Changes to Washington State\u27s recreational use criteria and implications for surface waters

Washington State’s surface water quality standards set limits on pollution in lakes, rivers, and marine waters in order to protect beneficial uses, such as swimming and fishing. Washington State Department of Ecology has recently announced a rulemaking to update recreational use criteria (RUC). Recreational use criteria are intended to protect human health while enjoying water-related activities. Recreational use criteria are based on bacterial indicators rather than direct measurements of pathogens. Washington’s current bacterial indicator, fecal coliform, was removed from the Environmental Protection Agency’s (EPA) recommendations in 1986. The EPA is now requiring states update their RUC to the new bacterial indicators, Escherichia coli (E. coli) or enterococcus. EPA epidemiological studies have demonstrated that fecal coliform does not correlate with gastrointestinal illnesses and is not a suitable indicator for recreating in waters. Contrarily, E. coli and enterococcus have a strong correlation with swimming-related gastrointestinal illnesses. In marine waters, Washington has adopted a single fecal coliform criterion for shellfish harvesting and primary contact recreation uses. Shellfish harvesting is regulated by the Federal Drug Administration (FDA) and has a more stringent fecal coliform criterion than contact recreation. To protect for both uses, Washington adopted the more stringent FDA’s fecal coliform criterion for shellfish harvesting and applied it to primary contact recreation. However, with the advent of new bacterial indicators, the shellfish harvesting and the primary contact recreation criterion will become decoupled. Shellfish harvesting will continue using FDA’s fecal coliform based criteria, while contact recreation will be based on enterococcus for marine waters. The objective of this presentation is to discuss the options for RUC for Washington State, implementation of new criteria, and policy outcomes of the rulemaking. Other topics will include determining acceptable levels of risk using bacterial indicators, background risks, and site-specific variability

Incorporation of genetic model parameters for cost-effective designs of genetic association studies using DNA pooling

<p>Abstract</p> <p>Background</p> <p>Studies of association methods using DNA pooling of single nucleotide polymorphisms (SNPs) have focused primarily on the effects of "machine-error", number of replicates, and the size of the pool. We use the non-centrality parameter (NCP) for the analysis of variance test to compute the approximate power for genetic association tests with DNA pooling data on cases and controls. We incorporate genetic model parameters into the computation of the NCP. Parameters involved in the power calculation are disease allele frequency, frequency of the marker SNP allele in coupling with the disease locus, disease prevalence, genotype relative risk, sample size, genetic model, number of pools, number of replicates of each pool, and the proportion of variance of the pooled frequency estimate due to machine variability. We compute power for different settings of number of replicates and total number of genotypings when the genetic model parameters are fixed. Several significance levels are considered, including stringent significance levels (due to the increasing popularity of 100 K and 500 K SNP "chip" data). We use a factorial design with two to four settings of each parameter and multiple regression analysis to assess which parameters most significantly affect power.</p> <p>Results</p> <p>The power can increase substantially as the genotyping number increases. For a fixed number of genotypings, the power is a function of the number of replicates of each pool such that there is a setting with maximum power. The four most significant parameters affecting power for association are: (1) genotype relative risk, (2) genetic model, (3) sample size, and (4) the interaction term between disease and SNP marker allele probabilities.</p> <p>Conclusion</p> <p>For a fixed number of genotypings, there is an optimal number of replicates of each pool that increases as the number of genotypings increases. Power is not substantially reduced when the number of replicates is close to but not equal to the optimal setting.</p

Animal models for COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the aetiological agent of coronavirus disease 2019 (COVID-19), an emerging respiratory infection caused by the introduction of a novel coronavirus into humans late in 2019 (frst detected in Hubei province, China). As of 18 September 2020, SARS-CoV-2 has spread to 215 countries, has infected more than 30 million people and has caused more than 950,000 deaths. As humans do not have pre-existing immunity to SARS-CoV-2, there is an urgent need to develop therapeutic agents and vaccines to mitigate the current pandemic and to prevent the re-emergence of COVID-19. In February 2020, the World Health Organization (WHO) assembled an international panel to develop animal models for COVID-19 to accelerate the testing of vaccines and therapeutic agents. Here we summarize the fndings to date and provides relevant information for preclinical testing of vaccine candidates and therapeutic agents for COVID-19.info:eu-repo/semantics/acceptedVersio

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Abstract Background Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions. </jats:sec

Exit Strategies For Business School Deans

&nbsp; &nbsp;While significant thought and discussion have been devoted toward becoming a business school dean, little attention has focused on exiting a successful deanship. This paper evaluates dean turnover, highlighting differences between intended versus actual exit strategies using survey data. We delineate the various exit options and address the advantages and disadvantages associated with each. Current and prospective business school deans, as well as university administrators, may utilize these findings to better prepare for leadership transitions

Power and sample size calculations in the presence of phenotype errors for case/control genetic association studies

Abstract Background Phenotype error causes reduction in power to detect genetic association. We present a quantification of phenotype error, also known as diagnostic error, on power and sample size calculations for case-control genetic association studies between a marker locus and a disease phenotype. We consider the classic Pearson chi-square test for independence as our test of genetic association. To determine asymptotic power analytically, we compute the distribution's non-centrality parameter, which is a function of the case and control sample sizes, genotype frequencies, disease prevalence, and phenotype misclassification probabilities. We derive the non-centrality parameter in the presence of phenotype errors and equivalent formulas for misclassification cost (the percentage increase in minimum sample size needed to maintain constant asymptotic power at a fixed significance level for each percentage increase in a given misclassification parameter). We use a linear Taylor Series approximation for the cost of phenotype misclassification to determine lower bounds for the relative costs of misclassifying a true affected (respectively, unaffected) as a control (respectively, case). Power is verified by computer simulation. Results Our major findings are that: (i) the median absolute difference between analytic power with our method and simulation power was 0.001 and the absolute difference was no larger than 0.011; (ii) as the disease prevalence approaches 0, the cost of misclassifying a unaffected as a case becomes infinitely large while the cost of misclassifying an affected as a control approaches 0. Conclusion Our work enables researchers to specifically quantify power loss and minimum sample size requirements in the presence of phenotype errors, thereby allowing for more realistic study design. For most diseases of current interest, verifying that cases are correctly classified is of paramount importance.</p

Computing Asymptotic Power and Sample Size for Case-Control Genetic Association Studies in the Presence of Phenotype and/or Genotype Misclassification Errors

It is well established that phenotype and genotype misclassification errors reduce the power to detect genetic association. Resampling a subset of the data (e.g, double-sampling) of genotype and/or phenotype with a gold standard measurement is one method to address this issue. We derive the non-centrality parameter (NCP) for the recently published Likelihood Ratio Test Allowing for Error (LRTae) in the presence of random phenotype and genotype errors. With the NCP, power and sample size can be analytically determined at any significance level. We verify analytic power with simulations using a 2**k factorial design given high and low settings of: case and control genotype frequencies, phenotype and genotype misclassification probabilities, total sample size, ratio of cases to controls, and proportions of phenotype and/or genotype double-samples. We also perform example applications of our method assuming equal costs for the LRTae method and the standard method that does not use double-sample information (LRTstd) to determine if power gain due to double-sampling a proportion of samples outweighs the reduction in sample size due to additional costs in obtaining double-samples.Our results showed a median difference of at most 0.01 between analytic and simulation power for the factorial design settings, with maximum difference of 0.054. For our cost/benefits analysis calculations, results for genotype errors are that double-sampling appears most beneficial (in terms of power gain) when cost of double-sampling is relatively low, irrespective of the proportion of individuals double-sampled. In the presence of phenotype error, there is always power gain using the LRTae method for the parameter settings considered. We have freely available software that performs power and sample size calculations for the LRTae method and cost/benefits analyses comparing power for LRTae and LRTstd methods assuming equal costs.

A study of alternative fuel impacts to navy fueling infrastructure

Energy reform in the United States Department of the Navy is currently a leading priority. Supporting reform efforts, the Honorable Ray Mabus, Secretary of the Navy, set a goal to sail a "Green Strike Group" composed of ships powered by alternative fuels by 2016. This report details considerations for implementing an alternative fuel for the Green Strike Group. This is accomplished by developing the requirements for an alternative fuel, analyzing several potential candidates, and recommending a preferred alternative (Fischer-Tropsch S-5). Additionally, this report describes the existing infrastructure supporting fuel distribution to Navy ships and explores options for changes necessary to support the selected alternative fuel. A notional mission profile is depicted, showing the Green Strike Group's progress from Norfolk, Virginia to the Arabian Sea and back again over the course of a 180-day deployment. A deterministic fuel estimation model and the succeeding, higher fidelity stochastic model are described, leading to the prediction of alternative fuel amount requirements and necessary geographic placement. Finally, this report concludes with the assertion that while sailing the Green Strike Group is technologically possible, significant and immediate economic investments are needed in order to realize the Secretary of the Navy's goal by 2016.http://archive.org/details/astudyoflternati109456953Approved for public release; distribution is unlimited