Effect of time and diet change on the bacterial community structure throughout the gastrointestinal tract and in faeces of the northern brown bandicoot, Isoodon macrourus

A significant gap, in not only peramelid nutritional physiology but marsupial nutrition as a whole, is the lack of information relating to microorganisms of the gastrointestinal tract. This research is a preliminary investigation that will provide a baseline for comparisons among peramelids. The high degree of 16S rRNA gene clones identified in this research that are closely related to culturable bacteria suggests that additional research will enable a more complete description of the gastrointestinal bacteria of I. macrourus. Most identifiable clones belonged to Clostridium and Ruminococcus. This research has confirmed that the hindgut of I. macrourus, the caecum, proximal colon and distal colon, are the main sites for microbial activity.

Probabilistic approach of water residence time and connectivity using Markov Chains with application to tidal embayments

Markov Chain analysis was recently proposed to assess the time scales and preferential pathways into biological or physical networks by computing residence time, first passage time, rates of transfer between nodes and number of passages in a node. We propose to adapt an algorithm already published for simple systems to physical systems described with a high resolution hydrodynamic model. The method is applied to bays and estuaries on the Eastern Coast of Canada for their interest in shellfish aquaculture. Current velocities have been computed by using a 2 dimensional grid of elements and circulation patterns were summarized by averaging Eulerian flows between adjacent elements. Flows and volumes allow computing probabilities of transition between elements and to assess the average time needed by virtual particles to move from one element to another, the rate of transfer between two elements, and the average residence time of each system. We also combined transfer rates and times to assess the main pathways of virtual particles released in farmed areas and the potential influence of farmed areas on other areas. We suggest that Markov chain is complementary to other sets of ecological indicators proposed to analyse the interactions between farmed areas - e.g. depletion index, carrying capacity assessment. Markov Chain has several advantages with respect to the estimation of connectivity between pair of sites. It makes possible to estimate transfer rates and times at once in a very quick and efficient way, without the need to perform long term simulations of particle or tracer concentration.

Resistance to phosphine in Sitophilus oryzae in Australia: A national analysis of trends and frequencies over time and geographical spread

In Australia, along with many other parts of the world, fumigation with phosphine is a vital component in controlling stored grain insect pests. However, resistance is a factor that may limit the continued efficacy of this fumigant. While strong resistance to phosphine has been identified and characterised, very little information is available on the causes of its development and spread. Data obtained from a unique national resistance monitoring and management program were analysed, using Bayesian hurdle modelling, to determine which factors may be responsible. Fumigation in unsealed storages, combined with a high frequency of weak resistance, were found to be the main criteria that led to the development of strong resistance in Sitophilus oryzae. Independent development, rather than gene flow via migration, appears to be primarily responsible for the geographic incidence of strong resistance to phosphine in S. oryzae. This information can now be utilised to direct resources and education into those areas at high risk and to refine phosphine resistance management strategies.

Parents’ experiences during the transition from childhood to adolescence with Type 1 Diabetes: Parent-child relationships and support received during this time and clinical research portfolio

Background: Type 1 Diabetes (T1D) management often worsens as children become adolescents. This can be a difficult time for parents as they hand over responsibility of diabetes management to their adolescent. Objectives: To look at the experiences of parents with a child with T1D as they move to adolescence and take more responsibility for their diabetes management. To find out about parents’ experience of support during this transition. Subjects: Three parents of adolescents with T1D. Participants were recruited from the NHS Highland Paediatric Diabetes Service. Methods: Participants took part in a one-to-one semi-structured interview with a researcher. Interpretative Phenomenological Analysis was used to analyse the interviews and find common themes across the interviews. Results: Participants experienced worry throughout their child’s transition to adolescence. They found it difficult to let their child take responsibility for their diabetes but acknowledged that their involvement caused tensions with their adolescent. Participants’ experience was that there were a number of practical adjustments to be made with a diagnosis of T1D and educating the network around their child was important. The participants reported that the diagnosis of T1D had an impact on the whole family and not just the child with the diagnosis. The parents felt well supported medically but said that the amount of time before their first clinic appointment felt too long. All participants had concerns about their adolescent moving to the adult diabetic service. Conclusions: Participants experienced worry relating to aspects of their adolescents T1D that they could not control, but were aware of the tensions caused by trying to keep elements of control. Areas of future research were identified.

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.

Mining and Energy Boom, Dutch Disease and Informality in Colombia: a DSGE Approach

The paper develops a Dynamic Stochastic General Equilibrium (DSGE) model, which assesses the macroeconomic and labor market effects derived from simulating a positive shock to the stochastic component of the mining-energy sector productivity. Calibrating the model for the Colombian economy, this shock generates a whole increase in formal wages and a raise in tax revenues, expanding total consumption of the household members. These facts increase non-tradable goods prices relative to tradable goods prices, then real exchange rate decreases (appreciation) and occurs a displacement of productive resources from the tradable (manufacturing) sector to the non-tradable sector, followed by an increase in formal GDP and formal job gains. This situation makes the formal sector to absorb workers from the informal sector through the non-tradable formal subsector, which causes informal GDP to go down. As a consequence, in the net consumption falls for informal workers, which leads some members of the household not to offer their labor force in the informal sector but instead they prefer to keep unemployed. Therefore, the final result on the labor market is a decrease in the number of informal workers, of which a part are in the formal sector and the rest are unemployed.

Nutritional status and its impact on time and relocation in postoperative complications of abdominal patients undergoing surgery

Introduction: The nutritional state is the independent factor that most influences the post-operational results in elective surgeries. Objective: to evaluate the influence of the nutritional state on the hospitalization period and on the post-operative complications of patients submitted to abdominal surgery. Methods: prospective study with 99 surgical patients over 18 years of age, submitted to abdominal surgeries in the period from April to October of 2013, in the Instituto de Medicina Integral Professor Fernando Figueira (IMIP). All patients were submitted to anthropometric nutritional evaluations through the body mass Index (BMI), arm circumference (AC) and triceps skinfold thickness (TEST). The biochemical evaluation was carried out from the leukogram and serum albumin results. The identification of candidate patients to nutritional therapy (NT) was carried out through the nutritional risk (NR) evaluation by using the BMI, loss of weight and hypoalbuminemia. The information about post-operational complications, hospitalization period and clinical diagnosis was collected from the medical records. Program SPSS version 13.0 and significance level of 5% were used for the statistical analysis. Results: The malnutrition diagnosed by the AC showed significant positive association with the presence of post-operative complications (p=0.02) and with hospitalization period (p=0.02). The presence of NR was greater when evaluated by hypoalbuminemia (28.9%), however, only 4% of the sample carried out the NT in the pre-operational period. The hospitalization period was greater for patients with malignant neoplasia (p<0.01). Conclusion: The malnutrition diagnosis of patients submitted to abdominal surgeries is associated to greater risk of post-operational complications and longer hospitalization permanence.

The Araucaria dendrochronological network: growth patterns and climatic signals of paraná-pine on the Southern Brazilian plateau.

2016