Genetic Influences on Parental Care in Nicrophorus vespilloides

The burying beetle (Nicrophorus vespilloides) has unusually highly developed parental care; parents prepare and maintain a food resource (thereby providing indirect parental care), feed through direct provisioning by regurgitation, and protect their larvae. Parental care is highly variable and can be uniparental female care, uniparental male care, or biparental. There are genetic components to the parenting behaviour of the burying beetle, the amount of direct and indirect care given, and the size of the brood are heritable and therefore genetic traits. In this thesis I have focused on two candidate genes that I predicted would influence parental care behaviour. The first is foraging, which has been shown to influence a range of social and reproductive behaviours in other insect species. Using QRTPCR and pharmacological manipulations I have investigated the role of Nvfor in adult and juvenile burying beetles. The second gene is inotocin, the insect orthologue of oxytocin. Oxytocin has been shown to influence social behaviour as well as many behaviours associated with reproduction in vertebrates and invertebrates, however the effects of inotocin have not yet been investigated in insects. I have used pharmacological manipulations to investigate the role of inotocin in parental behaviour in female burying beetles. Collectively my results demonstrate the central role of Nvfor in the control of direct parental care and the association with major behavioural changes in both adult and larval burying beetles. I have also demonstrated the possible involvement of oxytocin in the control of aggression towards conspecific larvae. These insights suggest the controlling mechanism for the behavioural changes seen in burying beetles is complex and involves interactions between many genes. Combined with previous research on these genes, it is clear they are key components in the evolution of sociality. Finally, my research indicates the power of the candidate gene approach, and suggests additional components of the related pathways that could be investigated.The University of Exete

Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach

In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as ‘low’, ‘medium-low’, ‘medium-high’, and ‘high’. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding

Developing predictive models of health literacy.

IntroductionLow health literacy (LHL) remains a formidable barrier to improving health care quality and outcomes. Given the lack of precision of single demographic characteristics to predict health literacy, and the administrative burden and inability of existing health literacy measures to estimate health literacy at a population level, LHL is largely unaddressed in public health and clinical practice. To help overcome these limitations, we developed two models to estimate health literacy.MethodsWe analyzed data from the 2003 National Assessment of Adult Literacy (NAAL), using linear regression to predict mean health literacy scores and probit regression to predict the probability of an individual having 'above basic' proficiency. Predictors included gender, age, race/ethnicity, educational attainment, poverty status, marital status, language spoken in the home, metropolitan statistical area (MSA) and length of time in U.S.ResultsAll variables except MSA were statistically significant, with lower educational attainment being the strongest predictor. Our linear regression model and the probit model accounted for about 30% and 21% of the variance in health literacy scores, respectively, nearly twice as much as the variance accounted for by either education or poverty alone.ConclusionsMultivariable models permit a more accurate estimation of health literacy than single predictors. Further, such models can be applied to readily available administrative or census data to produce estimates of average health literacy and identify communities that would benefit most from appropriate, targeted interventions in the clinical setting to address poor quality care and outcomes related to LHL

Neighborhood Effects on Health: Concentrated Advantage and Disadvantage

We investigate an alternative conceptualization of neighborhood context and its association with health. Using an index that measures a continuum of concentrated advantage and disadvantage, we examine whether the relationship between neighborhood conditions and health varies by socio-economic status. Using NHANES III data geo-coded to census tracts, we find that while largely uneducated neighborhoods are universally deleterious, individuals with more education benefit from living in highly educated neighborhoods to a greater degree than individuals with lower levels of education

Fruit quality and defect image classification with conditional GAN data augmentation

Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy fruit from undesirable fruit, such as those which are mouldy or damaged. State-of-the-art works in the field report high accuracy results on small datasets

Ambient Particulate Matter Air Pollution and Venous Thromboembolism in the Women’s Health Initiative Hormone Therapy Trials

BackgroundThe putative effects of postmenopausal hormone therapy on the association between particulate matter (PM) air pollution and venous thromboembolism (VTE) have not been assessed in a randomized trial of hormone therapy, despite its widespread use among postmenopausal women.ObjectiveIn this study, we examined whether hormone therapy modifies the association of PM with VTE risk.MethodsPostmenopausal women 50–79 years of age (n = 26,450) who did not have a history of VTE and who were not taking anticoagulants were enrolled in the Women’s Health Initiative Hormone Therapy trials at 40 geographically diverse U.S. clinical centers. The women were randomized to treatment with estrogen versus placebo (E trial) or to estrogen plus progestin versus placebo (E + P trial). We used age-stratified Cox proportional hazard models to examine the association between time to incident, centrally adjudicated VTE, and daily mean PM concentrations spatially interpolated at geocoded addresses of the participants and averaged over 1, 7, 30, and 365 days.ResultsDuring the follow-up period (mean, 7.7 years), 508 participants (2.0%) had VTEs at a rate of 2.6 events per 1,000 person-years. Unadjusted and covariate-adjusted VTE risk was not associated with concentrations of PM 0.05) regardless of PM averaging period, either before or after combining data from both trials [e.g., combined trial-adjusted hazard ratios (95% confidence intervals) per 10 μg/m3 increase in annual mean PM2.5 and PM10, were 0.93 (0.54–1.60) and 1.05 (0.72–1.53), respectively]. Findings were insensitive to alternative exposure metrics, outcome definitions, time scales, analytic methods, and censoring dates.ConclusionsIn contrast to prior research, our findings provide little evidence of an association between short-term or long-term PM exposure and VTE, or clinically important modification by randomized exposure to exogenous estrogens among postmenopausal women

Neighborhood context and ethnicity differences in body mass index: A multilevel analysis using the NHANES III (1988-1994)

A growing body of literature has documented a link between neighborhood context and health outcomes. However, little is known about the relationship between neighborhood context and body mass index (BMI) or whether the association between neighborhood context and BMI differs by ethnicity. This paper investigates several neighborhood characteristics as potential explanatory factors for the variation of BMI across the United States; further, this paper explores to what extent segregation and the concentration of disadvantage across neighborhoods help explain ethnic disparities in BMI. Using data geo-coded at the census tract-level and linked with individual-level data from the Third National Health and Examination Survey in the United States (U.S.), we find significant variation in BMI across U.S. neighborhoods. In addition, neighborhood characteristics have a significant association with body mass and partially explain ethnic disparities in BMI, net of individual-level adjustments. These data also reveal evidence that ethnic enclaves are not in fact advantageous for the body mass index of Hispanics - a relationship counter to what has been documented for other health outcomes

Does Place Explain Racial Health Disparities? Quantifying the Contribution of Residential Context to the Black/White Health Gap in the United States

The persistence of the black health disadvantage has been a puzzling component of health in the United States in spite of general declines in rates of morbidity and mortality over the past century. Studies that have focused on well-established individual-level determinants of health such as socio-economic status and health behaviors have been unable to fully explain these disparities. Recent research has begun to focus on other factors such as racism, discrimination, and segregation. Variation in neighborhood context - socio-demographic composition, social aspects, and built environment - has been postulated as an additional explanation for racial disparities, but few attempts have been made to quantify its overall contribution to the black/white health gap. This analysis is an attempt to generate an estimate of place effects on explaining health disparities by utilizing data from the US National Health Interview Survey (NHIS) (1989-1994), combined with a methodology for identifying residents of the same blocks both within and across NHIS survey cross-sections. Our results indicate that controlling for a single point-in-time measure of residential context results in a roughly 15 to 76 percent reduction of the black/white disparities in self-rated health that were previously unaccounted for by individual-level controls. The contribution of residential context toward explaining the black /white self-rated health gap varies by both age and gender such that contextual explanations of disparities decline with age and appear to be smaller among females