level analysis assessing the spatial overlap of earthquake hazards and refugee locations in southeastern Turkey, in hopes of determining how migration patterns are altering seismic risk in the region. Using migration estimates from the U.S. Humanitarian Information Unit, district-level population scenarios that combine official population statistics with camped and non-camped refugee population bounds were created. Probabilistic seismic hazard analysis was performed alongside these scenarios to map spatial variations in seismic risk between 2011 and 2015. Results show a relative southward increase of seismic risk for this period due to refugee migration. Additionally, earthquake fatalities were calculated using a semi-empirical loss estimation technique on five faults to determine degree of under-estimation resulting from forgoing migration data in loss modeling. It was found that refugee populations increase casualties by 11-12% using median population estimates, and upwards of 20% using high population estimates. These findings communicate the ongoing importance of placing environmental hazards in their appropriate regional context which unites physical, political, cultural, and socio-economic landscapes

Analysing the Effects of Excise Taxes Using Microsoft Excel

This paper examines the effects of an excise tax imposed on a monopolist's product. Then simple but quite general polynomial demand and cost curves are introduced and discussed, as is the Microsoft Excel workbook that embeds the functions. Finally, exercises based on selected special cases illustrating the use of the workbook are sketched.

Improving Earthquake Disaster Models with Post-Event Data: Insights from the 2015 Gorkha, Nepal Earthquake

Immense amounts of data are collected following earthquake disasters. Yet, it remains unclear how researchers’ might take full advantage of diverse post-disaster datasets. Using data from the 2015 Gorkha Nepal earthquake, this dissertation explores three ways in which post- disaster survey and assessment datasets can be used to inform models of seismic risk, vulnerability, and recovery processes. The first article presents an empirical analysis of scale issues in disaster vulnerability indices using a novel dataset of 750,000 households. This study finds that using aggregated household data to create social vulnerability indices can produce results that are meaningfully different from equivalent indices produced directly with household-level data. These results inform future development of vulnerability indices. The second article develops a Bayesian item-response theory modeling framework for estimating household-level reconstruction behavior from reconstruction progress surveys. This study provides a new way to quantitatively assess earthquake recovery, with results showing large differences in reconstruction probabilities among different levels of aid receipt, household willingness to commit additional resources, and geographic location. The final article uses engineering damage assessment data to develop a model for spatially interpolating geolocated clusters of rapid damage assessments onto a high-resolution grid. Incorporating ground truthed data significantly improves existing rapid estimates for completely damaged buildings and is feasible with the current scope of rapid damage assessment collection. Together, these contributions cast a vision for an improved disaster modeling ecosystem that more effectively integrates novel post-disaster data streams

EFFECT OF TWO PRACTICE SCENARIOS ON SONG MEMORIZATION ACCURACY

This study examined song memorization sequences using memorization accuracy scores. Vocal performers (N=42) were split into two groups. The participants in Group A were first asked to memorize only the text of a song in both non-rhythmic and rhythmic forms, then were asked to memorize only the melody of a song. The participants in Group B were first asked to memorize only the melody of a song, then to memorize only the text of a song in both non-rhythmic and rhythmic forms. Both groups were allowed to hear the whole song performed with words and melody before their memorization tasks and were allowed a short period of time to practice the whole song after their other memorization tasks. Participant scores on a final test of memorization reflected accuracy in text, intervals, and rhythms. Results indicated no significant difference in overall test scores according to memorization sequence. However, graduate students scored significantly higher than undergraduate students, and students with four or more years of piano study scored significantly higher than students with fewer than four years of piano study. Results were discussed in terms of memorization strategies for texted music, performance scoring methodologies, and suggestions for future research

On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies

The broader goal of this paper is to provide social researchers with some analytical guidelines when investigating structural equation models (SEM) with predominantly a formative specification. This research is the first to investigate the robustness and precision of parameter estimates of a formative SEM specification. Two distinctive scenarios (normal and non-normal data scenarios) are compared with the aid of a Monte Carlo simulation study for various covariance-based structural equation modeling (CBSEM) estimators and various partial least squares path modeling (PLS-PM) weighting schemes. Thus, this research is also one of the first to compare CBSEM and PLS-PM within the same simulation study. We establish that the maximum likelihood (ML) covariance-based discrepancy function provides accurate and robust parameter estimates for the formative SEM model under investigation when the methodological assumptions are met (e.g., adequate sample size, distributional assumptions, etc.). Under these conditions, ML-CBSEM outperforms PLS-PM. We also demonstrate that the accuracy and robustness of CBSEM decreases considerably when methodological requirements are violated, whereas PLS-PM results remain comparatively robust, e.g. irrespective of the data distribution. These findings are important for researchers and practitioners when having to choose between CBSEM and PLS-PM methodologies to estimate formative SEM in their particular research situation.PLS, path modeling, covariance structure analysis, structural equation modeling, formative measurement, simulation study

On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies

The broader goal of this paper is to provide social researchers with some analytical guidelines when investigating structural equation models (SEM) with predominantly a formative specification. This research is the first to investigate the robustness and precision of parameter estimates of a formative SEM specification. Two distinctive scenarios (normal and non-normal data scenarios) are compared with the aid of a Monte Carlo simulation study for various covariance-based structural equation modeling (CBSEM) estimators and various partial least squares path modeling (PLS-PM) weighting schemes. Thus, this research is also one of the first to compare CBSEM and PLS-PM within the same simulation study. We establish that the maximum likelihood (ML) covariance-based discrepancy function provides accurate and robust parameter estimates for the formative SEM model under investigation when the methodological assumptions are met (e.g., adequate sample size, distributional assumptions, etc.). Under these conditions, ML-CBSEM outperforms PLS-PM. We also demonstrate that the accuracy and robustness of CBSEM decreases considerably when methodological requirements are violated, whereas PLS-PM results remain comparatively robust, e.g. irrespective of the data distribution. These findings are important for researchers and practitioners when having to choose between CBSEM and PLS-PM methodologies to estimate formative SEM in their particular research situation.marketing ;