Nutrient partitioning during treatment of tuberculosis: gain in body fat mass but not in protein mass

Background: Tuberculosis is an important cause of wasting. The functional consequences of wasting and recovery may depend on the distribution of lost and gained nutrient stores between protein and fat masses. Objective: The goal was to study nutrient partitioning, ie, the proportion of weight change attributable to changes in fat mass (FM) versus protein mass (PM), during anti mycobacterial treatment. Design: Body-composition measures were made of 21 men and 9 women with pulmonary tuberculosis at baseline and after 1 and 6 mo of treatment. All subjects underwent dual-energy X-ray absorptiometry and deuterium bromide dilution tests, and a four-compartment model of FM, total body water (TBW), bone minerals (BM), and PM was derived. The ratio of PM to FM at any time was expressed as the energy content (p-ratio). Changes in the p-ratio were related to disease severity as measured by radiologic criteria. Results: Patients gained 10% in body weight (P < 0.001) from baseline to month 6. This was mainly due to a 44% gain in FM (P < 0.001); PM, BM, and TBW did not change significantly. Results were similar in men and women. The p-ratio decreased from baseline to month 1 and then fell further by month 6. Radiologic disease severity was not correlated with changes in the p-ratio. Conclusions: Microbiological cure of tuberculosis does not restore PM within 6 mo, despite a strong anabolic response. Change in the p-ratio is a suitable parameter for use in studying the effect of disease on body composition because it allows transformation of such effects into a normal distribution across a wide range of baseline proportion between fat and protein mass.

Body composition in children in remission from acute lymphoblastic leukemia

Background: Changes in body composition are commonly reported in pediatric survivors of acute lymphoblastic leukemia (ALL). However, the effect of ALL and of its treatment on body composition in children in remission from ALL has not been fully examined with the use of a reference method. Objectives: We aimed to determine the body composition and composition of fat-free mass (FFM) in children in remission from ALL. We also aimed to compare the effects that prednisolone and dexamethasone had on the body composition of an ALL survivor population. Design: This cross-sectional study measured height, weight, body volume, total body water, and bone mineral content in 24 children in remission from ALL and 24 age-matched, healthy control subjects. Body composition and FFM composition were evaluated by using the 4-component model. Results: The mean body mass index and fat mass index were significantly (P = 0.05 for both) higher in the ALL survivors than in age-matched control subjects. The composition of the FFM in the 2 treatment groups was not observed to differ significantly. Examination of the composition of FFM made it evident that children in remission from ALL had both significantly greater hydration (P = 0.001) and lower density (P = 0.0001) of FFM than did the control children. Conclusions: Children in remission from ALL may develop excess body fat. To measure body composition accurately in an ALL population, the high hydration and low density of FFM in this population should be taken into consideration.

Regulatory foci and organisational commitment

We use regulatory focus theory to derive specific predictions regarding the differential relationships between regulatory focus and commitment. We estimated a structural equation model using a sample of 520 private and public sector employees and found in line with our hypotheses that (a) promotion focus related more strongly to affective commitment than prevention focus, (b) prevention focus related more strongly to continuance commitment than promotion focus, (c) promotion and prevention focus had equally strong effects on normative commitment. Implications of these findings for the three-component model of commitment, especially the ‘dual nature’ of normative commitment, as well as implications for human resources management and leadership are discussed.

Pryolytic and kinetic study of Chlorella Vulgaris under isothermal and non-isothermal conditions

Algae are a new potential biomass for energy production but there is limited information on their pyrolysis and kinetics. The main aim of this thesis is to investigate the pyrolytic behaviour and kinetics of Chlorella vulgaris, a green microalga. Under pyrolysis conditions, these microalgae show their comparable capabilities to terrestrial biomass for energy and chemicals production. Also, the evidence from a preliminary pyrolysis by the intermediate pilot-scale reactor supports the applicability of these microalgae in the existing pyrolysis reactor. Thermal decomposition of Chlorella vulgaris occurs in a wide range of temperature (200-550°C) with multi-step reactions. To evaluate the kinetic parameters of their pyrolysis process, two approaches which are isothermal and non-isothermal experiments are applied in this work. New developed Pyrolysis-Mass Spectrometry (Py-MS) technique has the potential for isothermal measurements with a short run time and small sample size requirement. The equipment and procedure are assessed by the kinetic evaluation of thermal decomposition of polyethylene and lignocellulosic derived materials (cellulose, hemicellulose, and lignin). In the case of non-isothermal experiment, Thermogravimetry- Mass Spectrometry (TG-MS) technique is used in this work. Evolved gas analysis provides the information on the evolution of volatiles and these data lead to a multi-component model. Triplet kinetic values (apparent activation energy, pre-exponential factor, and apparent reaction order) from isothermal experiment are 57 (kJ/mol), 5.32 (logA, min-1), 1.21-1.45; 9 (kJ/mol), 1.75 (logA, min-1), 1.45 and 40 (kJ/mol), 3.88 (logA, min-1), 1.45- 1.15 for low, middle and high temperature region, respectively. The kinetic parameters from non-isothermal experiment are varied depending on the different fractions in algal biomass when the range of apparent activation energies are 73-207 (kJ/mol); pre-exponential factor are 5-16 (logA, min-1); and apparent reaction orders are 1.32–2.00. The kinetic procedures reported in this thesis are able to be applied to other kinds of biomass and algae for future works.

Essays in environmental economic valuation and decision making in the presence of an environmental disaster

The first essay developed a respondent model of Bayesian updating for a double-bound dichotomous choice (DB-DC) contingent valuation methodology. I demonstrated by way of data simulations that current DB-DC identifications of true willingness-to-pay (WTP) may often fail given this respondent Bayesian updating context. Further simulations demonstrated that a simple extension of current DB-DC identifications derived explicitly from the Bayesian updating behavioral model can correct for much of the WTP bias. Additional results provided caution to viewing respondents as acting strategically toward the second bid. Finally, an empirical application confirmed the simulation outcomes. The second essay applied a hedonic property value model to a unique water quality (WQ) dataset for a year-round, urban, and coastal housing market in South Florida, and found evidence that various WQ measures affect waterfront housing prices in this setting. However, the results indicated that this relationship is not consistent across any of the six particular WQ variables used, and is furthermore dependent upon the specific descriptive statistic employed to represent the WQ measure in the empirical analysis. These results continue to underscore the need to better understand both the WQ measure and its statistical form homebuyers use in making their purchase decision. The third essay addressed a limitation to existing hurricane evacuation modeling aspects by developing a dynamic model of hurricane evacuation behavior. A household's evacuation decision was framed as an optimal stopping problem where every potential evacuation time period prior to the actual hurricane landfall, the household's optimal choice is to either evacuate, or to wait one more time period for a revised hurricane forecast. A hypothetical two-period model of evacuation and a realistic multi-period model of evacuation that incorporates actual forecast and evacuation cost data for my designated Gulf of Mexico region were developed for the dynamic analysis. Results from the multi-period model were calibrated with existing evacuation timing data from a number of hurricanes. Given the calibrated dynamic framework, a number of policy questions that plausibly affect the timing of household evacuations were analyzed, and a deeper understanding of existing empirical outcomes in regard to the timing of the evacuation decision was achieved.

Changes in mass and nutrient content of wood during decomposition in a south Florida mangrove forest

1. Large pools of dead wood in mangrove forests following disturbances such as hurricanes may influence nutrient fluxes. We hypothesized that decomposition of wood of mangroves from Florida, USA (Avicennia germinans, Laguncularia racemosa and Rhizophora mangle), and the consequent nutrient dynamics, would depend on species, location in the forest relative to freshwater and marine influences and whether the wood was standing, lying on the sediment surface or buried. 2. Wood disks (8–10 cm diameter, 1 cm thick) from each species were set to decompose at sites along the Shark River, either buried in the sediment, on the soil surface or in the air (above both the soil surface and high tide elevation). 3. A simple exponential model described the decay of wood in the air, and neither species nor site had any effect on the decay coefficient during the first 13 months of decomposition. 4. Over 28 months of decomposition, buried and surface disks decomposed following a two-component model, with labile and refractory components. Avicennia germinans had the largest labile component (18 ± 2% of dry weight), while Laguncularia racemosa had the lowest (10 ± 2%). Labile components decayed at rates of 0.37–23.71% month−1, while refractory components decayed at rates of 0.001–0.033% month−1. Disks decomposing on the soil surface had higher decay rates than buried disks, but both were higher than disks in the air. All species had similar decay rates of the labile and refractory components, but A. germinans exhibited faster overall decay because of a higher proportion of labile components. 5. Nitrogen content generally increased in buried and surface disks, but there was little change in N content of disks in the air over the 2-year study. Between 17% and 68% of total phosphorus in wood leached out during the first 2 months of decomposition, with buried disks having the greater losses, P remaining constant or increasing slightly thereafter. 6. Newly deposited wood from living trees was a short-term source of N for the ecosystem but, by the end of 2 years, had become a net sink. Wood, however, remained a source of P for the ecosystem. 7. As in other forested ecosystems, coarse woody debris can have a significant impact on carbon and nutrient dynamics in mangrove forests. The prevalence of disturbances, such as hurricanes, that can deposit large amounts of wood on the forest floor accentuates the importance of downed wood in these forests.

Modeling Light Use Efficiency in a Subtropical Mangrove Forest Equipped with CO2 Eddy Covariance °C

Despite the importance of mangrove ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these forests remain poorly understood. This limited understanding is partly a result of the challenges associated with in situ flux studies. Tower-based CO2 eddy covariance (EC) systems are installed in only a few mangrove forests worldwide, and the longest EC record from the Florida Everglades contains less than 9 years of observations. A primary goal of the present study was to develop a methodology to estimate canopy-scale photosynthetic light use efficiency in this forest. These tower-based observations represent a basis for associating CO2 fluxes with canopy light use properties, and thus provide the means for utilizing satellite-based reflectance data for larger scale investigations. We present a model for mangrove canopy light use efficiency utilizing the enhanced green vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that is capable of predicting changes in mangrove forest CO2 fluxes caused by a hurricane disturbance and changes in regional environmental conditions, including temperature and salinity. Model parameters are solved for in a Bayesian framework. The model structure requires estimates of ecosystem respiration (RE), and we present the first ever tower-based estimates of mangrove forest RE derived from nighttime CO2 fluxes. Our investigation is also the first to show the effects of salinity on mangrove forest CO2 uptake, which declines 5% per each 10 parts per thousand (ppt) increase in salinity. Light use efficiency in this forest declines with increasing daily photosynthetic active radiation, which is an important departure from the assumption of constant light use efficiency typically applied in satellite-driven models. The model developed here provides a framework for estimating CO2 uptake by these forests from reflectance data and information about environmental conditions.

Essays in Environmental Economic Valuation and Decision Making in the Presence of an Environmental Disaster

The first essay developed a respondent model of Bayesian updating for a double-bound dichotomous choice (DB-DC) contingent valuation methodology. I demonstrated by way of data simulations that current DB-DC identifications of true willingness-to-pay (WTP) may often fail given this respondent Bayesian updating context. Further simulations demonstrated that a simple extension of current DB-DC identifications derived explicitly from the Bayesian updating behavioral model can correct for much of the WTP bias. Additional results provided caution to viewing respondents as acting strategically toward the second bid. Finally, an empirical application confirmed the simulation outcomes. The second essay applied a hedonic property value model to a unique water quality (WQ) dataset for a year-round, urban, and coastal housing market in South Florida, and found evidence that various WQ measures affect waterfront housing prices in this setting. However, the results indicated that this relationship is not consistent across any of the six particular WQ variables used, and is furthermore dependent upon the specific descriptive statistic employed to represent the WQ measure in the empirical analysis. These results continue to underscore the need to better understand both the WQ measure and its statistical form homebuyers use in making their purchase decision. The third essay addressed a limitation to existing hurricane evacuation modeling aspects by developing a dynamic model of hurricane evacuation behavior. A household’s evacuation decision was framed as an optimal stopping problem where every potential evacuation time period prior to the actual hurricane landfall, the household’s optimal choice is to either evacuate, or to wait one more time period for a revised hurricane forecast. A hypothetical two-period model of evacuation and a realistic multi-period model of evacuation that incorporates actual forecast and evacuation cost data for my designated Gulf of Mexico region were developed for the dynamic analysis. Results from the multi-period model were calibrated with existing evacuation timing data from a number of hurricanes. Given the calibrated dynamic framework, a number of policy questions that plausibly affect the timing of household evacuations were analyzed, and a deeper understanding of existing empirical outcomes in regard to the timing of the evacuation decision was achieved.

Gaussian Process Kernels for Cross-Spectrum Analysis in Electrophysiological Time Series

Multi-output Gaussian processes provide a convenient framework for multi-task problems. An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels. Recently, the spectral mixture (SM) kernel was proposed to model the spectral density of a single task in a Gaussian process framework. This work develops a novel covariance kernel for multiple outputs, called the cross-spectral mixture (CSM) kernel. This new, flexible kernel represents both the power and phase relationship between multiple observation channels. The expressive capabilities of the CSM kernel are demonstrated through implementation of 1) a Bayesian hidden Markov model, where the emission distribution is a multi-output Gaussian process with a CSM covariance kernel, and 2) a Gaussian process factor analysis model, where factor scores represent the utilization of cross-spectral neural circuits. Results are presented for measured multi-region electrophysiological data.

Effect of Termination of Long-term Free Air CO2 Enrichment on Physiology and Carbon Allocation in a Loblolly Pine Dominated Forest

This dissertation examined the response to termination of CO2 enrichment of a forest ecosystem exposed to long-term elevated atmospheric CO2 condition, and aimed at investigating responses and their underlying mechanisms of two important factors of carbon cycle in the ecosystem, stomatal conductance and soil respiration. Because the contribution of understory vegetation to the entire ecosystem grew with time, we first investigated the effect of elevated CO2 on understory vegetation. Potential growth enhancing effect of elevated CO2 were not observed, and light seemed to be a limiting factor. Secondly, we examined the importance of aerodynamic conductance to determine canopy conductance, and found that its effect can be negligible. Responses of stomatal conductance and soil respiration were assessed using Bayesian state space model. In two years after the termination of CO2 enrichment, stomatal conductance in formerly elevated CO2 returned to ambient level, while soil respiration became smaller than ambient level and did not recovered to ambient in two years.

Major and trace elements and Nd and Sr isotope geochemistry of basalts at DSDP Leg 74 Holes

Basement intersected in Holes 525A, 528, and 527 on the Walvis Ridge consists of submarine basalt flows and pillows with minor intercalated sediments. These holes are situated on the crest and mid- and lower NW flank of a NNW-SSE-trending ridge block which would have closely paralleled the paleo mid-ocean ridge. The basalts were erupted approximately 70 Ma, a date consistent with formation at the paleo mid-ocean ridge. The basalt types vary from aphyric quartz tholeiites on the Ridge crest to highly Plagioclase phyric olivine tholeiites on the flank. These show systematic differences in incompatible trace element and isotopic composition, and many element and isotope ratio pairs form systematic trends with the Ridge crest basalts at one end and the highly phyric Ridge flank basalts at the other. The low 143Nd/144Nd (0.51238) and high 87Sr/86Sr (0.70512) ratios of the Ridge crest basalts suggest derivation from an old Nd/Sm and Rb/Sr enriched mantle source. This isotopic signature is similar to that of alkaline basalts on Tristan da Cunha but offset by somewhat lower 143Nd/144Nd values. The isotopic ratio trends may be extrapolated beyond the Ridge flank basalts (which have 143Nd/144Nd of 0.51270 and 87Sr/86Sr of 0.70417) in the direction of typical MORB compositions. These isotopic correlations are equally consistent with mixing of depleted and enriched end-member melts or partial melting of an inhomogeneous, variably enriched mantle source. However, observed Zr-Ba-Nb-Y interelement relationships are inconsistent with any simple two-component model of magma mixing or partial melting. They also preclude extensive involvement of depleted (N-type) MORB material or its mantle sources in the petrogenesis of Walvis Ridge basalts.

Situation Background Assessment Recommendation (SBAR): Undergraduate Perspectives

Situation Background Assessment and Recommendation (SBAR): Undergraduate Perspectives C Morgan, L Adams, J Murray, R Dunlop, IK Walsh. Ian K Walsh, Centre for Medical Education, Queen’s University Belfast, Mulhouse Building, Royal Victoria Hospital, Grosvenor Road, Belfast BT12 6DP Background and Purpose: Structured communication tools are used to improve team communication quality.1,2 The Situation Background Assessment and Recommendation (SBAR) tool is widely adopted within patient safety.3 SBAR effectiveness is reportedly equivocal, suggesting use is not sustained beyond initial training.4-6 Understanding perspectives of those using SBAR may further improve clinical communication. We investigated senior medical undergraduate perspectives on SBAR, particularly when communicating with senior colleagues. Methodology: Mixed methods data collection was used. A previously piloted questionnaire with 12 five point Lickert scale questions and 3 open questions was given to all final year medical students. A subgroup also participated in 10 focus groups, deploying strictly structured audio-recorded questions. Selection was by convenience sampling, data gathered by open text questions and comments transcribed verbatim. In-vivo coding (iterative, towards data saturation) preceded thematic analysis. Results: 233 of 255 students (91%) completed the survey. 1. There were clearly contradictory viewpoints on SBAR usage. A recurrent theme was a desire for formal feedback and a relative lack of practice/experience with SBAR. 2. Students reported SBAR as having variable interpretation between individuals; limiting use as a shared mental model. 3. Brief training sessions are insufficient to embed the tool. 4. Most students reported SBAR helping effective communication, especially by providing structure in stressful situations. 5. Only 18.5% of students felt an alternative resource might be needed. Sub analysis of the themes highlighted: A. Lack of clarity regarding what information to include and information placement within the acronym, B. Senior colleague negative response to SBAR C. Lack of conciseness with the tool. Discussion and Conclusions: Despite a wide range of contradictory interpretation of SBAR utility, most students wish to retain the resource. More practice opportunities/feedback may enhance user confidence and understanding. References: (1) Leonard M, Graham S, Bonacum D. The human factor: the critical importance of effective teamwork and communication in providing safe care. Quality & Safety in Health Care 2004 Oct;13(Suppl 1):85-90. (2) d'Agincourt-Canning LG, Kissoon N, Singal M, Pitfield AF. Culture, communication and safety: lessons from the airline industry. Indian J Pediatr 2011 Jun;78(6):703-708. (3) Dunsford J. Structured communication: improving patient safety with SBAR. Nurs Womens Health 2009 Oct;13(5):384-390. (4) Compton J, Copeland K, Flanders S, Cassity C, Spetman M, Xiao Y, et al. Implementing SBAR across a large multihospital health system. Jt Comm J Qual Patient Saf 2012 Jun;38(6):261-268. (5) Ludikhuize J, de Jonge E, Goossens A. Measuring adherence among nurses one year after training in applying the Modified Early Warning Score and Situation-Background-Assessment-Recommendation instruments. Resuscitation 2011 Nov;82(11):1428-1433. (6) Cunningham NJ, Weiland TJ, van Dijk J, Paddle P, Shilkofski N, Cunningham NY. Telephone referrals by junior doctors: a randomised controlled trial assessing the impact of SBAR in a simulated setting. Postgrad Med J 2012 Nov;88(1045):619-626.

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.