Chronic helminth infection burden differentially affects haematopoietic cell development while ageing selectively impairs adaptive responses to infection

Throughout the lifespan of an individual, the immune system undergoes complex changes while facing novel and chronic infections. Helminths, which infect over one billion people and impose heavy livestock productivity losses, typically cause chronic infections by avoiding and suppressing host immunity. Yet, how age affects immune responses to lifelong parasitic infection is poorly understood. To disentangle the processes involved, we employed supervised statistical learning techniques to identify which factors among haematopoietic stem and progenitor cells (HSPC), and both innate and adaptive responses regulate parasite burdens and how they are affected by host age. Older mice harboured greater numbers of the parasites’ offspring than younger mice. Protective immune responses that did not vary with age were dominated by HSPC, while ageing specifically eroded adaptive immunity, with reduced numbers of naïve T cells, poor T cell responsiveness to parasites, and impaired antibody production. We identified immune factors consistent with previously-reported immune responses to helminths, and also revealed novel interactions between helminths and HSPC maturation. Our approach thus allowed disentangling the concurrent effects of ageing and infection across the full maturation cycle of the immune response and highlights the potential of such approaches to improve understanding of the immune system within the whole organism

Supplier Selection and Relationship Management: An Application of Machine Learning Techniques

Managing supply chains is an extremely challenging task due to globalization, short product life cycle, and recent advancements in information technology. These changes result in the increasing importance of managing the relationship with suppliers. However, the supplier selection literature mainly focuses on selecting suppliers based on previous performance, environmental and social criteria and ignores supplier relationship management. Moreover, although the explosion of data and the capabilities of machine learning techniques in handling dynamic and fast changing environment show promising results in customer relationship management, especially in customer lifetime value, this area has been untouched in the upstream side of supply chains. This research is an attempt to address this gap by proposing a framework to predict supplier future value, by incorporating the contract history data, relationship value, and supply network properties. The proposed model is empirically tested for suppliers of public works and government services Canada. Methodology wise, this thesis demonstrates the application of machine learning techniques for supplier selection and developing effective strategies for managing relationships. Practically, the proposed framework equips supply chain managers with a proactive and forward-looking approach for managing supplier relationship

Large-scale survey to estimate the prevalence of disorders for 192 Kennel Club registered breeds

Abstract Background Pedigree or purebred dogs are often stated to have high prevalence of disorders which are commonly assumed to be a consequence of inbreeding and selection for exaggerated features. However, few studies empirically report and rank the prevalence of disorders across breeds although such data are of critical importance in the prioritisation of multiple health concerns, and to provide a baseline against which to explore changes over time. This paper reports an owner survey that gathered disorder information on Kennel Club registered pedigree dogs, regardless of whether these disorders received veterinary care. This study aimed to determine the prevalence of disorders among pedigree dogs overall and, where possible, determine any variation among breeds. Results This study included morbidity data on 43,005 live dogs registered with the Kennel Club. Just under two thirds of live dogs had no reported diseases/conditions. The most prevalent diseases/conditions overall were lipoma (4.3%; 95% confidence interval 4.13-4.52%), skin (cutaneous) cyst (3.1%; 2.94-3.27%) and hypersensitivity (allergic) skin disorder (2.7%; 2.52-2.82%). For the most common disorders in the most represented breeds, 90 significant differences between the within breed prevalence and the overall prevalence are reported. Conclusion The results from this study have added vital epidemiological data on disorders in UK dogs. It is anticipated that these results will contribute to the forthcoming Breed Health & Conservation Plans, a Kennel Club initiative aiming to assist in the identification and prioritisation of breeding selection objectives for health and provide advice to breeders/owners regarding steps that may be taken to minimise the risk of the disease/disorders. Future breed-specific studies are recommended to report more precise prevalence estimates within more breeds

Multi-body dynamic and finite element modeling of ultra-large dump truck - haul road interactions for machine health and haul road structural integrity

Haul truck capacities have increased due to their economies of scale in large-scale surface mine production systems. Ultra-large trucks impose high dynamic loads on haul roads. The dynamic loads are exacerbated by road surface roughness and truck over-loading. The dynamic forces also subject trucks to high torsional stresses, which affect truck health. Current haul road response models are 2D and use static truckloads for low capacity trucks. Existing 3D models consider the road as a two-layer system. No models capture the truck dynamic effects on haul roads and predict strut pressures during haulage. Lagrangian mechanics was used to formulate the governing equations of the truck-haul road system. The equations were solved in MSC.ADAMS, based on multi-body dynamics, to generate the truck dynamic forces, which were verified and validated using data obtained from an open-pit mine. These forces were used in an FE model developed, verified and validated in ABAQUS to model the response of the haul road to the truck dynamic forces. The road was modeled using an elastoplastic Mohr-Coulomb model. The results showed that the maximum truck tire dynamic forces were 2.86 and 3.02 times the static force at rated payload and 20% over-loading, respectively. The trucks were exposed to torsional stresses that were up to 2.9 times the recommended threshold. Road deformation decreased with increasing layer modulus and increased with increasing payload. This study proposed novel multivariate models for predicting dynamic truck strut pressures. The novel 3D FE model and empirical relations for calculating truck dynamic forces incorporate truck dynamic forces into haul road design. This study forms a basis for designing structurally competent haul roads and improving truck health --Abstract, page iii

Multivariate Protein Signatures of Pre-Clinical Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Plasma Proteome Dataset

Background: Recent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD. Methods and Findings: We focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3×10−13). We applied a multivariate approach based on combinatorial optimization ((α,β)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering “meta-features,” representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%. Conclusions: Applying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages

Characterising Spanish retirees enrolled in university programs: four differentiated profiles

Due to age discrimination, retirees are usually viewed as a homogeneous group and therefore are all treated in the same way by society. In-depth research into the differences between the individuals in this age group gives us the opportunity to discover if all retirees deal with this change in the same manner. The objective of our study is to identify different profiles in a sample of Spanish retirees enrolled in university programs according to different factors. 991 Spanish retirees were evaluated both in terms of continuous variables, such as organizational pressures, health problems, interest in retirement, levels of work-related stress, social support and resilience, and demographic variables. A cluster analysis was performed which enables the definition of groups with homogeneous characteristics within large samples. The variables were included in each of the clusters using Student’s t-test for the continuous variables and a chi squared test for the categorical variables. The results of the analysis confirm the existence of four groups or clusters based on the variables used in the research. The differentiation of profiles for retirees helps to eliminate social discrimination motived by age and mistaken beliefs about retirees (ageism) and assists in the adjustment and personalization of retirement preparation programs. Keywords: retirement; resilience; social support; satisfaction; cluster analysis

Seeds, soils and moisture : ecophysiology to inform mine site restoration in arid zones

Mining in arid regions of Australia is followed by restoration and rehabilitation efforts. However, attempts to reintroduce many woody plant species have been unsuccessful. Water is the key limiting resource to plant growth and seed germination in arid zones. In this thesis, I investigated ecophysiological processes that may help improve recruitment across novel arid landscapes. I tested seed traits and dormancy cues of eight keystone plants and found that six of them had non-dormant, rapidly germinating seeds; a trait we propose is an adaptation to the region’s unpredictable rainfall. To identify the germination niche of species, I incubated seeds under different temperatures and water potentials, and found drought avoidance to be an important survival strategy for arid species. I collected soil samples to compare biophysicochemical properties of reconstructed soils to remnant ecosystems, and found that the distribution of clay content in the reconstructed soils did not mirror the remnant soils, compromising their ability to sustain perennial vegetation. I also monitored soil moisture and found that soil reconstruction reduces rainfall infiltration and retention, and subsequently increases evaporation. The synthesis of these results demonstrate some of the limitations to successful restoration in these systems, such as (1) unknown dormancy cues and poor seed longevity, (2) infrequent and episodic plant recruitment due to water limitation, and (3) reduced hydrological function of reconstructed soils. The failure to reinstate hydrological function is the major constraint to ecological restoration in this arid zone. Nonetheless, results from this study suggest that restoration is possible through more strategic use of seed, careful selection of drought tolerant species, and increasing soil moisture. Further failures to reinstate ecosystem function and community dynamics in arid zones with reconstructed soils can be prevented by understanding the edaphic constraints to plant establishment, and ameliorating conditions to mimic ecohydrological processes in remnant ecosystems.Doctor of Philosoph