The Importance of Weathered Crude Oil as a Source of Hydrocarbonoclastic Microorganisms in Contaminated Seawater

( Sheppard Petra J ),( Keryn L Simons ),( Krishna K Kadali ),( Sayali S Patil ),( Andrew S Ball ) 한국미생물 · 생명공학회 2012 Journal of microbiology and biotechnology Vol.22 No.9

This study investigated the hydrocarbonoclastic microbial community present on weathered crude oil and their ability to degrade weathered oil in seawater obtained from the Gulf St. Vincent (SA, Australia). Examination of the native seawater communities capable of utilizing hydrocarbon as the sole carbon source identified a maximum recovery of just 6.6 × 101 CFU/ml, with these values dramatically increased in the weathered oil, reaching 4.1 × 104 CFU/ml. The weathered oil (dominated by >C30 fractions; 750,000 ± 150,000 mg/l) was subject to an 8 week laboratory-based degradation microcosm study. By day 56, the natural inoculums degraded the soluble hydrocarbons (initial concentrations 3,400 ± 700 mg/l and 1,700 ± 340 mg/l for the control and seawater, respectively) to below detectable levels, and biodegradation of the residual oil reached 62% (254,000 ± 40,000 mg/l) and 66% (285,000 ± 45,000 mg/l) in the control and seawater sources, respectively. In addition, the residual oil gas chromatogram profiles changed with the presence of short and intermediate hydrocarbon chains. 16S rDNA DGGE sequence analysis revealed species affiliated with the genera Roseobacter, Alteromonas, Yeosuana aromativorans, and Pseudomonas, renowned oil-degrading organisms previously thought to be associated with the environment where the oil contaminated rather than also being present in the contaminating oil. This study highlights the importance of microbiological techniques for isolation and characterisation, coupled with molecular techniques for identification, in understanding the role and function of native oil communities.

Turbulent Combustion in SI Engines

CGW.Sheppard 한국자동차공학회 1998 한국자동차공학회 기타 간행물 Vol.- No.-

Poster Session : PS 0304 ; Geriatrics : “I Love You Honey”: Does Dementia Affect the Normal Grieving Process?

( Erin Sheppard ),( Halina Kusz ) 대한내과학회 2014 대한내과학회 추계학술대회 Vol.2014 No.1

Background: Grieving after the loss of a loved one is a normal process. People diagnosed with dementia also experience grief, but the manifestation may be different. Very little has been studied with regard to grief in an individual with dementia. Methods: We present the case study of a nursing home patient with advanced Alzheimer`s dementia diagnosed with depression after the sudden death of his wife. When asked about their relationship the patient usually answered “This is my wife, we are married 69 years”, and her typical response was “I love you honey”. Following her death, patient became withdrawn, had poor appetite with weight loss, and only responded to simple questions with ‘yes’ or ‘no’. We discuss this case example, along with a review of the literature, to develop several conclusions regarding dementia and grief. Results: Our search yielded little on this topic. Based on a review of the dementia literature, we offer several Conclusions: Many thoughts regarding grief and dementia are expressed in the literature, however, they are mainly related to caregivers` grief. The areas of the brain responsible for memory are distinct from those associated with emotion. Although those with dementia may be unable to logically express why they are experiencing various emotional states, it is quite clear that they do continue to experience these emotions, including grief. Conclusions: Although Alzheimer`s Dementia has signifi cant cognitive affects on those in which it is diagnosed, individuals do not lose all of their faculties immediately. Many components remain long after the degenerative process has begun. We should expect that those with Alzheimer`s are capable of experiencing emotional reactions to the loss of loved ones. Individuals should be allowed to experience and express their grief, as part of a healthy bereavement process.

Risk of Brain Tumor Induction from Pediatric Head CT Procedures: A Systematic Literature Review

( John P. Sheppard ),( Thien Nguyen ),( Yasmine Alkhalid ),( Joel S. Beckett ),( Noriko Salamon ),( Isaac Yang ) 대한뇌종양학회 대한신경종양학회 2018 Brain Tumor Research and Treatment Vol.6 No.1

Head computed tomography (CT) is instrumental for managing patients of all ages. However, its low dose radiation may pose a low but non-zero risk of tumor induction in pediatric patients. Here, we present a systematic literature review on the estimated incidence of brain tumor induction from head CT exams performed on children and adolescents. MEDLINE was searched using an electronic protocol and bibliographic searches to identify articles related to CT, cancer, and epidemiology or risk assessment. Sixteen studies that predicted or measured head CT-related neoplasm incidence or mortality were identified and reviewed. Epidemiological studies consistently cited increased tumor incidence in pediatric patients (ages 0-18) exposed to head CTs. Excess relative risk of new brain tumor averaged 1.29 (95% confidence interval, 0.66-1.93) for pediatric patients exposed to one or more head CTs. Tumor incidence increased with number of pediatric head CTs in a dose-dependent manner, with measurable excess incidence even after a single scan. Converging evidence from epidemiological studies supported a small excess risk of brain tumor incidence after even a single CT exam in pediatric patients. However, refined epidemiological methods are needed to control for confounding variables that may contribute to reverse causation, such as patients with pre-existing cancer or cancer susceptibility. CT remains an invaluable technology that should be utilized so long as there is clinical indication for the study and the radiation dose is as small as reasonably achievable.

Genetic Diversity of the Honey Bee and possible interactions with climate change

Walter S. Sheppard 한국응용곤충학회 2013 한국응용곤충학회 학술대회논문집 Vol.2013 No.10

With over 7 billion people on the planet, agriculture faces immense pressure to meet global demands for food. One third of consumed food relies on insect pollination with by far, the predominate pollinator being the honey bee, Apis mellifera. Although future challenges facing agriculture will come from multiple domains, one of the immediate challenges is honey bee decline. Stress associated with transportation, pesticide exposure nutritional limitations, various diseases and pests have all been recognized as potential factors in honey bee decline. With the prospect of future global changes in climate, honey bees will also face changes in forage availability and overwintering potential. At the level of the individual colony, research has shown that honey bee health is directly correlated to genetic diversity. Increased colony diversity is associated with lower disease intensity, increased disease resistance, greater workforce productivity and thermoregulation stability. Genetic diversity at the population level serves as the raw material for selective breeding in agriculturally important plants and animals, including the honey bee. Honey bees are not native to Korea, however, and importation and founder events associated with the establishment of honey bees represent a series of genetic bottlenecks that limits the diversity of introduced honey bee populations. Fortunately, Apis mellifera consists of around 28 recognized subspecies within its native range, each with specific adaptations to climatic selective pressures endemic to its own location. Climate change is expected to be bring a high degree of uncertainty in the future to climate expression in various locations. Fortunately, the honey bee has a wide breadth of diversity contained within various subspecies and careful importation and evaluation of specific stocks may be highly useful as we enter climate uncertainty in the future. With the recognition that agro-ecosystems are highly interconnected and multifaceted, one of the greatest challenges facing agriculture is preserving and improving honey bee health.

Racial Differences in Hospital Stays among Patients Undergoing Craniotomy for Tumour Resection at a Single Academic Hospital

( John P. Sheppard ),( Carlito Lagman ),( Prasanth Romiyo ),( Thien Nguyen ),( Daniel Azzam ),( Yasmine Alkhalid ),( Courtney Duong ),( Isaac Yang ) 대한뇌종양학회·대한신경종양학회·대한소아뇌종양학회 2019 Brain Tumor Research and Treatment Vol.7 No.2

Background Racial differences in American patients undergoing brain tumour surgery remain poorly characterized within urban medical centres. Our objective was to assess racial differences in operative brain tumour patients at a single academic hospital in Los Angeles, California. Methods We reviewed medical records of adult patients undergoing craniotomy for tumour resection from March 2013 to January 2017 at UCLA Medical Centre. Patients were categorized as Asian, Hispanic, Black, or White. Racial cohorts were matched on demographic variables for comparisons. Our primary outcome was post-operative length of stay (LOS). Secondary outcomes included hospital mortality and discharge disposition. Results In this study, 462 patients identified as Asian (15.1%), Hispanic (8.7%), Black (3.9%), or White (72.3%). After cohort matching, non-White patients had elevated risk of prolonged LOS [odds ratio (OR)=2.62 (1.44, 4.76)]. No differences were observed in hospital mortality or non-routine discharge. Longer LOS was positively correlated with non-routine discharge [rpb (458)=0.41, p<0.001]. Black patients with government insurance had average LOS 2.84 days shorter than Black patients with private insurance (p=0.04). Among Hispanics, government insurance was associated with nonroutine discharge [OR=4.93 (1.03, 24.00)]. Conclusion Racial differences manifested as extended LOS for non-White patients, with comparable rates of hospital mortality and non-routine discharge across races. Prolonged LOS loosely reflected complicated clinical course with greater risk of adverse discharge disposition. Private insurance coverage predicted markedly lower risk of non-routine discharge for Hispanic patients, and LOS of three additional days among Black patients. Further research is needed to elucidate the basis of these differences.

Combining PM _2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures

Kim, Sun-Young,Sheppard, Lianne,Larson, Timothy V.,Kaufman, Joel D.,Vedal, Sverre U.S. Dept. of Health, Education, and Welfare, Publ 2015 Environmental health perspectives Vol.123 No.7

<P><B>Background</B></P><P>Regulatory monitoring data have been the exposure data resource most commonly applied to studies of the association between long-term PM<SUB>2.5</SUB> components and health. However, data collected for regulatory purposes may not be compatible with epidemiological studies.</P><P><B>Objectives</B></P><P>We studied three important features of the PM<SUB>2.5</SUB> component monitoring data to determine whether it would be appropriate to combine all available data from multiple sources for developing spatiotemporal prediction models in the National Particle Component and Toxicity (NPACT) study.</P><P><B>Methods</B></P><P>The NPACT monitoring data were collected in an extensive monitoring campaign targeting cohort participant residences. The regulatory monitoring data were obtained from the Chemical Speciation Network (CSN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE). We performed exploratory analyses to examine features that could affect our approach to combining data: comprehensiveness of spatial coverage, comparability of analysis methods, and consistency in sampling protocols. In addition, we considered the viability of developing spatiotemporal prediction models given <I>a</I>) all available data, <I>b</I>) NPACT data only, and <I>c</I>) NPACT data with temporal trends estimated from other pollutants.</P><P><B>Results</B></P><P>The number of CSN/IMPROVE monitors was limited in all study areas. The different laboratory analysis methods and sampling protocols resulted in incompatible measurements between networks. Given these features we determined that it was preferable to develop our spatiotemporal models using only the NPACT data and under simplifying assumptions.</P><P><B>Conclusions</B></P><P>Investigators conducting epidemiological studies of long-term PM<SUB>2.5</SUB> components need to be mindful of the features of the monitoring data and incorporate this understanding into the design of their monitoring campaigns and the development of their exposure prediction models.</P><P><B>Citation</B></P><P>Kim SY, Sheppard L, Larson TV, Kaufman JD, Vedal S. 2015. Combining PM<SUB>2.5</SUB> component data from multiple sources: data consistency and characteristics relevant to epidemiological analyses of predicted long-term exposures. Environ Health Perspect 123:651–658; http://dx.doi.org/10.1289/ehp.1307744</P>

Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM _2.5 in Cohort Studies before the 1999 Implementation of Widespread Monitoring

Kim, Sun-Young,Olives, Casey,Sheppard, Lianne,Sampson, Paul D.,Larson, Timothy V.,Keller, Joshua P.,Kaufman, Joel D. National Institute of Environmental Health Science 2017 Environmental health perspectives Vol.125 No.1

<P><B>Introduction:</B></P><P>Recent cohort studies have used exposure prediction models to estimate the association between long-term residential concentrations of fine particulate matter (PM2.5) and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The U.S. Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999.</P><P><B>Objectives:</B></P><P>We evaluated a novel statistical approach to produce high-quality exposure predictions from 1980 through 2010 in the continental United States for epidemiological applications.</P><P><B>Methods:</B></P><P>We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. Temporal trends before 1999 were estimated by using a) extrapolation based on PM2.5 data in FRM/IMPROVE, b) PM2.5 sulfate data in the Clean Air Status and Trends Network, and c) visibility data across the Weather Bureau Army Navy network. We validated the models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Children’s Health Study (CHS), and the Inhalable Particulate Network (IPN).</P><P><B>Results:</B></P><P>In our validation using pre-1999 data, the prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2 = 0.84–0.91) with lower R2 values in early years. Model performance using CARB dichot and IPN data was worse (R2 = 0.00–0.85) most likely because of fewer monitoring sites and inconsistent sampling methods.</P><P><B>Conclusions:</B></P><P>Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods ≤ 30 years.</P><P><B>Citation:</B></P><P>Kim SY, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, Kaufman JD. 2017. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environ Health Perspect 125:38–46; http://dx.doi.org/10.1289/EHP131</P>

Population dynamics of Tetranychus urticae and Galendromus occidentalis on bent-cane greenhouse roses

Jeong Joon Ahn,Steve W. Sheppard 한국응용곤충학회 2010 한국응용곤충학회 학술대회논문집 Vol.2010 No.10

This study was conducted to examine the ability of the predacious mite G. occidentalis to limit population density of T. urticae infesting roses in the greenhouse, Washington state university, Pullman, U.S.A. Population fluctuations of T. urticae, G. occidentalis, and Macrosiphum euphorbiae were assessed for over two years on greenhouse roses through time series data. During the first half of experiment a stratified random sampling was performed (from May 2000 to May 2001) and the presence/absence and randomized sampling method was utilized during second half (from June 2001 to September 2002). Populations of T. urticae and G. occidentalis were estimated by R-functions or discrete prey-predator equations. During 121 weeks there were six population fluctuations of T. urticae, G. occidentalis and M. euphorbiae. The variation of G .occidentalis was closely associated with that of T. urticae but G. occidentalis did not overexploit T.urticae. The predicted populations of both mites were explained by above equations. M. euphorbiae and T. urticae might be compete for limited resource in the greenhouse rose and the movement of G. occidentalis was inhibited by the honeydew of M. euphorbiae. Therefore, M. euphorbiae showed negative relationship with T. urticae and G. occidentalis from the results of non-linear regression analysis. Although the relationship between T. urticae and G. occidentalis was negative it was unlikely to present complete biological control of T. urticae on greenhouse roses. It might be necessary an integrated management strategy to control T. urticae in bent-cane greenhouse roses.

Health Effects of Long-term Air Pollution: Influence of Exposure Prediction Methods

Kim, Sun-Young,Sheppard, Lianne,Kim, Ho Lippincott Williams Wilkins, Inc. 2009 Epidemiology Vol.20 No.3

BACKGROUND:: Air pollution studies increasingly estimate individual-level exposures from area-based measurements by using exposure prediction methods such as nearest-monitor and kriging predictions. However, little is known about the properties of these methods for health effects estimation. This simulation study explores how 2 common prediction approaches for fine particulate matter (PM2.5) affect relative risk estimates for cardiovascular events in a single geographic area. METHODS:: We estimated 2 sets of parameters to define correlation structures from 2002 data on PM2.5 in the Los Angeles area, and selected additional parameters to evaluate various correlation features. For each structure, annual average PM2.5 was generated at 22 monitoring sites and 2000 preselected individual locations in Los Angeles. Associated survival time until cardiovascular event was simulated for 10,000 hypothetical subjects. Using PM2.5 generated at monitoring sites, we predicted PM2.5 at subject locations by nearest-monitor and kriging interpolation. Finally, we estimated relative risks of the effect of PM2.5 on time to cardiovascular event. RESULTS:: Health effect estimates for cardiovascular events had higher or similar coverage probability for kriging compared with nearest-monitor exposures. The lower mean square error of nearest monitor prediction resulted from more precise but biased health effect estimates. The difference between these approaches dramatically moderated when spatial correlation increased and geographic characteristics were included in the mean model. CONCLUSIONS:: When the underlying exposure distribution has a large amount of spatial dependence, both kriging and nearest-monitor predictions gave good health effect estimates. For exposure with little spatial dependence, kriging exposure was preferable but gave very uncertain estimates.