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Quantifying pathogen transmission in multi-host systems is difficult, as exemplified in bovine tuberculosis (bTB) systems, but is crucial for control. The agent of bTB, , persists in cattle populations worldwide, often where potential wildlife reservoirs exist. However, the relative contribution of different host species to bTB persistence is generally unknown. In Britain, the role of badgers in infection persistence in cattle is highly contentious, despite decades of research and control efforts. We applied Bayesian phylogenetic and machine-learning approaches to bacterial genome data to quantify the roles of badgers and cattle in infection dynamics in the presence of data biases. Our results suggest that transmission occurs more frequently from badgers to cattle than (10.4x in the most likely model) and that within-species transmission occurs at higher rates than between-species transmission for both. If representative, our results suggest that control operations should target both cattle and badgers.

Established methods for whole-genome-sequencing (WGS) technology allow for the detection of single-nucleotide polymorphisms (SNPs) in the pathogen genomes sourced from host samples. The information obtained can be used to track the pathogen’s evolution in time and potentially identify ‘who-infected-whom’ with unprecedented accuracy. Successful methods include ‘phylodynamic approaches’ that integrate evolutionary and epidemiological data. However, they are typically computationally intensive, require extensive data, and are best applied when there is a strong molecular clock signal and substantial pathogen diversity. To determine how much transmission information can be inferred when pathogen genetic diversity is low and metadata limited, we propose an analytical approach that combines pathogen WGS data and sampling times from infected hosts. It accounts for ‘between-scale’ processes, in particular within-host pathogen evolution and between-host transmission. We applied this to a well-characterised population with an endemic Mycobacterium bovis (the causative agent of bovine/zoonotic tuberculosis, bTB) infection. Our results show that, even with such limited data and low diversity, the computation of the transmission probability between host pairs can help discriminate between likely and unlikely infection pathways and therefore help to identify potential transmission networks. However, the method can be sensitive to assumptions about within-host evolution.

Multi-host pathogens are particularly difficult to control, especially when at least one of the hosts acts as a hidden reservoir. Deep sequencing of densely sampled pathogens has the potential to transform this understanding, but requires analytical approaches that jointly consider epidemiological and genetic data to best address this problem. While there has been considerable success in analyses of single species systems, the hidden reservoir problem is relatively under-studied. A well-known exemplar of this problem is bovine Tuberculosis, a disease found in British and Irish cattle caused by Mycobacterium bovis , where the Eurasian badger has long been believed to act as a reservoir but remains of poorly quantified importance except in very specific locations. As a result, the effort that should be directed at controlling disease in badgers is unclear. Here, we analyse densely collected epidemiological and genetic data from a cattle population but do not explicitly consider any data from badgers. We use a simulation modelling approach to show that, in our system, a model that exploits available cattle demographic and herd-to-herd movement data, but only considers the ability of a hidden reservoir to generate pathogen diversity, can be used to choose between different epidemiological scenarios. In our analysis, a model where the reservoir does not generate any diversity but contributes to new infections at a local farm scale are significantly preferred over models which generate diversity and/or spread disease at broader spatial scales. While we cannot directly attribute the role of the reservoir to badgers based on this analysis alone, the result supports the hypothesis that under current cattle control regimes, infected cattle alone cannot sustain M. bovis circulation. Given the observed close phylogenetic relationship for the bacteria taken from cattle and badgers sampled near to each other, the most parsimonious hypothesis is that the reservoir is the infected badger population. More broadly, our approach demonstrates that carefully constructed bespoke models can exploit the combination of genetic and epidemiological data to overcome issues of extreme data bias, and uncover important general characteristics of transmission in multi-host pathogen systems.

Quantifying pathogen transmission in multi-host systems is difficult, as exemplified in bovine tuberculosis (bTB) systems, but is crucial for control. The agent of bTB, Mycobacterium bovis, persists in cattle populations worldwide, often where potential wildlife reservoirs exist. However, the relative contribution of different host species to bTB persistence is generally unknown. In Britain, the role of badgers in infection persistence in cattle is highly contentious, despite decades of research and control efforts. We applied Bayesian phylogenetic and machine-learning approaches to bacterial genome data to quantify the roles of badgers and cattle in M. bovis infection dynamics in the presence of data biases. Our results suggest that transmission occurs more frequently from badgers to cattle than vice versa (10.4x in the most likely model) and that within-species transmission occurs at higher rates than between-species transmission for both. If representative, our results suggest that control operations should target both cattle and badgers. Disease-causing microbes that infect more than one type of animal can be difficult to control. This is especially true when they infect wildlife. For example, Mycobacterium bovis is a bacterium that causes tuberculosis in tens of thousands of cattle in Britain every year and also infects badgers and other wildlife. Controlling the infections in cattle is essential, as it helps prevent the bacteria from infecting humans, improves cattle welfare and reduces the substantial costs to the livestock industry. Analysing the relatedness of M. bovis genomes from infected cattle and badgers may help scientists work out how often badgers infect cattle and vice versa. Scientists have collected data and M. bovis samples from infected badgers in Woodchester Park, in England, for over three decades. Using these data and additional information about M. bovis infecting nearby cattle may help scientists learn how the bacteria spreads and how to stop it. Now, Crispell et al. show that complex patterns of contact between cattle and badgers likely drive the persistence of tuberculosis in cattle, also known as bovine tuberculosis. In three separate analyses, Crispell et al. compared the genomes of M. bovis found in cattle and badgers, the animals' locations, when they were infected, and whether they could have been in contact. The analyses found that M. bovis was likely to have been transmitted more frequently from badgers to cattle rather than from cattle to badgers. They also showed that transmission within each species happened more often than transmission between species. If these results are confirmed by other studies, they may help scientists develop better strategies for controlling tuberculosis in British cattle. In particular, controversial control strategies – such as badger culls – could be more targeted to better combat tuberculosis in cattle but have less of an impact on badgers. These insights might also aid control efforts in other countries where bovine tuberculosis is a problem and an important source of human tuberculosis.

A spectrum of phenomena related to the reliability of AlGaN/GaN HEMTs are investigated in this thesis using numerical simulations. The focus is on trap related phenomena that lead to decrease in the power output and failure of devices, i.e. the current collapse and the device degradation. The current collapse phenomenon has been largely suppressed using SiN passivation, but there are gaps in the understanding of the process leading to this effect. Device degradation, on the other side, is a pending problem of current devices and an obstacle to wide penetration of the market. Calibration of I-V measurements of two devices is performed with high accuracy to provide a trustworthy starting point for modelling the phenomena of interest. Traditionally, in simulations of nitride based HEMTs, only direct piezoelectric effect is taken into account and the resulting interface charge is thence independent of the electric field. In this work, the impact of the electric field via the converse piezoelectric effect is taken into account and its impact on the bound charge and the drain current is studied, as a refinement of the simulation methodology. It is widely believed that the current collapse is caused by a virtual gate, i.e. electrons leaked to the surface of the device. We have found a charge distribution that reproduced the I-V measurement that shows current collapse, hence validating the concept of the virtual gate. While it was previously shown that the virtual gate has a similar impact on the I-V curve as is observed during the current collapse, we believe that this is for the first time that a wide range of gate and drain voltages was calibrated. High gate/drain voltage leading to permanent degradation was also investigated. The hypothesis that stress induced defects and dislocations might be responsible for the degradation was tested but not fully confirmed. Finally, the leakage of electrons thought to be responsible for formation of the virtual gate and the current collapse due to the Poole-Frenkel emission, is simulated in order to explain the surface charge distribution responsible for the current collapse and deduced in Chapter 5.

Multi-host pathogens are particularly difficult to control, especially when at least one of the hosts acts as a hidden reservoir. Deep sequencing of densely sampled pathogens has the potential to transform this understanding, but requires analytical approaches that jointly consider epidemiological and genetic data to best address this problem. While there has been considerable success in analyses of single species systems, the hidden reservoir problem is relatively under-studied. A well-known exemplar of this problem is bovine Tuberculosis, a disease found in British and Irish cattle caused by Mycobacterium bovis, where the Eurasian badger has long been believed to act as a reservoir but remains of poorly quantified importance except in very specific locations. As a result, the effort that should be directed at controlling disease in badgers is unclear. Here, we analyse densely collected epidemiological and genetic data from a cattle population but do not explicitly consider any data from badgers. We use a simulation modelling approach to show that, in our system, a model that exploits available cattle demographic and herd-to-herd movement data, but only considers the ability of a hidden reservoir to generate pathogen diversity, can be used to choose between different epidemiological scenarios. In our analysis, a model where the reservoir does not generate any diversity but contributes to new infections at a local farm scale are significantly preferred over models which generate diversity and/or spread disease at broader spatial scales. While we cannot directly attribute the role of the reservoir to badgers based on this analysis alone, the result supports the hypothesis that under current cattle control regimes, infected cattle alone cannot sustain M. bovis circulation. Given the observed close phylogenetic relationship for the bacteria taken from cattle and badgers sampled near to each other, the most parsimonious hypothesis is that the reservoir is the infected badger population. More broadly, our approach demonstrates that carefully constructed bespoke models can exploit the combination of genetic and epidemiological data to overcome issues of extreme data bias, and uncover important general characteristics of transmission in multi-host pathogen systems. For single host pathogens, pathogen genetic data have been transformative for understanding the transmission and control of many diseases, particuarly rapidly evolving RNA viruses. However garnering similar insights where pathogens are multi-host is more challenging, particularly when the evolution of the pathogen is slower and pathogen sampling often heavily biased. This is the case for Mycobacterium bovis, the causative agent of bovine Tuberculosis (bTB) and for which the Eurasian badger plays an as yet poorly understood role in transmission and spread. Here we have developed a computational model that incorporates M. bovis genetic data from cattle only with a highly abstracted model of an unobserved reservoir. Our research shows that a model in which the reservoir does not contribute to pathogen diversity, but is a source of infection in spatially localised areas around each farm, better describes the patterns of outbreaks observed in a population-level sample of a single M. bovis genotype in Northern Ireland over a period of 15 years, compared to models in which either the reservoir has no role, disease spread is spatially extensive, or where they generate considerable diversity on their own. While this reservoir model is not explicitly a model of badgers, its characteristics are consistent with other data that would suggest a reservoir consisting of infected badgers that contribute substantially to cattle infection, but could not maintain disease on their own.

Established methods for whole-genome-sequencing (WGS) technology allow for the detection of single-nucleotide polymorphisms (SNPs) in the pathogen genomes sourced from host samples. The information obtained can be used to track the pathogen’s evolution in time and potentially identify ‘who-infected-whom’ with unprecedented accuracy. Successful methods include ‘phylodynamic approaches’ that integrate evolutionary and epidemiological data. However, they are typically computationally intensive, require extensive data, and are best applied when there is a strong molecular clock signal and substantial pathogen diversity. To determine how much transmission information can be inferred when pathogen genetic diversity is low and metadata limited, we propose an analytical approach that combines pathogen WGS data and sampling times from infected hosts. It accounts for ‘between-scale’ processes, in particular within-host pathogen evolution and between-host transmission. We applied this to a well-characterised population with an endemic Mycobacterium bovis (the causative agent of bovine/zoonotic tuberculosis, bTB) infection. Our results show that, even with such limited data and low diversity, the computation of the transmission probability between host pairs can help discriminate between likely and unlikely infection pathways and therefore help to identify potential transmission networks, but can be sensitive to assumptions about within-host evolution.

PurposeThe purpose of this research is to identify and test selected economic and organisational factors influencing the sustainability of participatory budgeting (PB) in Slovakia.Design/methodology/approachThe data were collected from websites and publicly available documents from all local governments with urban municipality status in Slovakia, for a total of 155 municipalities, 59 of which have established a participatory budget. The main method applied was correlation analysis.FindingsThe findings indicate: (1) a weak inverse correlation between debt level and the continuous existence of PB in municipalities; (2) a moderate direct correlation between the duration of PB in a municipality and integration of PB in its programme budgeting; (3) a moderate direct correlation between the duration of PB in a municipality and the political experience of the mayor.Research limitations/implicationsThe findings are derived from a quantitative analysis of cases localised in a single country, and therefore interpretation requires an awareness of the context of local democracy in Slovakia. In this regard, any generalisation beyond the context under study needs to be considered with caution.Originality/valueThis research fills a gap in the role of PB in the context of sustainability, especially in the context of a post-communist country like Slovakia. It applies a quantitative approach to the analysis of PB, which is relatively rare in this field of study (qualitative case studies are predominant in PB research).

We report, for the first time, the effect of lanthanum zirconate (La 2 Zr 2 O 7 ) particles on the microstructure and mechanical behavior of an experimental molybdenum oxide dispersion-strengthened alloy. The focus was on the preparation of the novel Mo–La 2 Zr 2 O 7 composite using high-energy ball milling and spark plasma sintering and on the comparison of its microstructural and mechanical properties with pure Mo and Mo–La 2 O 3 ODS alloy counterparts. Mechanical properties were assessed using a Vickers hardness test at room temperature and a three-point flexural test in the temperature range from − 150 to 150 °C. The microstructure of the studied materials and their fracture behavior were evaluated using x-ray diffraction, energy-dispersive x-ray spectroscopy, and scanning electron and transmission electron microscopy. The strengthening effect of La 2 Zr 2 O 7  particles was found to be lower than that of La 2 O 3 particles, resulting in a 30-35% lower yield stress and flexural strength of the Mo–La 2 Zr 2 O 7 alloy compared to the Mo–La 2 O 3 alloy. The experimental Mo–La 2 Zr 2 O 7 alloy exhibited low plasticity and no distinct ductile-to-brittle transition temperature (DBTT) in the tested temperature range, unlike pure Mo and the Mo–La 2 O 3  alloy, which had the DBTT of 63 and 1 °C, respectively. Fracture occurred mainly in a brittle intergranular manner in the entire testing temperature range, while the counterpart materials showed localized plastic stretching at grain boundaries and within grains at and above the transition region. The observed behavior was primarily related to lower strengthening and brittleness as well as less effective grain boundary purification.

Beta hypersynchrony was recently introduced into clinical practice in Parkinson’s disease (PD) to identify the best stimulation contacts and for adaptive deep brain stimulation (aDBS) sensing. However, many other oscillopathies accompany the disease, and beta power sensing may not be optimal for all patients. The aim of this work was to study the potential clinical usefulness of beta power phase-amplitude coupling (PAC) with high frequency oscillations (HFOs). Subthalamic nucleus (STN) local field potentials (LFPs) from externalized DBS electrodes were recorded and analyzed in PD patients ( n  = 19). Beta power and HFOs were evaluated in a resting-state condition; PAC was then studied and compared with the electrode contact positions, structural connectivity, and medication state. Beta-HFO PAC (mainly in the 200–500 Hz range) was observed in all subjects. PAC was detectable more specifically in the motor part of the STN compared to beta power and HFOs. Moreover, the presence of PAC better corresponds to the stimulation setup based on the clinical effect. PAC is also sensitive to the laterality of symptoms and dopaminergic therapy, where the greater PAC cluster reflects the more affected side and medication “off” state. Coupling between beta power and HFOs is known to be a correlate of the PD “off” state. Beta-HFO PAC seems to be more sensitive than beta power itself and could be more helpful in the selection of the best clinical stimulation contact and probably also as a potential future input signal for aDBS.